ICML 2020 Schedule of Posters
All accepted papers for ICML 2020 are listed below, sorted into broad categories. Click on a category to jump directly to those papers.
Each paper is listed with a link to its PDF, BibTeX, and Video (only for registered participants of ICML 2020). Each video is available before the conference, and authors are available at two different time slots for online discussion (only for registered participants). The time slots are listed below the paper, in AOE time. If you click on the time, it will pop-up a converter.
- Accountability, Transparency and Interpretability
- Adversarial Examples
- Applications - Computer Vision
- Applications - Language, Speech and Dialog
- Applications - Neuroscience, Cognitive Science, Biology and Health
- Applications - Other
- Causality
- Deep Learning - Algorithms
- Deep Learning - General
- Deep Learning - Generative Models and Autoencoders
- Deep Learning - Theory
- Fairness, Equity, Justice, and Safety
- Gaussian Processes
- General Machine Learning Techniques
- Learning Theory
- Online Learning, Active Learning, and Bandits
- Optimization - Convex
- Optimization - General
- Optimization - Large Scale, Parallel and Distributed
- Optimization - Non-convex
- Planning, Control, and Multiagent Learning
- Privacy-preserving Statistics and Machine Learning
- Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods
- Probabilistic Inference - Models and Probabilistic Programming
- Reinforcement Learning - Deep RL
- Reinforcement Learning - General
- Reinforcement Learning - Theory
- Representation Learning
- Sequential, Network, and Time-Series Modeling
- Supervised Learning
- Transfer, Multitask and Meta-learning
- Trustworthy Machine Learning
- Unsupervised and Semi-Supervised Learning
Accountability, Transparency and Interpretability
Concise Explanations of Neural Networks using Adversarial Training
Prasad CHALASANI • Jiefeng Chen • Amrita Roy Chowdhury • Xi Wu • Somesh Jha
Keywords: Accountability, Transparency and Interpretability • Deep Learning - General • Adversarial Examples
PDF Bib Video Supplement
Problems with Shapley-value-based explanations as feature importance measures
I. Elizabeth Kumar • Suresh Venkatasubramanian • Carlos Scheidegger • Sorelle Friedler
Keywords: Accountability, Transparency and Interpretability
PDF Bib Video
When Explanations Lie: Why Many Modified BP Attributions Fail
Leon Sixt • Maximilian Granz • Tim Landgraf
Keywords: Accountability, Transparency and Interpretability • Deep Learning - Theory
PDF Bib Video Supplement
Multidimensional Shape Constraints
Maya Gupta • Erez Louidor • Oleksandr Mangylov • Nobu Morioka • Taman Narayan • Sen Zhao
Keywords: Accountability, Transparency and Interpretability • General Machine Learning Techniques • Supervised Learning • Fairness, Equity, Justice, and Safety
PDF Bib Video Supplement
Explaining Groups of Points in Low-Dimensional Representations
Gregory Plumb • Jonathan Terhorst • Sriram Sankararaman • Ameet Talwalkar
Keywords: Accountability, Transparency and Interpretability • Applications - Neuroscience, Cognitive Science, Biology and Health
PDF Bib Video Supplement
Interpolation between Residual and Non-Residual Networks
Zonghan Yang • Yang Liu • Chenglong Bao • Zuoqiang Shi
Keywords: Accountability, Transparency and Interpretability • Deep Learning - Algorithms
PDF Bib Video Supplement
Fairwashing explanations with off-manifold detergent
Christopher J Anders • Ann-Kathrin Dombrowski • Klaus-Robert Müller • Pan Kessel • Plamen Pasliev
Keywords: Accountability, Transparency and Interpretability • Adversarial Examples
PDF Bib Video Supplement
The Many Shapley Values for Model Explanation
Mukund Sundararajan • Amir Najmi
Keywords: Accountability, Transparency and Interpretability • Learning Theory
PDF Bib Video
Informative Dropout for Robust Representation Learning: A Shape-bias Perspective
Baifeng Shi • Dinghuai Zhang • Qi Dai • Zhanxing Zhu • Yadong Mu • Jingdong Wang
Keywords: Accountability, Transparency and Interpretability • Applications - Computer Vision • Deep Learning - Algorithms • Representation Learning
PDF Bib Video
Invariant Rationalization
Shiyu Chang • Yang Zhang • Mo Yu • Tommi Jaakkola
Keywords: Accountability, Transparency and Interpretability • Applications - Language, Speech and Dialog
PDF Bib Video Supplement
Causal Strategic Linear Regression
Yonadav Shavit • Benjamin L Edelman • Brian Axelrod
Keywords: Accountability, Transparency and Interpretability • Learning Theory • Causality • Trustworthy Machine Learning
PDF Bib Video Supplement
Predictive Multiplicity in Classification
Charles Marx • Flavio Calmon • Berk Ustun
Keywords: Accountability, Transparency and Interpretability • Fairness, Equity, Justice, and Safety
PDF Bib Video
On Second-Order Group Influence Functions for Black-Box Predictions
Samyadeep Basu • Xuchen You • Soheil Feizi
Keywords: Accountability, Transparency and Interpretability • Trustworthy Machine Learning
PDF Bib Video Supplement
Cost-Effective Interactive Attention Learning with Neural Attention Processes
Jay Heo • Junhyeon Park • Hyewon Jeong • Kwang joon Kim • Juho Lee • Eunho Yang • Sung Ju Hwang
Keywords: Accountability, Transparency and Interpretability • Applications - Neuroscience, Cognitive Science, Biology and Health • Sequential, Network, and Time-Series Modeling • Fairness, Equity, Justice, and Safety
PDF Bib Video Supplement
Inverse Active Sensing: Modeling and Understanding Timely Decision-Making
Daniel Jarrett • Mihaela van der Schaar
Keywords: Accountability, Transparency and Interpretability • Decision-Making
PDF Bib Video Supplement
Robust and Stable Black Box Explanations
Himabindu Lakkaraju • Nino Arsov • Osbert Bastani
Keywords: Accountability, Transparency and Interpretability • Supervised Learning
PDF Bib Video Supplement
Proper Network Interpretability Helps Adversarial Robustness in Classification
Akhilan Boopathy • Sijia Liu • Gaoyuan Zhang • Cynthia T Liu • Pin-Yu Chen • Shiyu Chang • Luca Daniel
Keywords: Accountability, Transparency and Interpretability • Adversarial Examples
PDF Bib Video Supplement
Mix-n-Match : Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning
Jize Zhang • Bhavya Kailkhura • T. Yong-Jin Han
Keywords: Accountability, Transparency and Interpretability • Deep Learning - General • Trustworthy Machine Learning • Fairness, Equity, Justice, and Safety
PDF Bib Video Supplement
The Shapley Taylor Interaction Index
Mukund Sundararajan • Kedar Dhamdhere • Ashish Agarwal
Keywords: Accountability, Transparency and Interpretability • Learning Theory
PDF Bib Video Supplement
Efficient nonparametric statistical inference on population feature importance using Shapley values
Brian Williamson • Jean Feng
Keywords: Accountability, Transparency and Interpretability • Learning Theory
PDF Bib Video Supplement
Reverse-engineering deep ReLU networks
David Rolnick • Konrad Kording
Keywords: Accountability, Transparency and Interpretability • Deep Learning - Theory
PDF Bib Video Supplement
Concept Bottleneck Models
Pang Wei Koh • Thao Nguyen • Yew Siang Tang • Stephen O Mussmann • Emma Pierson • Been Kim • Percy Liang
Keywords: Accountability, Transparency and Interpretability
PDF Bib Video Supplement
Generalized and Scalable Optimal Sparse Decision Trees
Jimmy Lin • Chudi Zhong • Diane Hu • Cynthia Rudin • Margo Seltzer
Keywords: Accountability, Transparency and Interpretability • Optimization - General • Fairness, Equity, Justice, and Safety
PDF Bib Video Supplement
Interpretations are Useful: Penalizing Explanations to Align Neural Networks with Prior Knowledge
Laura S Rieger • Chandan Singh • William J Murdoch • Bin Yu
Keywords: Accountability, Transparency and Interpretability • Fairness, Equity, Justice, and Safety
PDF Bib Video Supplement
Transparency Promotion with Model-Agnostic Linear Competitors
Hassan Rafique • Tong Wang • Qihang Lin • Arshia Sighani
Keywords: Accountability, Transparency and Interpretability
PDF Bib Video Supplement
Born-again Tree Ensembles
Thibaut Vidal • Maximilian Schiffer
Keywords: Accountability, Transparency and Interpretability • Supervised Learning • Optimization - General
PDF Bib Video Supplement
Adversarial Examples
Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization
Sicheng Zhu • Xiao Zhang • David Evans
Keywords: Adversarial Examples • Learning Theory • Trustworthy Machine Learning
PDF Bib Video Supplement
Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations
Florian Tramèr • Jens Behrmann • Nicholas Carlini • Nicolas Papernot • Jörn-Henrik Jacobsen
Keywords: Adversarial Examples
PDF Bib Video Supplement
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
Francesco Croce • Matthias Hein
Keywords: Adversarial Examples
PDF Bib Video Supplement
Randomized Smoothing of All Shapes and Sizes
Greg Yang • Tony Duan • J. Edward Hu • Hadi Salman • Ilya Razenshteyn • Jerry Li
Keywords: Adversarial Examples • Trustworthy Machine Learning
PDF Bib Video Supplement
Confidence-Calibrated Adversarial Training: Generalizing to Unseen Attacks
David Stutz • Matthias Hein • Bernt Schiele
Keywords: Adversarial Examples • Deep Learning - Algorithms • Trustworthy Machine Learning • Fairness, Equity, Justice, and Safety
PDF Bib Video Supplement
Parameterized Rate-Distortion Stochastic Encoder
Quan Hoang • Trung Le • Dinh Phung
Keywords: Adversarial Examples • Representation Learning • Supervised Learning
PDF Bib Video Supplement
Overfitting in adversarially robust deep learning
Leslie Rice • Eric Wong • Zico Kolter
Keywords: Adversarial Examples
PDF Bib Video Supplement
Robustness to Programmable String Transformations via Augmented Abstract Training
Yuhao Zhang • Aws Albarghouthi • Loris D'Antoni
Keywords: Adversarial Examples • Applications - Language, Speech and Dialog
PDF Bib Video Supplement
Towards Understanding the Dynamics of the First-Order Adversaries
Zhun Deng • Hangfeng He • Jiaoyang Huang • Weijie Su
Keywords: Adversarial Examples • Deep Learning - Theory • Deep Learning - General • Optimization - Non-convex
PDF Bib Video
Adversarial Robustness via Runtime Masking and Cleansing
Yi-Hsuan Wu • Chia-Hung Yuan • Shan-Hung Wu
Keywords: Adversarial Examples
PDF Bib Video Supplement
Adversarial Neural Pruning with Latent Vulnerability Suppression
Divyam Madaan • Jinwoo Shin • Sung Ju Hwang
Keywords: Adversarial Examples • Trustworthy Machine Learning
PDF Bib Video Supplement
Adversarial Robustness Against the Union of Multiple Perturbation Models
Pratyush Maini • Eric Wong • Zico Kolter
Keywords: Adversarial Examples
PDF Bib Video Supplement
Stronger and Faster Wasserstein Adversarial Attacks
Kaiwen Wu • Allen Houze Wang • Yaoliang Yu
Keywords: Adversarial Examples • Optimization - Convex
PDF Bib Video Supplement
More Data Can Expand The Generalization Gap Between Adversarially Robust and Standard Models
Lin Chen • Yifei Min • Mingrui Zhang • Amin Karbasi
Keywords: Adversarial Examples
PDF Bib Video Supplement
Adversarial Robustness for Code
Pavol Bielik • Martin Vechev
Keywords: Adversarial Examples • Applications - Other
PDF Bib Video Supplement
Defense Through Diverse Directions
Christopher M Bender • Yang Li • Yifeng Shi • Michael K. Reiter • Junier Oliva
Keywords: Adversarial Examples • Deep Learning - General
PDF Bib Video Supplement
On Breaking Deep Generative Model-based Defenses and Beyond
Yanzhi Chen • Renjie Xie • Zhanxing Zhu
Keywords: Adversarial Examples • Applications - Computer Vision • Deep Learning - Generative Models and Autoencoders
PDF Bib Video Supplement
Dual-Path Distillation: A Unified Framework to Improve Black-Box Attacks
Yonggang Zhang • Ya Li • Tongliang Liu • Xinmei Tian
Keywords: Adversarial Examples • Deep Learning - Algorithms
PDF Bib Video Supplement
Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack
Francesco Croce • Matthias Hein
Keywords: Adversarial Examples
PDF Bib Video Supplement
Adversarial Attacks on Probabilistic Autoregressive Forecasting Models
Raphaël Dang-Nhu • Gagandeep Singh • Pavol Bielik • Martin Vechev
Keywords: Adversarial Examples • Sequential, Network, and Time-Series Modeling • Sequential, Network, and Time-Series Modeling
PDF Bib Video Supplement
Curse of Dimensionality on Randomized Smoothing for Certifiable Robustness
Aounon Kumar • Alexander J Levine • Tom Goldstein • Soheil Feizi
Keywords: Adversarial Examples • Deep Learning - Theory • Trustworthy Machine Learning
PDF Bib Video Supplement
Attacks Which Do Not Kill Training Make Adversarial Learning Stronger
Jingfeng Zhang • Xilie Xu • Bo Han • Gang Niu • Lizhen Cui • Masashi Sugiyama • Mohan Kankanhalli
Keywords: Adversarial Examples • Deep Learning - Algorithms • Trustworthy Machine Learning
PDF Bib Video Supplement
Second-Order Provable Defenses against Adversarial Attacks
Sahil Singla • Soheil Feizi
Keywords: Adversarial Examples • Deep Learning - Algorithms • Deep Learning - Theory
PDF Bib Video Supplement
Towards Understanding the Regularization of Adversarial Robustness on Neural Networks
Yuxin Wen • Shuai Li • Kui Jia
Keywords: Adversarial Examples • Learning Theory
PDF Bib Video Supplement
Adversarial Risk via Optimal Transport and Optimal Couplings
Muni Sreenivas Pydi • Varun Jog
Keywords: Adversarial Examples • Learning Theory • Trustworthy Machine Learning
PDF Bib Video Supplement
Randomization matters How to defend against strong adversarial attacks
Rafael B Pinot • Raphael Ettedgui • Geovani Rizk • Yann Chevaleyre • Jamal Atif
Keywords: Adversarial Examples • Learning Theory
PDF Bib Video Supplement
Understanding and Mitigating the Tradeoff between Robustness and Accuracy
Aditi Raghunathan • Sang Michael Xie • Fanny Yang • John Duchi • Percy Liang
Keywords: Adversarial Examples • Unsupervised and Semi-Supervised Learning • Learning Theory
PDF Bib Video Supplement
Applications - Computer Vision
Closed Loop Neural-Symbolic Learning via Integrating Neural Perception, Grammar Parsing, and Symbolic Reasoning
Qing Li • Siyuan Huang • Yining Hong • Yixin Chen • Ying Nian Wu • Song-Chun Zhu
Keywords: Applications - Computer Vision • Applications - Language, Speech and Dialog • Applications - Neuroscience, Cognitive Science, Biology and Health • Deep Learning - Algorithms
PDF Bib Video Supplement
PackIt: A Virtual Environment for Geometric Planning
Akit Goyal • Jia Deng
Keywords: Applications - Computer Vision
PDF Bib Video Supplement
MoNet3D: Towards Accurate Monocular 3D Object Localization in Real Time
Xichuan Zhou • Yicong Peng • Chunqiao Long • Fengbo Ren • Cong Shi
Keywords: Applications - Computer Vision • Applications - Other • Deep Learning - Algorithms • Deep Learning - General
PDF Bib Video
Progressive Graph Learning for Open-Set Domain Adaptation
Yadan Luo • Zijian Wang • Zi Huang • Mahsa Baktashmotlagh
Keywords: Applications - Computer Vision • Applications - Other • Transfer, Multitask and Meta-learning
PDF Bib Video Supplement
Loss Function Search for Face Recognition
Xiaobo Wang • Shuo Wang • Cheng Chi • Shifeng Zhang • Tao Mei
Keywords: Applications - Computer Vision • Deep Learning - Algorithms
PDF Bib Video
Video Prediction via Example Guidance
Jingwei Xu • Huazhe Xu • Bingbing Ni • Xiaokang Yang • Trevor Darrell
Keywords: Applications - Computer Vision • Sequential, Network, and Time-Series Modeling
PDF Bib Video Supplement
Revisiting Training Strategies and Generalization Performance in Deep Metric Learning
Karsten Roth • Timo Milbich • Samrath Sinha • Prateek Gupta • Bjorn Ommer • Joseph Paul Cohen
Keywords: Applications - Computer Vision • General Machine Learning Techniques
PDF Bib Video Supplement
Frustratingly Simple Few-Shot Object Detection
Xin Wang • Thomas Huang • Joseph Gonzalez • Trevor Darrell • Fisher Yu
Keywords: Applications - Computer Vision • Transfer, Multitask and Meta-learning • Transfer, Multitask and Meta-learning
PDF Bib Video Supplement
Towards Accurate Post-training Network Quantization via Bit-Split and Stitching
Peisong Wang • Qiang Chen • Xiangyu He • Jian Cheng
Keywords: Applications - Computer Vision • Deep Learning - Algorithms
PDF Bib Video
Feature-map-level Online Adversarial Knowledge Distillation
Inseop Chung • SeongUk Park • Jangho Kim • Nojun Kwak
Keywords: Applications - Computer Vision • Deep Learning - General • Supervised Learning • Supervised Learning
PDF Bib Video Supplement
Distance Metric Learning with Joint Representation Diversification
Xu Chu • Yang Lin • Yasha Wang • Xiting Wang • Hailong Yu • Xin Gao • Qi Tong
Keywords: Applications - Computer Vision • Deep Learning - Algorithms • General Machine Learning Techniques • Representation Learning
PDF Bib Video Supplement
A Tree-Structured Decoder for Image-to-Markup Generation
Jianshu Zhang • Jun Du • Yongxin Yang • Yi-Zhe Song • Si Wei • Lirong Dai
Keywords: Applications - Computer Vision • Deep Learning - General • Sequential, Network, and Time-Series Modeling
PDF Bib Video
One Size Fits All: Can We Train One Denoiser for All Noise Levels?
Abhiram Gnanasambandam • Stanley Chan
Keywords: Applications - Computer Vision
PDF Bib Video
Analytic Marching: An Analytic Meshing Solution from Deep Implicit Surface Networks
Jiabao Lei • Kui Jia
Keywords: Applications - Computer Vision • Deep Learning - General
PDF Bib Video
Soft Threshold Weight Reparameterization for Learnable Sparsity
Aditya Kusupati • Vivek K Ramanujan • Raghav Somani • Mitchell Wortsman • Prateek Jain • Sham Kakade • Ali Farhadi
Keywords: Applications - Computer Vision • Resource efficient Machine Learning
PDF Bib Video Supplement
Message Passing Least Squares Framework and its Application to Rotation Synchronization
Yunpeng Shi • Gilad Lerman
Keywords: Applications - Computer Vision • Learning Theory • Optimization - Non-convex
PDF Bib Video
More Information Supervised Probabilistic Deep Face Embedding Learning
Ying Huang • Shangfeng Qiu • Wenwei Zhang • Xianghui Luo • Jinzhuo Wang
Keywords: Applications - Computer Vision • Deep Learning - Generative Models and Autoencoders • Supervised Learning • Learning Theory
PDF Bib Video
Neuro-Symbolic Visual Reasoning: Disentangling "Visual" from "Reasoning"
Saeed Amizadeh • Hamid Palangi • Oleksandr Polozov • Yichen Huang • Kazuhito Koishida
Keywords: Applications - Computer Vision • Applications - Language, Speech and Dialog • Sequential, Network, and Time-Series Modeling • Sequential, Network, and Time-Series Modeling
PDF Bib Video Supplement
Operation-Aware Soft Channel Pruning using Differentiable Masks
Minsoo Kang • Bohyung Han
Keywords: Applications - Computer Vision • Deep Learning - General • Deep Learning - General
PDF Bib Video
Adversarial Nonnegative Matrix Factorization
lei luo • Yanfu Zhang • Heng Huang
Keywords: Applications - Computer Vision • General Machine Learning Techniques
PDF Bib Video Supplement
Learning Factorized Weight Matrix for Joint Filtering
Xiangyu Xu • Yongrui Ma • Wenxiu Sun
Keywords: Applications - Computer Vision
PDF Bib Video
Implicit Geometric Regularization for Learning Shapes
Amos Gropp • Lior Yariv • Niv Haim • Matan Atzmon • Yaron Lipman
Keywords: Applications - Computer Vision • Deep Learning - General
PDF Bib Video Supplement
DRWR: A Differentiable Renderer without Rendering for Unsupervised 3D Structure Learning from Silhouette Images
Zhizhong Han • Chao Chen • Yu-Shen Liu • Matthias Zwicker
Keywords: Applications - Computer Vision
PDF Bib Video Supplement
Equivariant Neural Rendering
Emilien Dupont • Miguel Angel Bautista Martin • Alex Colburn • Aditya Sankar • Josh Susskind • Qi Shan
Keywords: Applications - Computer Vision • Deep Learning - General • Deep Learning - Generative Models and Autoencoders • Representation Learning
PDF Bib Video Supplement
Applications - Language, Speech and Dialog
Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs
Meng Qu • Tianyu Gao • Louis-Pascal A. C. Xhonneux • Jian Tang
Keywords: Applications - Language, Speech and Dialog • Deep Learning - General • Transfer, Multitask and Meta-learning • Sequential, Network, and Time-Series Modeling
PDF Bib Video Supplement
Deep Graph Random Process for Relational-Thinking-Based Speech Recognition
Hengguan Huang • Fuzhao Xue • Hao Wang • Ye Wang
Keywords: Applications - Language, Speech and Dialog • Deep Learning - General
PDF Bib Video Supplement
XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalisation
Junjie Hu • Sebastian Ruder • Aditya Siddhant • Graham Neubig • Orhan Firat • Melvin Johnson
Keywords: Applications - Language, Speech and Dialog • Transfer, Multitask and Meta-learning
PDF Bib Video Supplement
Unsupervised Speech Decomposition via Triple Information Bottleneck
Kaizhi Qian • Yang Zhang • Shiyu Chang • Mark Hasegawa-Johnson • David Cox
Keywords: Applications - Language, Speech and Dialog • Deep Learning - Generative Models and Autoencoders • Deep Learning - Generative Models and Autoencoders
PDF Bib Video Supplement
Discriminative Adversarial Search for Abstractive Summarization
Thomas Scialom • Paul-Alexis Dray • Sylvain Lamprier • Benjamin Piwowarski • Jacopo Staiano
Keywords: Applications - Language, Speech and Dialog
PDF Bib Video
Adversarial Mutual Information for Text Generation
Boyuan Pan • Yazheng Yang • Kaizhao Liang • Bhavya Kailkhura • Zhongming Jin • Xian-Sheng Hua • Deng Cai • Bo Li
Keywords: Applications - Language, Speech and Dialog • Sequential, Network, and Time-Series Modeling
PDF Bib Video Supplement
Learning to Encode Position for Transformer with Continuous Dynamical Model
Xuanqing Liu • Hsiang-Fu Yu • Inderjit S. Dhillon • Cho-Jui Hsieh
Keywords: Applications - Language, Speech and Dialog
PDF Bib Video Supplement
How recurrent networks implement contextual processing in sentiment analysis
Niru Maheswaranathan • David Sussillo
Keywords: Applications - Language, Speech and Dialog • Sequential, Network, and Time-Series Modeling
PDF Bib Video Supplement
Neural Topic Modeling with Continual Lifelong Learning
Pankaj Gupta • Yatin Chaudhary • Thomas A. Runkler • Hinrich Schuetze
Keywords: Applications - Language, Speech and Dialog • Online Learning, Active Learning, and Bandits • Transfer, Multitask and Meta-learning
PDF Bib Video Supplement
An EM Approach to Non-autoregressive Conditional Sequence Generation
Zhiqing Sun • Yiming Yang
Keywords: Applications - Language, Speech and Dialog • Sequential, Network, and Time-Series Modeling
PDF Bib Video Supplement
PowerNorm: Rethinking Batch Normalization in Transformers
Sheng Shen • Zhewei Yao • Amir Gholami • Michael Mahoney • Kurt Keutzer
Keywords: Applications - Language, Speech and Dialog • Deep Learning - General
PDF Bib Video Supplement
UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training
Hangbo Bao • Li Dong • Furu Wei • Wenhui Wang • Nan Yang • Xiaodong Liu • Yu Wang • Jianfeng Gao • Songhao Piao • Ming Zhou • Hsiao-Wuen Hon
Keywords: Applications - Language, Speech and Dialog
PDF Bib Video Supplement
On the Relation between Quality-Diversity Evaluation and Distribution-Fitting Goal in Text Generation
Jianing Li • Yanyan Lan • Jiafeng Guo • Xueqi Cheng
Keywords: Applications - Language, Speech and Dialog
PDF Bib Video Supplement
Explainable and Discourse Topic-aware Neural Language Understanding
Yatin Chaudhary • Hinrich Schütze • Pankaj Gupta
Keywords: Applications - Language, Speech and Dialog • Representation Learning
PDF Bib Video Supplement
Description Based Text Classification with Reinforcement Learning
Duo Chai • Wei Wu • Qinghong Han • Fei Wu • Jiwei Li
Keywords: Applications - Language, Speech and Dialog • Reinforcement Learning - Deep RL
PDF Bib Video Supplement
Differentiable Product Quantization for End-to-End Embedding Compression
Ting Chen • Lala Li • Yizhou Sun
Keywords: Applications - Language, Speech and Dialog • Deep Learning - Algorithms • Sequential, Network, and Time-Series Modeling
PDF Bib Video Supplement
PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
Jingqing Zhang • Yao Zhao • Mohammad Saleh • Peter J. Liu
Keywords: Applications - Language, Speech and Dialog • Unsupervised and Semi-Supervised Learning • Transfer, Multitask and Meta-learning
PDF Bib Video Supplement
Non-autoregressive Machine Translation with Disentangled Context Transformer
Jungo Kasai • James Cross • Marjan Ghazvininejad • Jiatao Gu
Keywords: Applications - Language, Speech and Dialog • Sequential, Network, and Time-Series Modeling
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Dispersed Exponential Family Mixture VAEs for Interpretable Text Generation
Wenxian Shi • Hao Zhou • Ning Miao • Lei Li
Keywords: Applications - Language, Speech and Dialog
PDF Bib Video Supplement
Graph-based, Self-Supervised Program Repair from Diagnostic Feedback
Michihiro Yasunaga • Percy Liang
Keywords: Applications - Language, Speech and Dialog • Applications - Other
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A Sequential Self Teaching Approach for Improving Generalization in Sound Event Recognition
Anurag Kumar • Vamsi Ithapu
Keywords: Applications - Language, Speech and Dialog • Deep Learning - Algorithms • Transfer, Multitask and Meta-learning
PDF Bib Video Supplement
Retrieval Augmented Language Model Pre-Training
Kelvin Guu • Kenton Lee • Zora Tung • Panupong Pasupat • Mingwei Chang
Keywords: Applications - Language, Speech and Dialog • Applications - Other • Representation Learning • Transfer, Multitask and Meta-learning
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PoWER-BERT: Accelerating BERT Inference via Progressive Word-vector Elimination
Saurabh Goyal • Anamitra Roy Choudhury • Saurabh Raje • Venkatesan T Chakaravarthy • Yogish Sabharwal • Ashish Verma
Keywords: Applications - Language, Speech and Dialog • Sequential, Network, and Time-Series Modeling • Optimization - General • Optimization - General
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Word-Level Speech Recognition With a Letter to Word Encoder
Ronan Collobert • Awni Hannun • Gabriel Synnaeve
Keywords: Applications - Language, Speech and Dialog • Deep Learning - Algorithms • Deep Learning - General • Sequential, Network, and Time-Series Modeling
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Aligned Cross Entropy for Non-Autoregressive Machine Translation
Marjan Ghazvininejad • Vladimir Karpukhin • Luke Zettlemoyer • Omer Levy
Keywords: Applications - Language, Speech and Dialog • Sequential, Network, and Time-Series Modeling
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LowFER: Low-rank Bilinear Pooling for Link Prediction
Saadullah Amin • Stalin Varanasi • Katherine Ann Dunfield • Günter Neumann
Keywords: Applications - Language, Speech and Dialog • General Machine Learning Techniques • Optimization - General
PDF Bib Video Supplement
Calibration, Entropy Rates, and Memory in Language Models
Mark Braverman • Xinyi Chen • Sham Kakade • Karthik Narasimhan • Cyril Zhang • Yi Zhang
Keywords: Applications - Language, Speech and Dialog • Deep Learning - Generative Models and Autoencoders • Sequential, Network, and Time-Series Modeling • Learning Theory
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WaveFlow: A Compact Flow-based Model for Raw Audio
Wei Ping • Kainan Peng • Kexin Zhao • Zhao Song
Keywords: Applications - Language, Speech and Dialog • Deep Learning - Generative Models and Autoencoders
PDF Bib Video
Structural Language Models of Code
Uri Alon • Roy Sadaka • Omer Levy • Eran Yahav
Keywords: Applications - Language, Speech and Dialog • Code Generation
PDF Bib Video Supplement
Non-Autoregressive Neural Text-to-Speech
Kainan Peng • Wei Ping • Zhao Song • Kexin Zhao
Keywords: Applications - Language, Speech and Dialog
PDF Bib Video
Voice Separation with an Unknown Number of Multiple Speakers
Eliya Nachmani • Yossi Adi • Lior Wolf
Keywords: Applications - Language, Speech and Dialog • Deep Learning - General
PDF Bib Video
Train Big, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers
Zhuohan Li • Eric Wallace • Sheng Shen • Kevin Lin • Kurt Keutzer • Dan Klein • Joey Gonzalez
Keywords: Applications - Language, Speech and Dialog • Deep Learning - Algorithms
PDF Bib Video Supplement
Applications - Neuroscience, Cognitive Science, Biology and Health
Adding seemingly uninformative labels helps in low data regimes
Christos Matsoukas • Albert Bou I Hernandez • Yue Liu • Karin Dembrower • Gisele Miranda • Emir Konuk • Johan Fredin Haslum • Athanasios Zouzos • Peter Lindholm • Fredrik Strand • Kevin Smith
Keywords: Applications - Neuroscience, Cognitive Science, Biology and Health • Applications - Computer Vision • Supervised Learning
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TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics
Alexander Y Tong • Jessie Huang • Guy Wolf • David van Dijk • Smita Krishnaswamy
Keywords: Applications - Neuroscience, Cognitive Science, Biology and Health • Unsupervised and Semi-Supervised Learning • Sequential, Network, and Time-Series Modeling
PDF Bib Video Supplement
A Graph to Graphs Framework for Retrosynthesis Prediction
Chence Shi • Minkai Xu • Hongyu Guo • Ming Zhang • Jian Tang
Keywords: Applications - Neuroscience, Cognitive Science, Biology and Health • Deep Learning - Algorithms • Deep Learning - Generative Models and Autoencoders
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Population-Based Black-Box Optimization for Biological Sequence Design
Christof Angermueller • David Belanger • Andreea Gane • Zelda Mariet • David Dohan • Kevin Murphy • Lucy Colwell • D Sculley
Keywords: Applications - Neuroscience, Cognitive Science, Biology and Health • Optimization - General
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Continuously Indexed Domain Adaptation
Hao Wang • Hao He • Dina Katabi
Keywords: Applications - Neuroscience, Cognitive Science, Biology and Health • Applications - Other • Deep Learning - General • Transfer, Multitask and Meta-learning
PDF Bib Video Supplement
Active World Model Learning in Agent-rich Environments with Progress Curiosity
Kuno Kim • Megumi Sano • Julian De Freitas • Nick Haber • Daniel Yamins
Keywords: Applications - Neuroscience, Cognitive Science, Biology and Health • Reinforcement Learning - General
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SimGANs: Simulator-Based Generative Adversarial Networks for ECG Synthesis to Improve Deep ECG Classification
Tomer Golany • Kira Radinsky • Daniel Freedman
Keywords: Applications - Neuroscience, Cognitive Science, Biology and Health • Deep Learning - Generative Models and Autoencoders • Deep Learning - General
PDF Bib Video Supplement
Two Routes to Scalable Credit Assignment without Weight Symmetry
Daniel Kunin • Aran Nayebi • Javier Sagastuy-Brena • Surya Ganguli • Jonathan Bloom • Daniel Yamins
Keywords: Applications - Neuroscience, Cognitive Science, Biology and Health • Deep Learning - Algorithms • Deep Learning - General • Deep Learning - General
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Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search
Binghong Chen • Chengtao Li • Hanjun Dai • Le Song
Keywords: Applications - Neuroscience, Cognitive Science, Biology and Health • Retrosynthetic Planning
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Learning to Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning
Sai Krishna Gottipati • Boris Sattarov • Sufeng Niu • Yashaswi Pathak • Haoran Wei • Shengchao Liu • Shengchao Liu • Simon Blackburn • Karam Thomas • Connor Coley • Jian Tang • Sarath Chandar • Yoshua Bengio
Keywords: Applications - Neuroscience, Cognitive Science, Biology and Health • Deep Learning - Algorithms • Reinforcement Learning - Deep RL • Reinforcement Learning - General
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Abstraction Mechanisms Predict Generalization in Deep Neural Networks
Alex Gain • Hava Siegelmann
Keywords: Applications - Neuroscience, Cognitive Science, Biology and Health • Deep Learning - Theory • Learning Theory
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Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies
Shengpu Tang • Aditya Modi • Michael Sjoding • Jenna Wiens
Keywords: Applications - Neuroscience, Cognitive Science, Biology and Health • Reinforcement Learning - General
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A general recurrent state space framework for modeling neural dynamics during decision-making
David M Zoltowski • Jonathan W Pillow • Scott Linderman
Keywords: Applications - Neuroscience, Cognitive Science, Biology and Health • Sequential, Network, and Time-Series Modeling • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods
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Mapping natural-language problems to formal-language solutions using structured neural representations
Kezhen Chen • Qiuyuan Huang • Hamid Palangi • Paul Smolensky • Ken Forbus • Jianfeng Gao
Keywords: Applications - Neuroscience, Cognitive Science, Biology and Health • Sequential, Network, and Time-Series Modeling • General Machine Learning Techniques • Representation Learning
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Temporal Phenotyping using Deep Predictive Clustering of Disease Progression
Changhee Lee • Mihaela van der Schaar
Keywords: Applications - Neuroscience, Cognitive Science, Biology and Health • Unsupervised and Semi-Supervised Learning
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DeepCoDA: personalized interpretability for compositional health data
Thomas P. Quinn • Dang Nguyen • Santu Rana • Sunil Gupta • Svetha Venkatesh
Keywords: Applications - Neuroscience, Cognitive Science, Biology and Health • Applications - Neuroscience, Cognitive Science, Biology and Health • Deep Learning - General
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Influenza Forecasting Framework based on Gaussian Processes
Christoph Zimmer • Reza Yaesoubi
Keywords: Applications - Neuroscience, Cognitive Science, Biology and Health • Applications - Neuroscience, Cognitive Science, Biology and Health
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Probing Emergent Semantics in Predictive Agents via Question Answering
Abhishek Das • Federico Carnevale • Hamza Merzic • Laura Rimell • Rosalia Schneider • Josh Abramson • Alden Hung • Arun Ahuja • Stephen Clark • Greg Wayne • Felix Hill
Keywords: Applications - Neuroscience, Cognitive Science, Biology and Health • Reinforcement Learning - Deep RL • Representation Learning
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BoXHED: Boosted eXact Hazard Estimator with Dynamic covariates
Xiaochen Wang • Arash Pakbin • Bobak J Mortazavi • Hongyu Zhao • Donald Lee
Keywords: Applications - Neuroscience, Cognitive Science, Biology and Health • Supervised Learning • Sequential, Network, and Time-Series Modeling
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Emergence of Separable Manifolds in Deep Language Representations
Jonathan Mamou • Hang Le • Miguel A Del Rio • Cory Stephenson • Hanlin Tang • Yoon Kim • SueYeon Chung
Keywords: Applications - Neuroscience, Cognitive Science, Biology and Health • Applications - Language, Speech and Dialog
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Bio-Inspired Hashing for Unsupervised Similarity Search
Chaitanya Ryali • John Hopfield • Leopold Grinberg • Dmitry Krotov
Keywords: Applications - Neuroscience, Cognitive Science, Biology and Health • Applications - Other • Unsupervised and Semi-Supervised Learning
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Entropy Minimization In Emergent Languages
Eugene Kharitonov • Rahma Chaabouni • Diane Bouchacourt • Marco Baroni
Keywords: Applications - Neuroscience, Cognitive Science, Biology and Health • Planning, Control, and Multiagent Learning • Applications - Language, Speech and Dialog
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Visual Grounding of Learned Physical Models
Yunzhu Li • Toru Lin • Kexin Yi • Daniel Bear • Daniel Yamins • Jiajun Wu • Joshua Tenenbaum • Antonio Torralba
Keywords: Applications - Neuroscience, Cognitive Science, Biology and Health
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Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders
Ioana Bica • Ahmed Alaa • Mihaela van der Schaar
Keywords: Applications - Neuroscience, Cognitive Science, Biology and Health
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Student-Teacher Curriculum Learning via Reinforcement Learning: Predicting Hospital Inpatient Admission Location
Rasheed El-Bouri • David Eyre • Peter Watkinson • Tingting Zhu • David Clifton
Keywords: Applications - Neuroscience, Cognitive Science, Biology and Health • Transfer, Multitask and Meta-learning • Reinforcement Learning - General • Supervised Learning
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Efficient Non-conjugate Gaussian Process Factor Models for Spike Count Data using Polynomial Approximations
Stephen L Keeley • David Zoltowski • Yiyi Yu • Spencer Smith • Jonathan W Pillow
Keywords: Applications - Neuroscience, Cognitive Science, Biology and Health • Probabilistic Inference - Models and Probabilistic Programming • Gaussian Processes
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Causal Effect Estimation and Optimal Dose Suggestions in Mobile Health
Liangyu Zhu • Wenbin Lu • Rui Song
Keywords: Applications - Neuroscience, Cognitive Science, Biology and Health • General Machine Learning Techniques • Sequential, Network, and Time-Series Modeling • Causality
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A Generative Model for Molecular Distance Geometry
Gregor N. C. Simm • José Miguel Hernández-Lobato
Keywords: Applications - Neuroscience, Cognitive Science, Biology and Health • Deep Learning - Generative Models and Autoencoders • Deep Learning - Generative Models and Autoencoders • Representation Learning
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Applications - Other
Divide and Conquer: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks
Ahmed Taha Elthakeb • Prannoy Pilligundla • Fatemeh Mireshghallah • Alexander Cloninger • Hadi Esmaeilzadeh
Keywords: Applications - Other • Deep Learning - General
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Adversarial Attacks on Copyright Detection Systems
Parsa Saadatpanah • Ali Shafahi • Tom Goldstein
Keywords: Applications - Other • Adversarial Examples
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Online Learned Continual Compression with Adaptive Quantization Modules
Lucas Caccia • Eugene Belilovsky • Massimo Caccia • Joelle Pineau
Keywords: Applications - Other • Continual Learning
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Learning Optimal Tree Models under Beam Search
Jingwei Zhuo • Ziru Xu • Wei Dai • Han Zhu • Han Li • Jian Xu • Kun Gai
Keywords: Applications - Other • Applications - Other
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Why Are Learned Indexes So Effective?
Paolo Ferragina • Fabrizio Lillo • Giorgio Vinciguerra
Keywords: Applications - Other • Applications - Other • Applications - Other
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Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising
Xiaotian Hao • Zhaoqing Peng • Yi Ma • Guan Wang • Junqi Jin • Jianye Hao • Shan Chen • Rongquan Bai • Mingzhou Xie • Miao Xu • Zhenzhe Zheng • Chuan Yu • Han Li • Jian Xu • Kun Gai
Keywords: Applications - Other • Reinforcement Learning - Deep RL • Reinforcement Learning - General • Optimization - General
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Bayesian Graph Neural Networks with Adaptive Connection Sampling
Arman Hasanzadeh • Ehsan Hajiramezanali • Shahin Boluki • Mingyuan Zhou • Nick Duffield • Krishna Narayanan • Xiaoning Qian
Keywords: Applications - Other • Deep Learning - General
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Inductive-bias-driven Reinforcement Learning For Efficient Schedules in Heterogeneous Clusters
Subho S Banerjee • Saurabh Jha • Zbigniew Kalbarczyk • Ravishankar Iyer
Keywords: Applications - Other • Reinforcement Learning - Deep RL • Probabilistic Inference - Models and Probabilistic Programming • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods
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An Imitation Learning Approach for Cache Replacement
Evan Liu • Milad Hashemi • Kevin Swersky • Parthasarathy Ranganathan • Junwhan Ahn
Keywords: Applications - Other • Reinforcement Learning - Deep RL
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Discriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions
Ahmed Alaa • Mihaela van der Schaar
Keywords: Applications - Other • Trustworthy Machine Learning
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Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach
Martin Mladenov • Elliot Creager • Omer Ben-Porat • Kevin Swersky • Richard Zemel • Craig Boutilier
Keywords: Applications - Other • Optimization - General • Fairness, Equity, Justice, and Safety
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NetGAN without GAN: From Random Walks to Low-Rank Approximations
Luca Rendsburg • Holger Heidrich • Ulrike von Luxburg
Keywords: Applications - Other • Deep Learning - General
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Learning Selection Strategies in Buchberger’s Algorithm
Dylan Peifer • Michael Stillman • Daniel Halpern-Leistner
Keywords: Applications - Other • Reinforcement Learning - General
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Global Concavity and Optimization in a Class of Dynamic Discrete Choice Models
Yiding Feng • Ekaterina Khmelnitskaya • Denis Nekipelov
Keywords: Applications - Other • Reinforcement Learning - General • Reinforcement Learning - Theory
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Multi-Precision Policy Enforced Training (MuPPET) : A Precision-Switching Strategy for Quantised Fixed-Point Training of CNNs
Aditya Rajagopal • Diederik Vink • Stylianos Venieris • Christos-Savvas Bouganis
Keywords: Applications - Other • Applications - Computer Vision • Deep Learning - Algorithms • General Machine Learning Techniques
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Leveraging Frequency Analysis for Deep Fake Image Recognition
Joel Frank • Thorsten Eisenhofer • Lea Schönherr • Asja Fischer • Dorothea Kolossa • Thorsten Holz
Keywords: Applications - Other • Deep Learning - General • Deep Learning - General
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Robust Graph Representation Learning via Neural Sparsification
Cheng Zheng • Bo Zong • Wei Cheng • Dongjin Song • Jingchao Ni • Wenchao Yu • Haifeng Chen • Wei Wang
Keywords: Applications - Other • Sequential, Network, and Time-Series Modeling • Representation Learning • Unsupervised and Semi-Supervised Learning
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Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters
Wenhui Yu • Zheng Qin
Keywords: Applications - Other
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Learning Robot Skills with Temporal Variational Inference
Tanmay Shankar • Abhinav Gupta
Keywords: Applications - Other • Reinforcement Learning - Deep RL • Unsupervised and Semi-Supervised Learning
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Transformer Hawkes Process
Simiao Zuo • Haoming Jiang • Zichong Li • Tuo Zhao • Hongyuan Zha
Keywords: Applications - Other • Sequential, Network, and Time-Series Modeling
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Learning to Simulate and Design for Structural Engineering
Kai-Hung Chang • Chin-Yi Cheng
Keywords: Applications - Other • Sequential, Network, and Time-Series Modeling
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When deep denoising meets iterative phase retrieval
Yaotian Wang • Xiaohang Sun • Jason Fleischer
Keywords: Applications - Other • Optimization - Non-convex
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Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functions
Ahmed Alaa • Mihaela van der Schaar
Keywords: Applications - Other • Applications - Neuroscience, Cognitive Science, Biology and Health
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Adaptive Droplet Routing in Digital Microfluidic Biochips Using Deep Reinforcement Learning
Tung-Che Liang • Zhanwei Zhong • Yaas Bigdeli • Tsung-Yi Ho • Krishnendu Chakrabarty • Richard Fair
Keywords: Applications - Other • Applications - Neuroscience, Cognitive Science, Biology and Health • Applications - Other • Reinforcement Learning - Deep RL
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Uncertainty-Aware Lookahead Factor Models for Quantitative Investing
Lakshay Chauhan • John Alberg • Zachary Lipton
Keywords: Applications - Other • Sequential, Network, and Time-Series Modeling
PDF Bib Video Supplement
Reinforcement Learning for Molecular Design Guided by Quantum Mechanics
Gregor N. C. Simm • Robert Pinsler • José Miguel Hernández-Lobato
Keywords: Applications - Other • Reinforcement Learning - Deep RL
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Amortized Finite Element Analysis for Fast PDE-Constrained Optimization
Tianju Xue • Alex Beatson • Sigrid Adriaenssens • Ryan P. Adams
Keywords: Applications - Other • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods
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Accelerating Large-Scale Inference with Anisotropic Vector Quantization
Ruiqi Guo • Philip Sun • Erik Lindgren • Quan Geng • David Simcha • Felix Chern • Sanjiv Kumar
Keywords: Applications - Other • Applications - Other • Optimization - Large Scale, Parallel and Distributed
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Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraints
Cong Shen • Zhiyang Wang • Sofia Villar • Mihaela van der Schaar
Keywords: Applications - Other • Fairness, Equity, Justice, and Safety
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Goodness-of-Fit Tests for Inhomogeneous Random Graphs
Soham Dan • Bhaswar B. Bhattacharya
Keywords: Applications - Other • Learning Theory
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A Markov Decision Process Model for Socio-Economic Systems Impacted by Climate Change
Salman Sadiq Shuvo • Yasin Yilmaz • Alan Bush • Mark Hafen
Keywords: Applications - Other • Applications - Other • Applications - Other • Reinforcement Learning - Deep RL
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Causality
LazyIter: A Fast Algorithm for Counting Markov Equivalent DAGs and Designing Experiments
Ali AhmadiTeshnizi • Saber Salehkaleybar • Negar Kiyavash
Keywords: Causality • Probabilistic Inference - Models and Probabilistic Programming
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Invariant Risk Minimization Games
Kartik Ahuja • Karthikeyan Shanmugam • Kush R. Varshney • Amit Dhurandhar
Keywords: Causality • Learning Theory
PDF Bib Video Supplement
Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models
Yuta Saito • Shota Yasui
Keywords: Causality • Evaluation metrics
PDF Bib Video Supplement
Full Law Identification in Graphical Models of Missing Data: Completeness Results
Razieh Nabi • Rohit Bhattacharya • Ilya Shpitser
Keywords: Causality • Missing Data
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Efficient Identification in Linear Structural Causal Models with Auxiliary Cutsets
Daniel Kumor • Carlos Cinelli • Elias Bareinboim
Keywords: Causality • Probabilistic Inference - Models and Probabilistic Programming
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Characterizing Distribution Equivalence and Structure Learning for Cyclic and Acyclic Directed Graphs
AmirEmad Ghassami • Alan Yang • Negar Kiyavash • Kun Zhang
Keywords: Causality • Probabilistic Inference - Models and Probabilistic Programming
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Efficient Intervention Design for Causal Discovery with Latents
Raghavendra Addanki • Shiva Kasiviswanathan • Andrew McGregor • Cameron Musco
Keywords: Causality • Optimization - General
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Estimation of Bounds on Potential Outcomes For Decision Making
Maggie Makar • Fredrik Johansson • John Guttag • David Sontag
Keywords: Causality • Applications - Neuroscience, Cognitive Science, Biology and Health • General Machine Learning Techniques • Learning Theory
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Efficient Policy Learning from Surrogate-Loss Classification Reductions
Andrew Bennett • Nathan Kallus
Keywords: Causality • Learning Theory
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Designing Optimal Dynamic Treatment Regimes: A Causal Reinforcement Learning Approach
Junzhe Zhang
Keywords: Causality • Reinforcement Learning - General
PDF Bib Video
Cost-effectively Identifying Causal Effects When Only Response Variable is Observable
Tian-Zuo Wang • Xi-Zhu Wu • Sheng-Jun Huang • Zhi-Hua Zhou
Keywords: Causality
PDF Bib Video Supplement
DeepMatch: Balancing Deep Covariate Representations for Causal Inference Using Adversarial Training
Nathan Kallus
Keywords: Causality • Adversarial Examples
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Learning and Sampling of Atomic Interventions from Observations
Arnab Bhattacharyya • Sutanu Gayen • SARAVANAN KANDASAMY • Ashwin Maran • Vinodchandran N. Variyam
Keywords: Causality • Learning Theory • Learning Theory • Probabilistic Inference - Models and Probabilistic Programming
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Causal Inference using Gaussian Processes with Structured Latent Confounders
Sam A Witty • Kenta Takatsu • David Jensen • Vikash Mansinghka
Keywords: Causality • Probabilistic Inference - Models and Probabilistic Programming • Gaussian Processes • Probabilistic Inference - Models and Probabilistic Programming
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Causal Effect Identifiability under Partial-Observability
Sanghack Lee • Elias Bareinboim
Keywords: Causality
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Causal Structure Discovery from Distributions Arising from Mixtures of DAGs
Basil N. Saeed • Snigdha Panigrahi • Caroline Uhler
Keywords: Causality • Probabilistic Inference - Models and Probabilistic Programming
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Distinguishing Cause from Effect Using Quantiles: Bivariate Quantile Causal Discovery
Natasa Tagasovska • Valérie Chavez-Demoulin • Thibault Vatter
Keywords: Causality • Learning Theory • Learning Theory • Trustworthy Machine Learning
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Deep Learning - Algorithms
Inducing and Exploiting Activation Sparsity for Fast Inference on Deep Neural Networks
Mark Kurtz • Justin Kopinsky • Rati Gelashvili • Alexander Matveev • John Carr • Michael Goin • William Leiserson • Sage Moore • Nir Shavit • Dan Alistarh
Keywords: Deep Learning - Algorithms • Applications - Computer Vision • Deep Learning - General
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Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup
Jang-Hyun Kim • Wonho Choo • Hyun Oh Song
Keywords: Deep Learning - Algorithms • Deep Learning - General
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Adversarial Filters of Dataset Biases
Ronan Le Bras • Swabha Swayamdipta • Chandra Bhagavatula • Rowan Zellers • Matthew Peters • Ashish Sabharwal • Yejin Choi
Keywords: Deep Learning - Algorithms • Applications - Computer Vision • Applications - Language, Speech and Dialog
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Learning Representations that Support Extrapolation
Taylor W Webb • Zachary Dulberg • Steven Frankland • Alexander Petrov • Randall O'Reilly • Jonathan Cohen
Keywords: Deep Learning - Algorithms • Deep Learning - General
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Towards Adaptive Residual Network Training: A Neural-ODE Perspective
chengyu dong • Liyuan Liu • Zichao Li • Jingbo Shang
Keywords: Deep Learning - Algorithms • Deep Learning - General
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Data Valuation using Reinforcement Learning
Jinsung Yoon • Sercan Ö. Arık • Tomas Pfister
Keywords: Deep Learning - Algorithms • Deep Learning - General
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Efficient Proximal Mapping of the 1-path-norm of Shallow Networks
Fabian R Latorre • Paul Rolland • Nadav Hallak • Volkan Cevher
Keywords: Deep Learning - Algorithms • Deep Learning - General • Optimization - Non-convex • Adversarial Examples
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Rigging the Lottery: Making All Tickets Winners
Utku Evci • Trevor Gale • Jacob Menick • Pablo Samel Castro • Erich Elsen
Keywords: Deep Learning - Algorithms • Applications - Computer Vision • Deep Learning - General • Optimization - General
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DessiLBI: Exploring Structural Sparsity of Deep Networks via Differential Inclusion Paths
Yanwei Fu • Chen Liu • Donghao Li • Xinwei Sun • Jinshan ZENG • Yuan Yao
Keywords: Deep Learning - Algorithms • Deep Learning - General • Optimization - General
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SoftSort: A Continuous Relaxation for the argsort Operator
Sebastian Prillo • Julian M Eisenschlos
Keywords: Deep Learning - Algorithms • Supervised Learning
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RIFLE: Backpropagation in Depth for Deep Transfer Learning through Re-Initializing the Fully-connected LayEr
Xingjian Li • Haoyi Xiong • Haozhe An • Cheng-Zhong Xu • Dejing Dou
Keywords: Deep Learning - Algorithms • Transfer, Multitask and Meta-learning
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Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention
Angelos Katharopoulos • Apoorv Vyas • Nikolaos Pappas • François Fleuret
Keywords: Deep Learning - Algorithms • Deep Learning - General • Sequential, Network, and Time-Series Modeling
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Learning Algebraic Multigrid Using Graph Neural Networks
Ilay Luz • Meirav Galun • Haggai Maron • Ronen Basri • Irad Yavneh
Keywords: Deep Learning - Algorithms • Deep Learning Based Linear Solvers
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Self-supervised Label Augmentation via Input Transformations
Hankook Lee • Sung Ju Hwang • Jinwoo Shin
Keywords: Deep Learning - Algorithms • Representation Learning • Transfer, Multitask and Meta-learning
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Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODE
Juntang Zhuang • Nicha Dvornek • Xiaoxiao Li • Sekhar Tatikonda • Xenophon Papademetris • James S Duncan
Keywords: Deep Learning - Algorithms • Deep Learning - General • Supervised Learning
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Confidence-Aware Learning for Deep Neural Networks
Jooyoung Moon • Jihyo Kim • Younghak Shin • Sangheum Hwang
Keywords: Deep Learning - Algorithms • confidence estimation
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Optimizing Data Usage via Differentiable Rewards
Xinyi Wang • Hieu Pham • Paul Michel • Antonios Anastasopoulos • Jaime Carbonell • Graham Neubig
Keywords: Deep Learning - Algorithms • Applications - Computer Vision • Applications - Language, Speech and Dialog
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Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation
Xiang Jiang • Qicheng Lao • Stan Matwin • Mohammad Havaei
Keywords: Deep Learning - Algorithms • Unsupervised and Semi-Supervised Learning
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Beyond Synthetic Noise: Deep Learning on Controlled Noisy Labels
Lu Jiang • Di Huang • Mason Liu • Weilong Yang
Keywords: Deep Learning - Algorithms • Trustworthy Machine Learning
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Graph Optimal Transport for Cross-Domain Alignment
Liqun Chen • Zhe Gan • Yu Cheng • Linjie Li • Lawrence Carin Duke • Jingjing Liu
Keywords: Deep Learning - Algorithms
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Do We Need Zero Training Loss After Achieving Zero Training Error?
Takashi Ishida • Ikko Yamane • Tomoya Sakai • Gang Niu • Masashi Sugiyama
Keywords: Deep Learning - Algorithms • Deep Learning - General
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SIGUA: Forgetting May Make Learning with Noisy Labels More Robust
Bo Han • Gang Niu • Xingrui Yu • Quanming Yao • Miao Xu • Ivor Tsang • Masashi Sugiyama
Keywords: Deep Learning - Algorithms • Unsupervised and Semi-Supervised Learning
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Error-Bounded Correction of Noisy Labels
Songzhu Zheng • pengxiang wu • Aman Goswami • Mayank Goswami • Dimitris N. Metaxas • Chao Chen
Keywords: Deep Learning - Algorithms • Supervised Learning
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Sparse Sinkhorn Attention
Yi Tay • Dara Bahri • Liu Yang • Donald Metzler • Da-Cheng Juan
Keywords: Deep Learning - Algorithms • Deep Learning - General
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Finding trainable sparse networks through Neural Tangent Transfer
Tianlin Liu • Friedemann Zenke
Keywords: Deep Learning - Algorithms • Deep Learning - General • Deep Learning - General • Transfer, Multitask and Meta-learning
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Does label smoothing mitigate label noise?
Michal Lukasik • Srinadh Bhojanapalli • Aditya K Menon • Sanjiv Kumar
Keywords: Deep Learning - Algorithms • Deep Learning - Theory • Supervised Learning
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Guided Learning of Nonconvex Models through Successive Functional Gradient Optimization
Rie Johnson • Tong Zhang
Keywords: Deep Learning - Algorithms • Deep Learning - General
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Up or Down? Adaptive Rounding for Post-Training Quantization
Markus Nagel • Rana Ali Amjad • Mart van Baalen • Christos Louizos • Tijmen Blankevoort
Keywords: Deep Learning - Algorithms • Quantization
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Tuning-free Plug-and-Play Proximal Algorithm for Inverse Imaging Problems
Kaixuan Wei • Angelica I Aviles-Rivero • Jingwei Liang • Ying Fu • Carola-Bibiane B Schönlieb • Hua Huang
Keywords: Deep Learning - Algorithms
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Haar Graph Pooling
Yu Guang Wang • Ming Li • Zheng Ma • Guido Montúfar • Xiaosheng Zhuang • Yanan Fan
Keywords: Deep Learning - Algorithms • Deep Learning - General
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Countering Language Drift with Seeded Iterated Learning
Yuchen Lu • Soumye Singhal • Florian Strub • Aaron Courville • Olivier Pietquin
Keywords: Deep Learning - Algorithms • Applications - Language, Speech and Dialog • Supervised Learning
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Go Wide, Then Narrow: Efficient Training of Deep Thin Networks
Denny Zhou • Mao Ye • Chen Chen • Tianjian Meng • Mingxing Tan • Xiaodan Song • Quoc Le • Qiang Liu • Dale Schuurmans
Keywords: Deep Learning - Algorithms • Deep Learning - General
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Extrapolation for Large-batch Training in Deep Learning
Tao Lin • Lingjing Kong • Sebastian Stich • Martin Jaggi
Keywords: Deep Learning - Algorithms • Deep Learning - General • Optimization - Large Scale, Parallel and Distributed
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Generating Programmatic Referring Expressions via Program Synthesis
Jiani Huang • Calvin Smith • Osbert Bastani • Rishabh Singh • Aws Albarghouthi • Mayur Naik
Keywords: Deep Learning - Algorithms
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Deep Learning - General
On Relativistic f-Divergences
Alexia Jolicoeur-Martineau
Keywords: Deep Learning - General • Applications - Computer Vision • Deep Learning - Generative Models and Autoencoders • Probabilistic Inference - Models and Probabilistic Programming
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Graph Structure of Neural Networks
Jiaxuan You • Jure Leskovec • Kaiming He • Saining Xie
Keywords: Deep Learning - General • Applications - Other • Sequential, Network, and Time-Series Modeling
PDF Bib Video Supplement
PENNI: Pruned Kernel Sharing for Efficient CNN Inference
Shiyu Li • Edward Hanson • Hai Li • Yiran Chen
Keywords: Deep Learning - General • Applications - Computer Vision • Applications - Other
PDF Bib Video Supplement
Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum Search
Yong Guo • Yaofo Chen • Yin Zheng • Peilin Zhao • Jian Chen • Junzhou Huang • Mingkui Tan
Keywords: Deep Learning - General • Deep Learning - Algorithms
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Small Data, Big Decisions: Model Selection in the Small-Data Regime
Jörg Bornschein • Francesco Visin • Simon Osindero
Keywords: Deep Learning - General • Supervised Learning
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Scalable Differentiable Physics for Learning and Control
Yi-Ling Qiao • Junbang Liang • Vladlen Koltun • Ming C. Lin
Keywords: Deep Learning - General
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Variational Bayesian Quantization
Yibo Yang • Robert Bamler • Stephan Mandt
Keywords: Deep Learning - General • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods • Probabilistic Inference - Models and Probabilistic Programming
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DropNet: Reducing Neural Network Complexity via Iterative Pruning
Chong Min John Tan • Mehul Motani
Keywords: Deep Learning - General • Supervised Learning
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Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors
Michael W Dusenberry • Ghassen Jerfel • Yeming Wen • Yian Ma • Jasper Snoek • Katherine Heller • Balaji Lakshminarayanan • Dustin Tran
Keywords: Deep Learning - General
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GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation
Marc Brockschmidt
Keywords: Deep Learning - General
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Neural Clustering Processes
Ari Pakman • Yueqi Wang • Catalin Mitelut • JinHyung Lee • Liam Paninski
Keywords: Deep Learning - General • Applications - Neuroscience, Cognitive Science, Biology and Health • Unsupervised and Semi-Supervised Learning • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods
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Pretrained Generalized Autoregressive Model with Adaptive Probabilistic Label Clusters for Extreme Multi-label Text Classification
Hui Ye • Zhiyu Chen • Da-Han Wang • Brian Davison
Keywords: Deep Learning - General • Applications - Language, Speech and Dialog • Supervised Learning • Optimization - Large Scale, Parallel and Distributed
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Explicit Gradient Learning for Black-Box Optimization
Elad Sarafian • mor sinay • yoram louzoun • Noa Agmon • Sarit Kraus
Keywords: Deep Learning - General • Deep Learning - Algorithms • Optimization - Non-convex
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Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks
Agustinus Kristiadi • Matthias Hein • Philipp Hennig
Keywords: Deep Learning - General • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods • Trustworthy Machine Learning • Fairness, Equity, Justice, and Safety
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Inferring DQN structure for high-dimensional continuous control
Andrey Sakryukin • Chedy Raissi • Mohan Kankanhalli
Keywords: Deep Learning - General • Reinforcement Learning - Deep RL • Reinforcement Learning - General
PDF Bib Video Supplement
On the Generalization Benefit of Noise in Stochastic Gradient Descent
Samuel L. Smith • Erich Elsen • Soham De
Keywords: Deep Learning - General • Applications - Computer Vision
PDF Bib Video Supplement
AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks
Yonggan Fu • Wuyang Chen • Haotao Wang • Haoran Li • Yingyan Lin • Zhangyang Wang
Keywords: Deep Learning - General • Transfer, Multitask and Meta-learning
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Graph Filtration Learning
Christoph Hofer • Florian Graf • Bastian A Rieck • Marc Niethammer • Roland Kwitt
Keywords: Deep Learning - General • Applications - Other • Representation Learning • Supervised Learning
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Boosting Deep Neural Network Efficiency with Dual-Module Inference
Liu Liu • Lei Deng • Zhaodong Chen • Yuke Wang • Shuangchen Li • Jingwei Zhang • Yihua Yang • Zhenyu Gu • Yufei Ding • Yuan Xie
Keywords: Deep Learning - General • Deep Learning - Algorithms • Deep Learning - General • General Machine Learning Techniques
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Multigrid Neural Memory
Tri Huynh • Michael Maire • Matthew Walter
Keywords: Deep Learning - General • Sequential, Network, and Time-Series Modeling
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Deep Isometric Learning for Visual Recognition
Haozhi Qi • Chong You • Xiaolong Wang • Yi Ma • Jitendra Malik
Keywords: Deep Learning - General • Applications - Computer Vision • Deep Learning - General
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An Explicitly Relational Neural Network Architecture
Murray P Shanahan • Kyriacos Nikiforou • Antonia Creswell • Christos Kaplanis • David Barrett • Marta Garnelo
Keywords: Deep Learning - General • Representation Learning
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Convergence Rates of Variational Inference in Sparse Deep Learning
Badr-Eddine Chérief-Abdellatif
Keywords: Deep Learning - General • Deep Learning - Theory • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods • Probabilistic Inference - Models and Probabilistic Programming
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Linear Mode Connectivity and the Lottery Ticket Hypothesis
Jonathan Frankle • Gintare Karolina Dziugaite • Daniel M. Roy • Michael Carbin
Keywords: Deep Learning - General • Empirical Analysis of Deep Learning
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On Leveraging Pretrained GANs for Generation with Limited Data
Miaoyun Zhao • Yulai Cong • Lawrence Carin Duke
Keywords: Deep Learning - General • Applications - Computer Vision • Transfer, Multitask and Meta-learning
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CLUB: A Contrastive Log-ratio Upper Bound of Mutual Information
Pengyu Cheng • Weituo Hao • Shuyang Dai • Jiachang Liu • Zhe Gan • Lawrence Carin Duke
Keywords: Deep Learning - General
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Understanding and Stabilizing GANs' Training Dynamics Using Control Theory
Kun Xu • Chongxuan Li • Jun Zhu • Bo Zhang
Keywords: Deep Learning - General • Deep Learning - Generative Models and Autoencoders
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Let's Agree to Agree: Neural Networks Share Classification Order on Real Datasets
Guy Hacohen • Leshem Choshen • Daphna Weinshall
Keywords: Deep Learning - General • Deep Learning - General • Representation Learning
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Normalized Flat Minima: Exploring Scale Invariant Definition of Flat Minima for Neural Networks Using PAC-Bayesian Analysis
Yusuke Tsuzuku • Issei Sato • Masashi Sugiyama
Keywords: Deep Learning - General
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Optimizer Benchmarking Needs to Account for Hyperparameter Tuning
Prabhu Teja Sivaprasad • Florian Mai • Thijs Vogels • Martin Jaggi • François Fleuret
Keywords: Deep Learning - General • Transfer, Multitask and Meta-learning • Optimization - General
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Training Binary Neural Networks using the Bayesian Learning Rule
Xiangming Meng • Roman Bachmann • Mohammad Emtiyaz Khan
Keywords: Deep Learning - General • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods
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Variance Reduction in Stochastic Particle-Optimization Sampling
Jianyi Zhang • Yang Zhao • Changyou Chen
Keywords: Deep Learning - General • Probabilistic Inference - Models and Probabilistic Programming • Probabilistic Inference - Models and Probabilistic Programming
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Orthogonalized SGD and Nested Architectures for Anytime Neural Networks
Chengcheng Wan • Henry (Hank) Hoffmann • shan lu • Michael Maire
Keywords: Deep Learning - General • Deep Learning - General
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Training Neural Networks for and by Interpolation
Leonard Berrada • Andrew Zisserman • M. Pawan Kumar
Keywords: Deep Learning - General • Optimization - Large Scale, Parallel and Distributed
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Small-GAN: Speeding up GAN Training using Core-Sets
Samrath Sinha • Han Zhang • Anirudh Goyal • Yoshua Bengio • Hugo Larochelle • Augustus Odena
Keywords: Deep Learning - General • Deep Learning - Generative Models and Autoencoders • Unsupervised and Semi-Supervised Learning
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Implicit Euler Skip Connections: Enhancing Adversarial Robustness via Numerical Stability
Mingjie Li • Lingshen He • Zhouchen Lin
Keywords: Deep Learning - General • Supervised Learning • Adversarial Examples
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Feature Quantization Improves GAN Training
Yang Zhao • Chunyuan Li • Ping Yu • Jianfeng Gao • Changyou Chen
Keywords: Deep Learning - General • Applications - Computer Vision • Deep Learning - General • Deep Learning - Generative Models and Autoencoders
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Multi-Agent Routing Value Iteration Network
Quinlan Sykora • Mengye Ren • Raquel Urtasun
Keywords: Deep Learning - General • Planning, Control, and Multiagent Learning • Planning, Control, and Multiagent Learning • Optimization - General
PDF Bib Video Supplement
How Good is the Bayes Posterior in Deep Neural Networks Really?
Florian Wenzel • Kevin Roth • Bastiaan S Veeling • Jakub Swiatkowski • Linh Tran • Stephan M Mandt • Jasper Snoek • Tim Salimans • Rodolphe Jenatton • Sebastian Nowozin
Keywords: Deep Learning - General • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods • Probabilistic Inference - Models and Probabilistic Programming • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods
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Circuit-Based Intrinsic Methods to Detect Overfitting
Satrajit Chatterjee • Alan Mishchenko
Keywords: Deep Learning - General • Deep Learning - Algorithms • Accountability, Transparency and Interpretability • Fairness, Equity, Justice, and Safety
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PDO-eConvs: Partial Differential Operator Based Equivariant Convolutions
Zhengyang Shen • Lingshen He • Zhouchen Lin • Jinwen Ma
Keywords: Deep Learning - General • Supervised Learning
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Unsupervised Discovery of Interpretable Directions in the GAN Latent Space
Andrey Voynov • Artem Babenko
Keywords: Deep Learning - General • Deep Learning - Generative Models and Autoencoders • Unsupervised and Semi-Supervised Learning • Accountability, Transparency and Interpretability
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On the Noisy Gradient Descent that Generalizes as SGD
Jingfeng Wu • Wenqing Hu • Haoyi Xiong • Jun Huan • Vladimir Braverman • Zhanxing Zhu
Keywords: Deep Learning - General • Deep Learning - Algorithms • Deep Learning - Theory • Optimization - General
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Estimating Model Uncertainty of Neural Networks in Sparse Information Form
Jongseok Lee • Matthias Humt • Jianxiang Feng • Rudolph Triebel
Keywords: Deep Learning - General • Applications - Other • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods • Probabilistic Inference - Models and Probabilistic Programming
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Training Linear Neural Networks: Non-Local Convergence and Complexity Results
Armin Eftekhari
Keywords: Deep Learning - General • Deep Learning - Theory • Optimization - General • Optimization - Non-convex
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Channel Equilibrium Networks for Learning Deep Representation
Wenqi Shao • Shitao Tang • Xingang Pan • Ping Tan • Xiaogang Wang • Ping Luo
Keywords: Deep Learning - General • Applications - Computer Vision • Sequential, Network, and Time-Series Modeling
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The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks
Jakub Swiatkowski • Kevin Roth • Bastiaan S Veeling • Linh Tran • Joshua V Dillon • Jasper Snoek • Stephan M Mandt • tim salimans • Rodolphe Jenatton • Sebastian Nowozin
Keywords: Deep Learning - General • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods • Probabilistic Inference - Models and Probabilistic Programming
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Universal Equivariant Multilayer Perceptrons
Siamak Ravanbakhsh
Keywords: Deep Learning - General • equivariance
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Do GANs always have Nash equilibria?
Farzan Farnia • Asuman Ozdaglar
Keywords: Deep Learning - General • Deep Learning - Theory • Deep Learning - General • Learning Theory
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Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection
Mao Ye • Chengyue Gong • Lizhen Nie • Denny Zhou • Adam Klivans • Qiang Liu
Keywords: Deep Learning - General • Applications - Computer Vision • Deep Learning - Algorithms
PDF Bib Video Supplement
From ImageNet to Image Classification: Contextualizing Progress on Benchmarks
Dimitris Tsipras • Shibani Santurkar • Logan Engstrom • Andrew Ilyas • Aleksander Madry
Keywords: Deep Learning - General • Datasets
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Neural Architecture Search in A Proxy Validation Loss Landscape
Yanxi Li • Minjing Dong • Yunhe Wang • Chang Xu
Keywords: Deep Learning - General
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Lorentz Group Equivariant Neural Network for Particle Physics
Alexander Bogatskiy • Brandon M Anderson • Jan Offermann • Marwah Roussi • David Miller • Risi Kondor
Keywords: Deep Learning - General • Applications - Other • Deep Learning - Theory
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Why bigger is not always better: on finite and infinite neural networks
Laurence Aitchison
Keywords: Deep Learning - General
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Learning disconnected manifolds: a no GAN's land
Ugo Tanielian • Thibaut Issenhuth • Elvis Dohmatob • Jeremie Mary
Keywords: Deep Learning - General
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A Natural Lottery Ticket Winner: Reinforcement Learning with Ordinary Neural Circuits
Ramin Hasani • Mathias Lechner • Alexander Amini • Daniela Rus • Radu Grosu
Keywords: Deep Learning - General • Applications - Neuroscience, Cognitive Science, Biology and Health • Applications - Other
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Bayesian Sparsification of Deep C-valued Networks
Ivan Nazarov • Evgeny Burnaev
Keywords: Deep Learning - General
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Combining Differentiable PDE Solvers and Graph Neural Networks for Fluid Flow Prediction
Filipe de Avila Belbute-Peres • Thomas D. Economon • Zico Kolter
Keywords: Deep Learning - General • Applications - Other • Deep Learning - General
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SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates
Lingkai Kong • Jimeng Sun • Chao Zhang
Keywords: Deep Learning - General • Fairness, Equity, Justice, and Safety
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On Learning Sets of Symmetric Elements
Haggai Maron • Or Litany • Gal Chechik • Ethan Fetaya
Keywords: Deep Learning - General • Set learning
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Revisiting Spatial Invariance with Low-Rank Local Connectivity
Gamaleldin F. Elsayed • Prajit Ramachandran • Jonathon Shlens • Simon Kornblith
Keywords: Deep Learning - General • Applications - Computer Vision • Applications - Neuroscience, Cognitive Science, Biology and Health • Representation Learning
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Angular Visual Hardness
Beidi Chen • Weiyang Liu • Zhiding Yu • Jan Kautz • Anshumali Shrivastava • Animesh Garg • Animashree Anandkumar
Keywords: Deep Learning - General • Applications - Computer Vision • Deep Learning - General
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On Layer Normalization in the Transformer Architecture
Ruibin Xiong • Yunchang Yang • Di He • Kai Zheng • Shuxin Zheng • Chen Xing • Huishuai Zhang • Yanyan Lan • Liwei Wang • Tieyan Liu
Keywords: Deep Learning - General • Deep Learning - General
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Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data
Marc Finzi • Samuel Stanton • Pavel Izmailov • Andrew Gordon Gordon Wilson
Keywords: Deep Learning - General • Applications - Computer Vision • Deep Learning - General • Sequential, Network, and Time-Series Modeling
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Deep Learning - Generative Models and Autoencoders
Learning Flat Latent Manifolds with VAEs
Nutan Chen • Alexej Klushyn • Francesco Ferroni • Justin Bayer • Patrick van der Smagt
Keywords: Deep Learning - Generative Models and Autoencoders • Representation Learning
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PoKED: A Semi-Supervised System for Word Sense Disambiguation
Feng Wei
Keywords: Deep Learning - Generative Models and Autoencoders • Applications - Other • Applications - Language, Speech and Dialog • Unsupervised and Semi-Supervised Learning
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Deep Gaussian Markov Random Fields
Per Sidén • Fredrik Lindsten
Keywords: Deep Learning - Generative Models and Autoencoders • Spatial Models
PDF Bib Video Supplement
Educating Text Autoencoders: Latent Representation Guidance via Denoising
Tianxiao Shen • Jonas Mueller • Dr.Regina Barzilay • Tommi Jaakkola
Keywords: Deep Learning - Generative Models and Autoencoders • Applications - Language, Speech and Dialog • Deep Learning - Generative Models and Autoencoders • Representation Learning
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Latent Variable Modelling with Hyperbolic Normalizing Flows
Joey Bose • Ariella Smofsky • Renjie Liao • Prakash Panangaden • Will Hamilton
Keywords: Deep Learning - Generative Models and Autoencoders • Representation Learning • Probabilistic Inference - Models and Probabilistic Programming
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Improving Molecular Design by Stochastic Iterative Target Augmentation
Kevin Yang • Wengong Jin • Kyle Swanson • Dr.Regina Barzilay • Tommi Jaakkola
Keywords: Deep Learning - Generative Models and Autoencoders • Computational Chemistry
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Reliable Fidelity and Diversity Metrics for Generative Models
Muhammad Ferjad Naeem • Seong Joon Oh • Yunjey Choi • Youngjung Uh • Jaejun Yoo
Keywords: Deep Learning - Generative Models and Autoencoders • Applications - Computer Vision • Probabilistic Inference - Models and Probabilistic Programming
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Convolutional dictionary learning based auto-encoders for natural exponential-family distributions
Bahareh Tolooshams • Andrew Song • Simona Temereanca • Demba Ba
Keywords: Deep Learning - Generative Models and Autoencoders • Deep Learning - General • Supervised Learning • Optimization - General
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Variational Autoencoders with Riemannian Brownian Motion Priors
Dimitris Kalatzis • David Eklund • Georgios Arvanitidis • Søren Hauberg
Keywords: Deep Learning - Generative Models and Autoencoders • Representation Learning • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods • Probabilistic Inference - Models and Probabilistic Programming
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Fair Generative Modeling via Weak Supervision
Kristy Choi • Aditya Grover • Trisha Singh • Rui Shu • Stefano Ermon
Keywords: Deep Learning - Generative Models and Autoencoders • Fairness, Equity, Justice, and Safety
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Associative Memory in Iterated Overparameterized Sigmoid Autoencoders
Yibo Jiang • Cengiz Pehlevan
Keywords: Deep Learning - Generative Models and Autoencoders • Applications - Neuroscience, Cognitive Science, Biology and Health • Deep Learning - Theory • Supervised Learning
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Generative Pretraining From Pixels
Mark Chen • Alec Radford • Rewon Child • Jeffrey Wu • Heewoo Jun • David Luan • Ilya Sutskever
Keywords: Deep Learning - Generative Models and Autoencoders • Sequential, Network, and Time-Series Modeling • Representation Learning • Unsupervised and Semi-Supervised Learning
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Tails of Lipschitz Triangular Flows
Priyank Jaini • Ivan Kobyzev • Yaoliang Yu • Marcus Brubaker
Keywords: Deep Learning - Generative Models and Autoencoders • Unsupervised and Semi-Supervised Learning
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Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities
Jonas Köhler • Leon Klein • Frank Noe
Keywords: Deep Learning - Generative Models and Autoencoders • Deep Learning - General • Unsupervised and Semi-Supervised Learning • Probabilistic Inference - Models and Probabilistic Programming
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Improving Generative Imagination in Object-Centric World Models
Zhixuan Lin • Yi-Fu Wu • Skand Peri • Bofeng Fu • Jindong Jiang • Sungjin Ahn
Keywords: Deep Learning - Generative Models and Autoencoders • Sequential, Network, and Time-Series Modeling • Representation Learning • Unsupervised and Semi-Supervised Learning
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Predictive Sampling with Forecasting Autoregressive Models
Auke Wiggers • Emiel Hoogeboom
Keywords: Deep Learning - Generative Models and Autoencoders • Unsupervised and Semi-Supervised Learning
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Low Bias Low Variance Gradient Estimates for Hierarchical Boolean Stochastic Networks
Adeel Pervez • Taco Cohen • Efstratios Gavves
Keywords: Deep Learning - Generative Models and Autoencoders
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Variable Skipping for Autoregressive Range Density Estimation
Eric Liang • Zongheng Yang • Ion Stoica • Pieter Abbeel • Yan Duan • Peter Chen
Keywords: Deep Learning - Generative Models and Autoencoders • Applications - Other • Sequential, Network, and Time-Series Modeling • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods
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Learning Calibratable Policies using Programmatic Style-Consistency
Eric Zhan • Albert Tseng • Yisong Yue • Adith Swaminathan • Matthew Hausknecht
Keywords: Deep Learning - Generative Models and Autoencoders
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PolyGen: An Autoregressive Generative Model of 3D Meshes
Charlie Nash • Yaroslav Ganin • S. M. Ali Eslami • Peter Battaglia
Keywords: Deep Learning - Generative Models and Autoencoders • Applications - Computer Vision • Sequential, Network, and Time-Series Modeling • Sequential, Network, and Time-Series Modeling
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A Chance-Constrained Generative Framework for Sequence Optimization
Xianggen Liu • Qiang Liu • Sen Song • Jian Peng
Keywords: Deep Learning - Generative Models and Autoencoders • Sequential, Network, and Time-Series Modeling • Deep Learning - General
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Multi-Objective Molecule Generation using Interpretable Substructures
Wengong Jin • Dr.Regina Barzilay • Tommi Jaakkola
Keywords: Deep Learning - Generative Models and Autoencoders • Computational Chemistry
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On Implicit Regularization in $\beta$-VAEs
Abhishek Kumar • Ben Poole
Keywords: Deep Learning - Generative Models and Autoencoders • Deep Learning - Generative Models and Autoencoders
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Hypernetwork approach to generating point clouds
Przemysław Spurek • Sebastian Winczowski • Jacek Tabor • Maciej Zamorski • Maciej Zieba • Tomasz Trzcinski
Keywords: Deep Learning - Generative Models and Autoencoders • Deep Learning - Generative Models and Autoencoders
PDF Bib Video
Scalable Deep Generative Modeling for Sparse Graphs
Hanjun Dai • Azade Nazi • Yujia Li • Bo Dai • Dale Schuurmans
Keywords: Deep Learning - Generative Models and Autoencoders • Applications - Other • Sequential, Network, and Time-Series Modeling
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Learning from Irregularly-Sampled Time Series: A Missing Data Perspective
Steven Cheng-Xian Li • Benjamin Marlin
Keywords: Deep Learning - Generative Models and Autoencoders • Deep Learning - General • Sequential, Network, and Time-Series Modeling
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Semi-Supervised Learning with Normalizing Flows
Pavel Izmailov • Polina Kirichenko • Marc Finzi • Andrew Gordon Gordon Wilson
Keywords: Deep Learning - Generative Models and Autoencoders • Unsupervised and Semi-Supervised Learning • Probabilistic Inference - Models and Probabilistic Programming
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ControlVAE: Controllable Variational Autoencoder
Huajie Shao • Shuochao Yao • Dachun Sun • Aston Zhang • Shengzhong Liu • Dongxin Liu • Jun Wang • Tarek Abdelzaher
Keywords: Deep Learning - Generative Models and Autoencoders • Deep Learning - Generative Models and Autoencoders • Probabilistic Inference - Models and Probabilistic Programming
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Distribution Augmentation for Generative Modeling
Heewoo Jun • Rewon Child • Mark Chen • John Schulman • Aditya Ramesh • Alec Radford • Ilya Sutskever
Keywords: Deep Learning - Generative Models and Autoencoders • Sequential, Network, and Time-Series Modeling
PDF Bib Video
ACFlow: Flow Models for Arbitrary Conditional Likelihoods
Yang Li • Shoaib Akbar • Junier Oliva
Keywords: Deep Learning - Generative Models and Autoencoders • Unsupervised and Semi-Supervised Learning • Probabilistic Inference - Models and Probabilistic Programming
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Latent Space Factorisation and Manipulation via Matrix Subspace Projection
Xiao Li • Chenghua Lin • Ruizhe Li • Chaozheng Wang • Frank Guerin
Keywords: Deep Learning - Generative Models and Autoencoders • Applications - Computer Vision • General Machine Learning Techniques • Representation Learning
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Generative Flows with Matrix Exponential
Changyi Xiao • Ligang Liu
Keywords: Deep Learning - Generative Models and Autoencoders • Unsupervised and Semi-Supervised Learning
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AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation
Jae Hyun Lim • Aaron Courville • Christopher Pal • Chin-Wei Huang
Keywords: Deep Learning - Generative Models and Autoencoders • Deep Learning - General • Reinforcement Learning - General • Learning Theory
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Multilinear Latent Conditioning for Generating Unseen Attribute Combinations
Markos Georgopoulos • Grigorios Chrysos • Maja Pantic • Yannis Panagakis
Keywords: Deep Learning - Generative Models and Autoencoders • General Machine Learning Techniques
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Latent Bernoulli Autoencoder
Jiri Fajtl • Vasileios Argyriou • Dorothy Monekosso • Paolo Remagnino
Keywords: Deep Learning - Generative Models and Autoencoders • Applications - Computer Vision • Deep Learning - General • Deep Learning - Generative Models and Autoencoders
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Doubly Stochastic Variational Inference for Neural Processes with Hierarchical Latent Variables
Qi Wang • Herke van Hoof
Keywords: Deep Learning - Generative Models and Autoencoders • Deep Learning - General
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Evaluating Lossy Compression Rates of Deep Generative Models
Sicong Huang • Alireza Makhzani • Yanshuai Cao • Roger B Grosse
Keywords: Deep Learning - Generative Models and Autoencoders
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Normalizing Flows on Tori and Spheres
Danilo Jimenez Rezende • George Papamakarios • Sebastien Racaniere • Michael Albergo • Gurtej Kanwar • Phiala Shanahan • Kyle Cranmer
Keywords: Deep Learning - Generative Models and Autoencoders • Directional Statistics
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Learning Autoencoders with Relational Regularization
Hongteng Xu • Dixin Luo • Ricardo Henao • Svati Shah • Lawrence Carin Duke
Keywords: Deep Learning - Generative Models and Autoencoders • Deep Learning - Algorithms • Deep Learning - Generative Models and Autoencoders
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Perceptual Generative Autoencoders
Zijun Zhang • Ruixiang ZHANG • Zongpeng Li • Yoshua Bengio • Liam Paull
Keywords: Deep Learning - Generative Models and Autoencoders • Deep Learning - Generative Models and Autoencoders
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VFlow: More Expressive Generative Flows with Variational Data Augmentation
Jianfei Chen • Cheng Lu • Biqi Chenli • Jun Zhu • Tian Tian
Keywords: Deep Learning - Generative Models and Autoencoders • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods • Probabilistic Inference - Models and Probabilistic Programming
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Rate-distortion optimization guided autoencoder for isometric embedding in Euclidean latent space
Keizo Kato • Jing Zhou • Tomotake Sasaki • Akira Nakagawa
Keywords: Deep Learning - Generative Models and Autoencoders • Deep Learning - Generative Models and Autoencoders
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Source Separation with Deep Generative Priors
Vivek Jayaram • John Thickstun
Keywords: Deep Learning - Generative Models and Autoencoders • Probabilistic Inference - Models and Probabilistic Programming
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Hierarchical Generation of Molecular Graphs using Structural Motifs
Wengong Jin • Dr.Regina Barzilay • Tommi Jaakkola
Keywords: Deep Learning - Generative Models and Autoencoders • Computational Chemistry
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Bridging the Gap Between f-GANs and Wasserstein GANs
Jiaming Song • Stefano Ermon
Keywords: Deep Learning - Generative Models and Autoencoders • Deep Learning - General • Learning Theory
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Relaxing Bijectivity Constraints with Continuously Indexed Normalising Flows
Rob Cornish • Anthony L Caterini • George Deligiannidis • Arnaud Doucet
Keywords: Deep Learning - Generative Models and Autoencoders • Deep Learning - General • Unsupervised and Semi-Supervised Learning • Probabilistic Inference - Models and Probabilistic Programming
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Semi-Supervised StyleGAN for Disentanglement Learning
Weili Nie • Tero Karras • Animesh Garg • Shoubhik Debnath • Anjul Patney • Ankit B Patel • Animashree Anandkumar
Keywords: Deep Learning - Generative Models and Autoencoders • Deep Learning - General • Representation Learning • Unsupervised and Semi-Supervised Learning
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Training Deep Energy-Based Models with f-Divergence Minimization
Lantao Yu • Yang Song • Jiaming Song • Stefano Ermon
Keywords: Deep Learning - Generative Models and Autoencoders
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Deep Learning - Theory
Frequency Bias in Neural Networks for Input of Non-Uniform Density
Ronen Basri • Meirav Galun • Amnon Geifman • David Jacobs • Yoni Kasten • Shira Kritchman
Keywords: Deep Learning - Theory
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Optimization Theory for ReLU Neural Networks Trained with Normalization Layers
Yonatan Dukler • Quanquan Gu • Guido F Montufar
Keywords: Deep Learning - Theory • Deep Learning - General • Optimization - Non-convex
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Student Specialization in Deep Rectified Networks With Finite Width and Input Dimension
Yuandong Tian
Keywords: Deep Learning - Theory
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Infinite attention: NNGP and NTK for deep attention networks
Jiri Hron • Yasaman Bahri • Jascha Sohl-Dickstein • Roman Novak
Keywords: Deep Learning - Theory • Deep Learning - General • Sequential, Network, and Time-Series Modeling • Gaussian Processes
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Proving the Lottery Ticket Hypothesis: Pruning is All You Need
Eran Malach • Gilad Yehudai • Shai Shalev-Schwartz • Ohad Shamir
Keywords: Deep Learning - Theory • Deep Learning - General • Learning Theory
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The Neural Tangent Kernel in High Dimensions: Triple Descent and a Multi-Scale Theory of Generalization
Ben Adlam • Jeffrey Pennington
Keywords: Deep Learning - Theory
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Dissecting Non-Vacuous Generalization Bounds based on the Mean-Field Approximation
Konstantinos Pitas
Keywords: Deep Learning - Theory • Deep Learning - General
PDF Bib Video Supplement
Unique Properties of Flat Minima in Deep Networks
Rotem Mulayoff • Tomer Michaeli
Keywords: Deep Learning - Theory • Deep Learning - General
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Fractional Underdamped Langevin Dynamics: Retargeting SGD with Momentum under Heavy-Tailed Gradient Noise
Umut Simsekli • Lingjiong Zhu • Yee Whye Teh • Mert Gürbüzbalaban
Keywords: Deep Learning - Theory • Deep Learning - Algorithms
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Information-Theoretic Local Minima Characterization and Regularization
Zhiwei Jia • Hao Su
Keywords: Deep Learning - Theory • Deep Learning - General
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Eliminating the Invariance on the Loss Landscape of Linear Autoencoders
Reza Oftadeh • Jiayi Shen • Zhangyang Wang • Dylan Shell
Keywords: Deep Learning - Theory • Deep Learning - Generative Models and Autoencoders • Deep Learning - General
PDF Bib Video Supplement
Low-loss connection of weight vectors: distribution-based approaches
Ivan Anokhin • Dmitry Yarotsky
Keywords: Deep Learning - Theory • Applications - Other • Supervised Learning
PDF Bib Video Supplement
Constructive Universal High-Dimensional Distribution Generation through Deep ReLU Networks
Dmytro Perekrestenko • Stephan Müller • Helmut Bölcskei
Keywords: Deep Learning - Theory • Learning Theory
PDF Bib Video
Better depth-width trade-offs for neural networks through the lens of dynamical systems
Vaggos Chatziafratis • Sai Ganesh Nagarajan • Ioannis Panageas
Keywords: Deep Learning - Theory
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Dynamics of Deep Neural Networks and Neural Tangent Hierarchy
Jiaoyang Huang • Horng-Tzer Yau
Keywords: Deep Learning - Theory • Deep Learning - General • General Machine Learning Techniques • Optimization - Non-convex
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The Impact of Neural Network Overparameterization on Gradient Confusion and Stochastic Gradient Descent
Karthik Abinav Sankararaman • Soham De • Zheng Xu • W. Ronny Huang • Tom Goldstein
Keywords: Deep Learning - Theory
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Attentive Group Equivariant Convolutional Networks
David W Romero • Erik J Bekkers • Jakub M Tomczak • Mark Hoogendoorn
Keywords: Deep Learning - Theory • Applications - Computer Vision • Deep Learning - General • Deep Learning - General
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Rethinking Bias-Variance Trade-off for Generalization of Neural Networks
Zitong Yang • Yaodong Yu • Chong You • Jacob Steinhardt • Yi Ma
Keywords: Deep Learning - Theory • Learning Theory
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Implicit competitive regularization in GANs
Florian T Schaefer • Hongkai Zheng • Animashree Anandkumar
Keywords: Deep Learning - Theory
PDF Bib Video Supplement
On the Number of Linear Regions of Convolutional Neural Networks
Huan Xiong • Lei Huang • Mengyang Yu • Li Liu • Fan Zhu • Ling Shao
Keywords: Deep Learning - Theory
PDF Bib Video Supplement
Topologically Densified Distributions
Christoph Hofer • Florian Graf • Marc Niethammer • Roland Kwitt
Keywords: Deep Learning - Theory • Representation Learning • Learning Theory • Learning Theory
PDF Bib Video Supplement
Generalization and Representational Limits of Graph Neural Networks
Vikas K Garg • Stefanie Jegelka • Tommi Jaakkola
Keywords: Deep Learning - Theory • Applications - Other
PDF Bib Video Supplement
The Implicit and Explicit Regularization Effects of Dropout
Colin Wei • Sham Kakade • Tengyu Ma
Keywords: Deep Learning - Theory • Learning Theory
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A Mean Field Analysis Of Deep ResNet And Beyond: Towards Provably Optimization Via Overparameterization From Depth
Yiping Lu • Chao Ma • Yulong Lu • Jianfeng Lu • Lexing Ying
Keywords: Deep Learning - Theory • Deep Learning - General
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Beyond Signal Propagation: Is Feature Diversity Necessary in Deep Neural Network Initialization?
Yaniv Blumenfeld • Dar Gilboa • Daniel Soudry
Keywords: Deep Learning - Theory
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Optimal Continual Learning has Perfect Memory and is NP-hard
Jeremias Knoblauch • Hisham Husain • Tom Diethe
Keywords: Deep Learning - Theory • General Machine Learning Techniques • Sequential, Network, and Time-Series Modeling
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Double Trouble in Double Descent: Bias and Variance(s) in the Lazy Regime
Stéphane d'Ascoli • Maria Refinetti • Giulio Biroli • Florent Krzakala
Keywords: Deep Learning - Theory • Supervised Learning • General Machine Learning Techniques • Learning Theory
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Random Matrix Theory Proves that Deep Learning Representations of GAN-data Behave as Gaussian Mixtures
Mohamed El Amine Seddik • Cosme Louart • Mohamed Tamaazousti • Romain COUILLET
Keywords: Deep Learning - Theory • General Machine Learning Techniques
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A Sample Complexity Separation between Non-Convex and Convex Meta-Learning
Nikunj Saunshi • Yi Zhang • Mikhail Khodak • Sanjeev Arora
Keywords: Deep Learning - Theory • Transfer, Multitask and Meta-learning • Representation Learning
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Towards a General Theory of Infinite-Width Limits of Neural Classifiers
Eugene A. Golikov
Keywords: Deep Learning - Theory • Learning Theory
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Disentangling Trainability and Generalization in Deep Neural Networks
Lechao Xiao • Jeffrey Pennington • Samuel S Schoenholz
Keywords: Deep Learning - Theory • Deep Learning - General • General Machine Learning Techniques • Gaussian Processes
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Deep Molecular Programming: A Natural Implementation of Binary-Weight ReLU Neural Networks
Marko Vasic • Cameron Chalk • Sarfraz Khurshid • David Soloveichik
Keywords: Deep Learning - Theory • Applications - Neuroscience, Cognitive Science, Biology and Health • Deep Learning - General
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Landscape Connectivity and Dropout Stability of SGD Solutions for Over-parameterized Neural Networks
Alexander Shevchenko • Marco Mondelli
Keywords: Deep Learning - Theory • Learning Theory
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Maximum-and-Concatenation Networks
Xingyu Xie • Hao Kong • Jianlong Wu • Wayne Zhang • Guangcan Liu • Zhouchen Lin
Keywords: Deep Learning - Theory • Deep Learning - General
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Low-Rank Bottleneck in Multi-head Attention Models
Srinadh Bhojanapalli • Chulhee Yun • Ankit Singh Rawat • Sashank Reddi • Sanjiv Kumar
Keywords: Deep Learning - Theory • Deep Learning - General
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Fairness, Equity, Justice, and Safety
Detecting Out-of-Distribution Examples with Gram Matrices
Chandramouli Shama Sastry • Sageev Oore
Keywords: Fairness, Equity, Justice, and Safety • Out of distribution detection
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Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing
Sanghamitra Dutta • Dennis Wei • Hazar Yueksel • Pin-Yu Chen • Sijia Liu • Kush R. Varshney
Keywords: Fairness, Equity, Justice, and Safety • Learning Theory
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Neural Network Control Policy Verification With Persistent Adversarial Perturbation
Yuh-shyang Wang • Lily Weng • Luca Daniel
Keywords: Fairness, Equity, Justice, and Safety • Planning, Control, and Multiagent Learning
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Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning
Esther Rolf • Max Simchowitz • Sarah Dean • Lydia T. Liu • Daniel Bjorkegren • Moritz Hardt • Joshua Blumenstock
Keywords: Fairness, Equity, Justice, and Safety • Applications - Other
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FR-Train: A Mutual Information-Based Approach to Fair and Robust Training
Yuji Roh • Kangwook Lee • Steven Whang • Changho Suh
Keywords: Fairness, Equity, Justice, and Safety • Applications -> Crowdsourcing • Deep Learning - General • Trustworthy Machine Learning
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Fair Learning with Private Demographic Data
Hussein Mozannar • Mesrob Ohannessian • Nathan Srebro
Keywords: Fairness, Equity, Justice, and Safety • Privacy-preserving Statistics and Machine Learning
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Feature Noise Induces Loss Discrepancy Across Groups
Fereshte Khani • Percy Liang
Keywords: Fairness, Equity, Justice, and Safety • Accountability, Transparency and Interpretability • Trustworthy Machine Learning
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Too Relaxed to Be Fair
Michael Lohaus • Michaël Perrot • Ulrike von Luxburg
Keywords: Fairness, Equity, Justice, and Safety • Supervised Learning
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Fair k-Centers via Maximum Matching
Matthew D Jones • Huy Nguyen • Thy D Nguyen
Keywords: Fairness, Equity, Justice, and Safety • Unsupervised and Semi-Supervised Learning
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Measuring Non-Expert Comprehension of Machine Learning Fairness Metrics
Debjani Saha • Candice Schumann • Duncan C McElfresh • John P Dickerson • Michelle Mazurek • Michael Tschantz
Keywords: Fairness, Equity, Justice, and Safety • Applications - Other • Accountability, Transparency and Interpretability
PDF Bib Video Supplement
Collaborative Machine Learning with Incentive-Aware Model Rewards
Rachael Hwee Ling Sim • Yehong Zhang • Mun Choon Chan • Bryan Kian Hsiang Low
Keywords: Fairness, Equity, Justice, and Safety • Learning Theory
PDF Bib Video Supplement
Bounding the fairness and accuracy of classifiers from population statistics
Sivan Sabato • Elad Yom-Tov
Keywords: Fairness, Equity, Justice, and Safety
PDF Bib Video Supplement
Causal Modeling for Fairness In Dynamical Systems
Elliot Creager • David Madras • Toniann Pitassi • Richard Zemel
Keywords: Fairness, Equity, Justice, and Safety • Causality
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How to Solve Fair k-Center in Massive Data Models
Ashish Chiplunkar • Sagar Kale • Sivaramakrishnan Natarajan Ramamoorthy
Keywords: Fairness, Equity, Justice, and Safety • Unsupervised and Semi-Supervised Learning • Optimization - General • Optimization - Large Scale, Parallel and Distributed
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FACT: A Diagnostic for Group Fairness Trade-offs
Joon Sik Kim • Jiahao Chen • Ameet Talwalkar
Keywords: Fairness, Equity, Justice, and Safety • Applications - Other
PDF Bib Video Supplement
Minimax Pareto Fairness: A Multi Objective Perspective
Natalia L Martinez • Martin A Bertran • Guillermo Sapiro
Keywords: Fairness, Equity, Justice, and Safety
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Data preprocessing to mitigate bias: A maximum entropy based approach
L. Elisa Celis • Vijay Keswani • Nisheeth K. Vishnoi
Keywords: Fairness, Equity, Justice, and Safety • Trustworthy Machine Learning • Trustworthy Machine Learning
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Two Simple Ways to Learn Individual Fairness Metrics from Data
Debarghya Mukherjee • Mikhail Yurochkin • Moulinath Banerjee • Yuekai Sun
Keywords: Fairness, Equity, Justice, and Safety • General Machine Learning Techniques
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DeBayes: a Bayesian Method for Debiasing Network Embeddings
Maarten Buyl • Tijl De Bie
Keywords: Fairness, Equity, Justice, and Safety • Sequential, Network, and Time-Series Modeling • Representation Learning
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Gaussian Processes
Differentiable Likelihoods for Fast Inversion of 'Likelihood-Free' Dynamical Systems
Hans Kersting • Nicholas Krämer • Martin Schiegg • Christian Daniel • Michael Tiemann • Philipp Hennig
Keywords: Gaussian Processes • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods • Probabilistic Inference - Models and Probabilistic Programming • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods
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Projective Preferential Bayesian Optimization
Petrus Mikkola • Milica Todorovic • Jari Järvi • Patrick Rinke • Samuel Kaski
Keywords: Gaussian Processes • Supervised Learning • Probabilistic Inference - Models and Probabilistic Programming
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Healing Products of Gaussian Process Experts
samuel cohen • Rendani Mbuvha • Tshilidzi Marwala • Marc Deisenroth
Keywords: Gaussian Processes • Probabilistic Inference - Models and Probabilistic Programming
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State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes
William Wilkinson • Paul Chang • Michael Andersen • Arno Solin
Keywords: Gaussian Processes
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Randomly Projected Additive Gaussian Processes for Regression
Ian A Delbridge • David Bindel • Andrew Gordon Gordon Wilson
Keywords: Gaussian Processes • General Machine Learning Techniques
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A quantile-based approach for hyperparameter transfer learning
David Salinas • Huibin Shen • Valerio Perrone
Keywords: Gaussian Processes • Transfer, Multitask and Meta-learning • Transfer, Multitask and Meta-learning
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Bayesian Optimisation over Multiple Continuous and Categorical Inputs
Binxin Ru • Ahsan Alvi • Vu Nguyen • Michael A. Osborne • Stephen Roberts
Keywords: Gaussian Processes
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Modulating Surrogates for Bayesian Optimization
Erik Bodin • Markus Kaiser • Ieva Kazlauskaite • Zhenwen Dai • Neill Campbell • Carl Henrik Ek
Keywords: Gaussian Processes • Bayesian Optimization
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Knowing The What But Not The Where in Bayesian Optimization
Vu Nguyen • Michael A. Osborne
Keywords: Gaussian Processes
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Efficiently sampling functions from Gaussian process posteriors
James Wilson • Viacheslav Borovitskiy • Alexander Terenin • Peter Mostowsky • Marc Deisenroth
Keywords: Gaussian Processes
PDF Bib Video Supplement
Scalable Exact Inference in Multi-Output Gaussian Processes
Wessel P Bruinsma • Eric Perim • William Tebbutt • Scott Hosking • Arno Solin • Richard E. Turner
Keywords: Gaussian Processes
PDF Bib Video Supplement
Inter-domain Deep Gaussian Processes
Tim G. J. Rudner • Dino Sejdinovic • Yarin Gal
Keywords: Gaussian Processes • Probabilistic Inference - Models and Probabilistic Programming
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R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games
Zhongxiang Dai • Yizhou Chen • Bryan Kian Hsiang Low • Patrick Jaillet • Teck-Hua Ho
Keywords: Gaussian Processes • Online Learning, Active Learning, and Bandits
PDF Bib Video Supplement
BINOCULARS for efficient, nonmyopic sequential experimental design
Shali Jiang • Henry R Chai • Javier Gonzalez • Roman Garnett
Keywords: Gaussian Processes • Online Learning, Active Learning, and Bandits • Probabilistic Inference - Models and Probabilistic Programming
PDF Bib Video Supplement
Scalable Gaussian Process Separation for Kernels with a Non-Stationary Phase
Jan Graßhoff • Alexandra Jankowski • Philipp Rostalski
Keywords: Gaussian Processes • Sequential, Network, and Time-Series Modeling
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Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances
Csaba Toth • Harald Oberhauser
Keywords: Gaussian Processes • Sequential, Network, and Time-Series Modeling • Sequential, Network, and Time-Series Modeling • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods
PDF Bib Video Supplement
Sparse Gaussian Processes with Spherical Harmonic Features
Vincent Dutordoir • Nicolas Durrande • James Hensman
Keywords: Gaussian Processes • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods
PDF Bib Video Supplement
Private Outsourced Bayesian Optimization
Dmitrii Kharkovskii • Zhongxiang Dai • Bryan Kian Hsiang Low
Keywords: Gaussian Processes • Online Learning, Active Learning, and Bandits
PDF Bib Video Supplement
Parametric Gaussian Process Regressors
Martin Jankowiak • Geoff Pleiss • Jacob R. Gardner
Keywords: Gaussian Processes
PDF Bib Video Supplement
General Machine Learning Techniques
Partial Trace Regression and Low-Rank Kraus Decomposition
Hachem Kadri • Stephane Ayache • Riikka Huusari • Alain Rakotomamonjy • Ralaivola Liva
Keywords: General Machine Learning Techniques • Supervised Learning
PDF Bib Video Supplement
Nonparametric Score Estimators
Yuhao Zhou • Jiaxin Shi • Jun Zhu
Keywords: General Machine Learning Techniques • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods
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An end-to-end approach for the verification problem: learning the right distance
Joao B Monteiro • Isabela Albuquerque • Jahangir Alam • R Devon Hjelm • Tiago H Falk
Keywords: General Machine Learning Techniques • Applications - Language, Speech and Dialog • Deep Learning - Algorithms • Supervised Learning
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Polynomial Tensor Sketch for Element-wise Function of Low-Rank Matrix
Insu Han • Haim Avron • Jinwoo Shin
Keywords: General Machine Learning Techniques • Unsupervised and Semi-Supervised Learning • General Machine Learning Techniques • Optimization - General
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Duality in RKHSs with Infinite Dimensional Outputs: Application to Robust Losses
Pierre Laforgue • Alex Lambert • Luc Brogat-Motte • Florence d'Alché-Buc
Keywords: General Machine Learning Techniques • Sequential, Network, and Time-Series Modeling • Optimization - Convex
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Near Input Sparsity Time Kernel Embeddings via Adaptive Sampling
David Woodruff • Amir Zandieh
Keywords: General Machine Learning Techniques • Unsupervised and Semi-Supervised Learning • General Machine Learning Techniques
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A Swiss Army Knife for Minimax Optimal Transport
Sofien Dhouib • Ievgen Redko • Tanguy Kerdoncuff • Rémi Emonet • Marc Sebban
Keywords: General Machine Learning Techniques • Optimal Transport
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DeltaGrad: Rapid retraining of machine learning models
Yinjun Wu • Edgar Dobriban • Susan Davidson
Keywords: General Machine Learning Techniques • Applications - Other • Optimization - General • Trustworthy Machine Learning
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Regularized Optimal Transport is Ground Cost Adversarial
François-Pierre Paty • Marco Cuturi
Keywords: General Machine Learning Techniques • Optimal transport
PDF Bib Video
Implicit Regularization of Random Feature Models
Arthur Jacot • Berfin Simsek • Francesco Spadaro • Clément Hongler • Franck Gabriel
Keywords: General Machine Learning Techniques • Learning Theory • Gaussian Processes
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On Efficient Constructions of Checkpoints
Yu Chen • Zhenming LIU • Bin Ren • Xin Jin
Keywords: General Machine Learning Techniques
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Neural Kernels Without Tangents
Vaishaal Shankar • Alex Fang • Wenshuo Guo • Sara Fridovich-Keil • Jonathan Ragan-Kelley • Ludwig Schmidt • Benjamin Recht
Keywords: General Machine Learning Techniques
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Learning Similarity Metrics for Numerical Simulations
Georg Kohl • Kiwon Um • Nils Thuerey
Keywords: General Machine Learning Techniques • Physical simulation
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Non-separable Non-stationary random fields
Kangrui Wang • Oliver A Hamelijnck • Theodoros Damoulas • Mark Steel
Keywords: General Machine Learning Techniques • Probabilistic Inference - Models and Probabilistic Programming • Gaussian Processes
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Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks
Blake A Bordelon • Abdulkadir Canatar • Cengiz Pehlevan
Keywords: General Machine Learning Techniques • Statistical Mechanics of Machine Learning
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FedBoost: A Communication-Efficient Algorithm for Federated Learning
Jenny Hamer • Mehryar Mohri • Ananda Theertha Suresh
Keywords: General Machine Learning Techniques • Supervised Learning • Learning Theory
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Graph-based Nearest Neighbor Search: From Practice to Theory
Liudmila Prokhorenkova • Aleksandr Shekhovtsov
Keywords: General Machine Learning Techniques
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Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to-Event Data
Tamara Fernandez • Arthur Gretton • Nicolas Rivera • Wenkai Xu
Keywords: General Machine Learning Techniques
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Representing Unordered Data Using Complex-Weighted Multiset Automata
Justin DeBenedetto • David Chiang
Keywords: General Machine Learning Techniques • Deep Learning - General • Sequential, Network, and Time-Series Modeling • Optimization - General
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Task Understanding from Confusing Multi-task Data
Xin Su • Yizhou Jiang • Shangqi Guo • Feng Chen
Keywords: General Machine Learning Techniques • Supervised Learning • Transfer, Multitask and Meta-learning
PDF Bib Video Supplement
Sub-linear Memory Sketches for Near Neighbor Search on Streaming Data
Benjamin Coleman • Richard Baraniuk • Anshumali Shrivastava
Keywords: General Machine Learning Techniques • Algorithms
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Ordinal Non-negative Matrix Factorization for Recommendation
Olivier Gouvert • Thomas Oberlin • Cédric Févotte
Keywords: General Machine Learning Techniques • Applications - Other • Probabilistic Inference - Models and Probabilistic Programming
PDF Bib Video
Multiresolution Tensor Learning for Efficient and Interpretable Spatial Analysis
Jung Yeon Park • Kenneth Carr • Stephan Zheng • Yisong Yue • Rose Yu
Keywords: General Machine Learning Techniques • Applications - Other • Optimization - Large Scale, Parallel and Distributed • Optimization - General
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Median Matrix Completion: from Embarrassment to Optimality
Weidong Liu • Xiaojun Mao • Raymond K. W. Wong
Keywords: General Machine Learning Techniques • Applications - Other • Learning Theory • Trustworthy Machine Learning
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A Geometric Approach to Archetypal Analysis via Sparse Projections
Vinayak Abrol • Pulkit Sharma
Keywords: General Machine Learning Techniques • Applications - Computer Vision • Unsupervised and Semi-Supervised Learning
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Self-Modulating Nonparametric Event-Tensor Factorization
Zheng Wang • Xinqi Chu • Shandian Zhe
Keywords: General Machine Learning Techniques • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods • Probabilistic Inference - Models and Probabilistic Programming • Gaussian Processes
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Generalization Guarantees for Sparse Kernel Approximation with Entropic Optimal Features
Liang Ding • Rui Tuo • Shahin Shahrampour
Keywords: General Machine Learning Techniques
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Supervised Quantile Normalization for Low Rank Matrix Factorization
Marco Cuturi • Olivier Teboul • Jonathan Niles-Weed • Jean-Philippe Vert
Keywords: General Machine Learning Techniques • Applications - Neuroscience, Cognitive Science, Biology and Health • Unsupervised and Semi-Supervised Learning
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Evolutionary Topology Search for Tensor Network Decomposition
Chao Li • Zhun Sun
Keywords: General Machine Learning Techniques • Representation Learning
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Learning Deep Kernels for Non-Parametric Two-Sample Tests
Feng Liu • Wenkai Xu • Jie Lu • Guangquan Zhang • Arthur Gretton • D. J. Sutherland
Keywords: General Machine Learning Techniques • Deep Learning - Algorithms • Supervised Learning
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Online Multi-Kernel Learning with Graph-Structured Feedback
Pouya M Ghari • Yanning Shen
Keywords: General Machine Learning Techniques
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Streaming Coresets for Symmetric Tensor Factorization
Supratim Shit • Rachit Chhaya • Jayesh Choudhari • Anirban Dasgupta
Keywords: General Machine Learning Techniques • Online / Streaming Algorithm
PDF Bib Video Supplement
Kernel interpolation with continuous volume sampling
Ayoub Belhadji • Rémi Bardenet • Pierre Chainais
Keywords: General Machine Learning Techniques • Learning Theory • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods
PDF Bib Video Supplement
Tensor denoising and completion based on ordinal observations
Chanwoo Lee • Miaoyan Wang
Keywords: General Machine Learning Techniques • Learning Theory
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Learning Task-Agnostic Embedding of Multiple Black-Box Experts for Multi-Task Model Fusion
Nghia Hoang • Thanh Chi Lam • Bryan Kian Hsiang Low • Patrick Jaillet
Keywords: General Machine Learning Techniques • Probabilistic Inference - Models and Probabilistic Programming
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Convolutional Kernel Networks for Graph-Structured Data
Dexiong Chen • Laurent Jacob • Julien Mairal
Keywords: General Machine Learning Techniques • Supervised Learning
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Deep Divergence Learning
Hatice Kubra Cilingir • Rachel Manzelli • Brian Kulis
Keywords: General Machine Learning Techniques • Deep Learning - Algorithms • Unsupervised and Semi-Supervised Learning
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Consistent Estimators for Learning to Defer to an Expert
Hussein Mozannar • David Sontag
Keywords: General Machine Learning Techniques • Trustworthy Machine Learning
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Harmonic Decompositions of Convolutional Networks
Meyer Scetbon • Zaid Harchaoui
Keywords: General Machine Learning Techniques • Deep Learning - Theory • Learning Theory
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Learning Theory
The Intrinsic Robustness of Stochastic Bandits to Strategic Manipulation
Zhe Feng • David Parkes • Haifeng Xu
Keywords: Learning Theory • Online Learning, Active Learning, and Bandits
PDF Bib Video Supplement
Decentralised Learning with Random Features and Distributed Gradient Descent
Dominic Richards • Patrick Rebeschini • Lorenzo Rosasco
Keywords: Learning Theory • Learning Theory
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Sharp Statistical Guaratees for Adversarially Robust Gaussian Classification
Chen Dan • Yuting Wei • Pradeep Ravikumar
Keywords: Learning Theory • Adversarial Examples
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Stochastic Regret Minimization in Extensive-Form Games
Gabriele Farina • Christian Kroer • Tuomas Sandholm
Keywords: Learning Theory
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Recht-Re Noncommutative Arithmetic-Geometric Mean Conjecture is False
Zehua Lai • Lek-Heng Lim
Keywords: Learning Theory • General Machine Learning Techniques • Optimization - General
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Optimal Estimator for Unlabeled Linear Regression
hang zhang • Ping Li
Keywords: Learning Theory • Optimization - General
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Reserve Pricing in Repeated Second-Price Auctions with Strategic Bidders
Alexey Drutsa
Keywords: Learning Theory • Online Learning, Active Learning, and Bandits • Online Learning, Active Learning, and Bandits
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On Unbalanced Optimal Transport: An Analysis of Sinkhorn Algorithm
Khiem D Pham • Khang Le • Nhat Ho • Tung Pham • Hung Bui
Keywords: Learning Theory
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Optimal Non-parametric Learning in Repeated Contextual Auctions with Strategic Buyer
Alexey Drutsa
Keywords: Learning Theory • Online Learning, Active Learning, and Bandits • Online Learning, Active Learning, and Bandits
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Expert Learning through Generalized Inverse Multiobjective Optimization: Models, Insights, and Algorithms
Chaosheng Dong • Bo Zeng
Keywords: Learning Theory • Unsupervised and Semi-Supervised Learning • Unsupervised and Semi-Supervised Learning • Unsupervised and Semi-Supervised Learning
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Principled learning method for Wasserstein distributionally robust optimization with local perturbations
Yongchan Kwon • Wonyoung Kim • Joong-Ho Won • Myunghee Cho Paik
Keywords: Learning Theory • Distributionally robust optimization
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Strength from Weakness: Fast Learning Using Weak Supervision
Joshua D Robinson • Stefanie Jegelka • Suvrit Sra
Keywords: Learning Theory • Transfer, Multitask and Meta-learning • Learning Theory • Learning Theory
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Efficiently Learning Adversarially Robust Halfspaces with Noise
Omar Montasser • Surbhi Goel • Ilias Diakonikolas • Nathan Srebro
Keywords: Learning Theory • Learning Theory • Adversarial Examples
PDF Bib Video
Generalisation error in learning with random features and the hidden manifold model
Federica Gerace • Bruno Loureiro • Florent Krzakala • Marc Mézard • Lenka Zdeborová
Keywords: Learning Theory • statistical physics for learning
PDF Bib Video Supplement
Estimating the Number and Effect Sizes of Non-null Hypotheses
Jennifer R Brennan • Ramya Korlakai Vinayak • Kevin Jamieson
Keywords: Learning Theory
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Gradient-free Online Learning in Continuous Games with Delayed Rewards
Amélie Héliou • Panayotis Mertikopoulos • Zhengyuan Zhou
Keywords: Learning Theory • Online Learning, Active Learning, and Bandits • Online Learning, Active Learning, and Bandits
PDF Bib Video Supplement
On the consistency of top-k surrogate losses
Forest Yang • Sanmi Koyejo
Keywords: Learning Theory
PDF Bib Video Supplement
Logistic Regression for Massive Data with Rare Events
HaiYing Wang
Keywords: Learning Theory • Learning Theory • Learning Theory • Optimization - Large Scale, Parallel and Distributed
PDF Bib Video Supplement
Interpretable, Multidimensional, Multimodal Anomaly Detection with Negative Sampling for Detection of Device Failure
John Sipple
Keywords: Learning Theory • Applications - Other • Deep Learning - Algorithms • Supervised Learning
PDF Bib Video
Piecewise Linear Regression via a Difference of Convex Functions
Ali Siahkamari • Aditya Gangrade • Brian Kulis • Venkatesh Saligrama
Keywords: Learning Theory
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Robust Pricing in Dynamic Mechanism Design
Yuan Deng • Sébastien Lahaie • Vahab Mirrokni
Keywords: Learning Theory
PDF Bib Video
On a projective ensemble approach to two sample test for equality of distributions
Zhimei Li • Yaowu Zhang
Keywords: Learning Theory • Learning Theory
PDF Bib Video Supplement
Black-Box Methods for Restoring Monotonicity
Evangelia Gergatsouli • Brendan Lucier • Christos Tzamos
Keywords: Learning Theory
PDF Bib Video Supplement
Generalization via Derandomization
Jeffrey Negrea • Gintare Karolina Dziugaite • Daniel M. Roy
Keywords: Learning Theory
PDF Bib Video Supplement
Familywise Error Rate Control by Interactive Unmasking
Boyan Duan • Aaditya Ramdas • Larry Wasserman
Keywords: Learning Theory
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Strategyproof Mean Estimation from Multiple-Choice Questions
Anson Kahng • Gregory Kehne • Ariel D. Procaccia
Keywords: Learning Theory
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Learning Structured Latent Factors from Dependent Data:A Generative Model Framework from Information-Theoretic Perspective
Ruixiang ZHANG • Masanori Koyama • Katsuhiko Ishiguro
Keywords: Learning Theory • Learning Theory • Probabilistic Inference - Models and Probabilistic Programming • Fairness, Equity, Justice, and Safety
PDF Bib Video Supplement
Sample Amplification: Increasing Dataset Size even when Learning is Impossible
Brian Axelrod • Shivam Garg • Vatsal Sharan • Gregory Valiant
Keywords: Learning Theory • Learning Theory • Learning Theory • Probabilistic Inference - Models and Probabilistic Programming
PDF Bib Video Supplement
On Validation and Planning of An Optimal Decision Rule with Application in Healthcare Studies
Hengrui Cai • Wenbin Lu • Rui Song
Keywords: Learning Theory • Applications - Neuroscience, Cognitive Science, Biology and Health • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods • Causality
PDF Bib Video Supplement
When are Non-Parametric Methods Robust?
Robi Bhattacharjee • Kamalika Chaudhuri
Keywords: Learning Theory • Adversarial Examples
PDF Bib Video Supplement
Boosted Histogram Transform for Regression
Yuchao Cai • Hanyuan Hang • Hanfang Yang • Zhouchen Lin
Keywords: Learning Theory • Supervised Learning
PDF Bib Video Supplement
Improved Communication Cost in Distributed PageRank Computation – A Theoretical Study
Siqiang Luo
Keywords: Learning Theory • Learning Theory
PDF Bib Video
Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks
Pranjal Awasthi • Natalie Frank • Mehryar Mohri
Keywords: Learning Theory • Adversarial Examples
PDF Bib Video Supplement
No-Regret and Incentive-Compatible Online Learning
Rupert Freeman • David Pennock • Chara Podimata • Jennifer Wortman Vaughan
Keywords: Learning Theory • Online Learning, Active Learning, and Bandits
PDF Bib Video
From Chaos to Order: Symmetry and Conservation Laws in Game Dynamics
Sai Ganesh Nagarajan • David Balduzzi • Georgios Piliouras
Keywords: Learning Theory • Planning, Control, and Multiagent Learning • Online Learning, Active Learning, and Bandits • Reinforcement Learning - Theory
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Deep k-NN for Noisy Labels
Dara Bahri • Heinrich Jiang • Maya Gupta
Keywords: Learning Theory • Deep Learning - General • Supervised Learning
PDF Bib Video Supplement
Provable guarantees for decision tree induction: the agnostic setting
Guy Blanc • Jane Lange • Li-Yang Tan
Keywords: Learning Theory
PDF Bib Video Supplement
Learning the Valuations of a $k$-demand Agent
Hanrui Zhang • Vincent Conitzer
Keywords: Learning Theory • Online Learning, Active Learning, and Bandits • Learning Theory
PDF Bib Video
Optimal Bounds between f-Divergences and Integral Probability Metrics
Rohit Agrawal • Thibaut Horel
Keywords: Learning Theory • Optimization - Convex
PDF Bib Video
Approximation Capabilities of Neural ODEs and Invertible Residual Networks
Han Zhang • Xi Gao • Jacob Unterman • Tom Arodz
Keywords: Learning Theory • Deep Learning - General • Supervised Learning
PDF Bib Video
Fine-Grained Analysis of Stability and Generalization for Stochastic Gradient Descent
Yunwen Lei • Yiming Ying
Keywords: Learning Theory • Online Learning, Active Learning, and Bandits • Optimization - Convex
PDF Bib Video Supplement
Finite-Time Last-Iterate Convergence for Multi-Agent Learning in Games
Tianyi Lin • Zhengyuan Zhou • Panayotis Mertikopoulos • Michael Jordan
Keywords: Learning Theory • Online Learning, Active Learning, and Bandits
PDF Bib Video Supplement
A Distributional Framework For Data Valuation
Amirata Ghorbani • Michael P. Kim • James Zou
Keywords: Learning Theory • data valuation
PDF Bib Video Supplement
Data Amplification: Instance-Optimal Property Estimation
Yi Hao • Alon Orlitsky
Keywords: Learning Theory • Learning Theory • Learning Theory • Learning Theory
PDF Bib Video Supplement
Strategic Classification is Causal Modeling in Disguise
John P Miller • Smitha L Milli • Moritz Hardt
Keywords: Learning Theory • Accountability, Transparency and Interpretability • Causality
PDF Bib Video Supplement
Curvature-corrected learning dynamics in deep neural networks
Dongsung Huh
Keywords: Learning Theory • Optimization - Non-convex • Optimization - General
PDF Bib Video Supplement
On Learning Language-Invariant Representations for Universal Machine Translation
Han Zhao • Junjie Hu • Andrej Risteski
Keywords: Learning Theory • Applications - Language, Speech and Dialog • Sequential, Network, and Time-Series Modeling • Transfer, Multitask and Meta-learning
PDF Bib Video Supplement
Superpolynomial Lower Bounds for Learning One-Layer Neural Networks using Gradient Descent
Surbhi Goel • Aravind P Gollakota • Zhihan Jin • Sushrut Karmalkar • Adam Klivans
Keywords: Learning Theory
PDF Bib Video Supplement
Minimax-Optimal Off-Policy Evaluation with Linear Function Approximation
Yaqi Duan • Zeyu Jia • Mengdi Wang
Keywords: Learning Theory • Learning Theory • Reinforcement Learning - Theory
PDF Bib Video
Fiduciary Bandits
Gal Bahar • Omer Ben-Porat • Kevin Leyton-Brown • Moshe Tennenholtz
Keywords: Learning Theory • Fairness, Equity, Justice, and Safety
PDF Bib Video Supplement
The Role of Regularization in Classification of High-dimensional Noisy Gaussian Mixture
Francesca Mignacco • Florent Krzakala • Yue Lu • Pierfrancesco Urbani • Lenka Zdeborova
Keywords: Learning Theory • Learning Theory • Learning Theory
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The Performance Analysis of Generalized Margin Maximizers on Separable Data
Fariborz Salehi • Ehsan Abbasi • Babak Hassibi
Keywords: Learning Theory • Optimization - Convex
PDF Bib Video
Converging to Team-Maxmin Equilibria in Zero-Sum Multiplayer Games
Youzhi Zhang • Bo An
Keywords: Learning Theory • Optimization - General • Optimization - Non-convex
PDF Bib Video Supplement
Black-box Certification and Learning under Adversarial Perturbations
Hassan Ashtiani • Vinayak Pathak • Ruth Urner
Keywords: Learning Theory • Adversarial Examples
PDF Bib Video Supplement
Explainable k-Means and k-Medians Clustering
Michal Moshkovitz • Sanjoy Dasgupta • Cyrus Rashtchian • Nave Frost
Keywords: Learning Theory • Unsupervised and Semi-Supervised Learning • Accountability, Transparency and Interpretability
PDF Bib Video Supplement
Quantum Boosting
Srinivasan Arunachalam • Reevu Maity
Keywords: Learning Theory • Quantum computing
PDF Bib Video Supplement
Performative Prediction
Juan C Perdomo • Tijana Zrnic • Celestine Mendler-Dünner • Moritz Hardt
Keywords: Learning Theory • Optimization - Convex • Causality • Trustworthy Machine Learning
PDF Bib Video Supplement
Supervised learning: no loss no cry
Richard Nock • Aditya K Menon
Keywords: Learning Theory • Supervised Learning
PDF Bib Video Supplement
Optimization and Analysis of the pAp@k Metric for Recommender Systems
Gaurush Hiranandani • Warut Vijitbenjaronk • Sanmi Koyejo • Prateek Jain
Keywords: Learning Theory • Applications - Other • Supervised Learning • Sequential, Network, and Time-Series Modeling
PDF Bib Video Supplement
Near-Tight Margin-Based Generalization Bounds for Support Vector Machines
Allan Grønlund • Lior Kamma • Kasper Green Larsen
Keywords: Learning Theory • Learning Theory
PDF Bib Video
Uniform Convergence of Rank-weighted Learning
Justin Khim • Liu Leqi • Adarsh Prasad • Pradeep Ravikumar
Keywords: Learning Theory • Learning Theory
PDF Bib Video Supplement
Near-optimal sample complexity bounds for learning Latent $k-$polytopes and applications to Ad-Mixtures
Chiranjib Bhattacharyya • Ravindran Kannan
Keywords: Learning Theory • Learning Theory • Optimization - General • Probabilistic Inference - Models and Probabilistic Programming
PDF Bib Video
Real-Time Optimisation for Online Learning in Auctions
Lorenzo Croissant • Marc Abeille • Clement Calauzenes
Keywords: Learning Theory • Online Learning, Active Learning, and Bandits
PDF Bib Video Supplement
Fast Learning of Graph Neural Networks with Guaranteed Generalizability: One-hidden-layer Case
shuai zhang • Meng Wang • Sijia Liu • Pin-Yu Chen • Jinjun Xiong
Keywords: Learning Theory • Deep Learning - General • Unsupervised and Semi-Supervised Learning • Optimization - Non-convex
PDF Bib Video Supplement
Implicit Learning Dynamics in Stackelberg Games: Equilibria Characterization, Convergence Analysis, and Empirical Study
Tanner Fiez • Benjamin Chasnov • Lillian Ratliff
Keywords: Learning Theory • Planning, Control, and Multiagent Learning • Learning Theory • Optimization - General
PDF Bib Video Supplement
Understanding Self-Training for Gradual Domain Adaptation
Ananya Kumar • Tengyu Ma • Percy Liang
Keywords: Learning Theory • Unsupervised and Semi-Supervised Learning
PDF Bib Video
Extra-gradient with player sampling for faster convergence in n-player games
Samy Jelassi • Carles Domingo-Enrich • Damien Scieur • Arthur Mensch • Joan Bruna
Keywords: Learning Theory • Deep Learning - General • Optimization - Convex
PDF Bib Video Supplement
Fast computation of Nash Equilibria in Imperfect Information Games
Remi Munos • Julien Perolat • Jean-Baptiste Lespiau • Mark Rowland • Bart De Vylder • Marc Lanctot • Finbarr Timbers • Daniel Hennes • Shayegan Omidshafiei • Audrunas Gruslys • Mohammad Gheshlaghi Azar • Edward Lockhart • Karl Tuyls
Keywords: Learning Theory
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Class-Weighted Classification: Trade-offs and Robust Approaches
Ziyu Xu • Chen Dan • Justin Khim • Pradeep Ravikumar
Keywords: Learning Theory • Supervised Learning • Learning Theory • Learning Theory
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Teaching with Limited Information on the Learner's Behaviour
Ferdinando Cicalese • Sergio Filho • Eduardo S Laber • Marco Molinaro
Keywords: Learning Theory • Online Learning, Active Learning, and Bandits • Learning Theory • Optimization - General
PDF Bib Video Supplement
High-dimensional Robust Mean Estimation via Gradient Descent
Yu Cheng • Ilias Diakonikolas • Rong Ge • Mahdi Soltanolkotabi
Keywords: Learning Theory • Optimization - Non-convex
PDF Bib Video Supplement
Sparsified Linear Programming for Zero-Sum Equilibrium Finding
Brian Hu Zhang • Tuomas Sandholm
Keywords: Learning Theory
PDF Bib Video Supplement
The Cost-free Nature of Optimally Tuning Tikhonov Regularizers and Other Ordered Smoothers
Pierre C. Bellec • Dana Yang
Keywords: Learning Theory
PDF Bib Video Supplement
Learning Opinions in Social Networks
Vincent Conitzer • Debmalya Panigrahi • Hanrui Zhang
Keywords: Learning Theory • Applications - Other
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Federated Learning with Only Positive Labels
Felix Yu • Ankit Singh Rawat • Aditya K Menon • Sanjiv Kumar
Keywords: Learning Theory • Optimization - Large Scale, Parallel and Distributed • Privacy-preserving Statistics and Machine Learning
PDF Bib Video
Online Learning, Active Learning, and Bandits
Distributionally Robust Policy Evaluation and Learning in Offline Contextual Bandits
Nian Si • Fan Zhang • Zhengyuan Zhou • Jose Blanchet
Keywords: Online Learning, Active Learning, and Bandits • Online Learning, Active Learning, and Bandits • Trustworthy Machine Learning
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Online Learning with Imperfect Hints
Aditya Bhaskara • Ashok Cutkosky • Ravi Kumar • Manish Purohit
Keywords: Online Learning, Active Learning, and Bandits
PDF Bib Video Supplement
Online Convex Optimization in the Random Order Model
Dan Garber • Gal Korcia • Kfir Yehuda Levy
Keywords: Online Learning, Active Learning, and Bandits • Optimization - Convex
PDF Bib Video Supplement
Parameter-free, Dynamic, and Strongly-Adaptive Online Learning
Ashok Cutkosky
Keywords: Online Learning, Active Learning, and Bandits
PDF Bib Video
Neural Contextual Bandits with UCB-based Exploration
Dongruo Zhou • Lihong Li • Quanquan Gu
Keywords: Online Learning, Active Learning, and Bandits • Deep Learning - Theory
PDF Bib Video Supplement
Structure Adaptive Algorithms for Stochastic Bandits
Rémy Degenne • Han Shao • Wouter M Koolen
Keywords: Online Learning, Active Learning, and Bandits • Online Learning, Active Learning, and Bandits
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Robust Outlier Arm Identification
Yinglun Zhu • Sumeet Katariya • Robert Nowak
Keywords: Online Learning, Active Learning, and Bandits • Trustworthy Machine Learning
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Combinatorial Pure Exploration for Dueling Bandit
Wei Chen • Yihan Du • Longbo Huang • Haoyu Zhao
Keywords: Online Learning, Active Learning, and Bandits
PDF Bib Video Supplement
Reinforcement Learning for Non-Stationary Markov Decision Processes: The Blessing of (More) Optimism
Wang Chi Cheung • David Simchi-Levi • Ruihao Zhu
Keywords: Online Learning, Active Learning, and Bandits • Reinforcement Learning - Theory
PDF Bib Video
Information Particle Filter Tree: An Online Algorithm for POMDPs with Belief-Based Rewards on Continuous Domains
Johannes Fischer • Ömer Sahin Tas
Keywords: Online Learning, Active Learning, and Bandits • Planning, Control, and Multiagent Learning • Applications - Other • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods
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Gamification of Pure Exploration for Linear Bandits
Rémy Degenne • Pierre Menard • Xuedong Shang • Michal Valko
Keywords: Online Learning, Active Learning, and Bandits • Learning Theory • Learning Theory
PDF Bib Video Supplement
Structured Linear Contextual Bandits: A Sharp and Geometric Smoothed Analysis
Vidyashankar Sivakumar • Steven Wu • Arindam Banerjee
Keywords: Online Learning, Active Learning, and Bandits • Online Learning, Active Learning, and Bandits • Optimization - General
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Restarted Bayesian Online Change-point Detector achieves Optimal Detection Delay
Reda ALAMI • Odalric Maillard • Raphael Feraud
Keywords: Online Learning, Active Learning, and Bandits • Learning Theory
PDF Bib Video Supplement
Near-optimal Regret Bounds for Stochastic Shortest Path
Aviv Rosenberg • Alon Cohen • Yishay Mansour • Haim Kaplan
Keywords: Online Learning, Active Learning, and Bandits • Reinforcement Learning - Theory
PDF Bib Video Supplement
Doubly robust off-policy evaluation with shrinkage
Yi Su • Maria Dimakopoulou • Akshay Krishnamurthy • Miroslav Dudík
Keywords: Online Learning, Active Learning, and Bandits • Applications - Other
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Bandits for BMO Functions
Tianyu Wang • Cynthia Rudin
Keywords: Online Learning, Active Learning, and Bandits • Reinforcement Learning - General • Online Learning, Active Learning, and Bandits • Reinforcement Learning - Theory
PDF Bib Video Supplement
Thompson Sampling Algorithms for Mean-Variance Bandits
Qiuyu Zhu • Vincent Tan
Keywords: Online Learning, Active Learning, and Bandits • Reinforcement Learning - Theory
PDF Bib Video Supplement
Improved Bounds on Minimax Regret under Logarithmic Loss via Self-Concordance
Blair Bilodeau • Dylan Foster • Daniel M. Roy
Keywords: Online Learning, Active Learning, and Bandits • Learning Theory
PDF Bib Video Supplement
Random Hypervolume Scalarizations for Provable Multi-Objective Black Box Optimization
Richard Zhang • Daniel Golovin
Keywords: Online Learning, Active Learning, and Bandits • Online Learning, Active Learning, and Bandits • Optimization - Large Scale, Parallel and Distributed • Probabilistic Inference - Models and Probabilistic Programming
PDF Bib Video Supplement
Improved Optimistic Algorithms for Logistic Bandits
Louis Faury • Marc Abeille • Clément Calauzènes • Olivier Fercoq
Keywords: Online Learning, Active Learning, and Bandits
PDF Bib Video Supplement
Budgeted Online Influence Maximization
Pierre Perrault • Jennifer Healey • Zheng Wen • Michal Valko
Keywords: Online Learning, Active Learning, and Bandits • Sequential, Network, and Time-Series Modeling • Online Learning, Active Learning, and Bandits • Optimization - General
PDF Bib Video Supplement
Exploration Through Reward Biasing: Reward-Biased Maximum Likelihood Estimation for Stochastic Multi-Armed Bandits
Xi Liu • Ping-Chun Hsieh • Yu Heng Hung • Anirban Bhattacharya • P. R. Kumar
Keywords: Online Learning, Active Learning, and Bandits • Online Learning, Active Learning, and Bandits
PDF Bib Video Supplement
Active Learning on Attributed Graphs via Graph Cognizant Logistic Regression and Preemptive Query Generation
Florence Regol • Soumyasundar Pal • Yingxue Zhang • Mark Coates
Keywords: Online Learning, Active Learning, and Bandits • Applications - Other • Sequential, Network, and Time-Series Modeling • Unsupervised and Semi-Supervised Learning
PDF Bib Video Supplement
When Demands Evolve Larger and Noisier: Learning and Earning in a Growing Environment
Feng Zhu • Zeyu Zheng
Keywords: Online Learning, Active Learning, and Bandits • Dynamic Pricing
PDF Bib Video Supplement
Best Arm Identification for Cascading Bandits in the Fixed Confidence Setting
Zixin Zhong • Wang Chi Cheung • Vincent Tan
Keywords: Online Learning, Active Learning, and Bandits • Online Learning, Active Learning, and Bandits • Reinforcement Learning - Theory • Learning Theory
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On Thompson Sampling with Langevin Algorithms
Eric V Mazumdar • Aldo Pacchiano • Yian Ma • Michael Jordan • Peter Bartlett
Keywords: Online Learning, Active Learning, and Bandits • Online Learning, Active Learning, and Bandits
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Non-Stationary Delayed Bandits with Intermediate Observations
Claire Vernade • Andras Gyorgy • Timothy Arthur Mann
Keywords: Online Learning, Active Learning, and Bandits • Online Learning, Active Learning, and Bandits
PDF Bib Video Supplement
Preselection Bandits
Viktor Bengs • Eyke Hüllermeier
Keywords: Online Learning, Active Learning, and Bandits • Supervised Learning • Optimization - General
PDF Bib Video Supplement
Stochastic bandits with arm-dependent delays
Manegueu Anne Gael • Claire Vernade • Alexandra Carpentier • Michal Valko
Keywords: Online Learning, Active Learning, and Bandits • Online Learning, Active Learning, and Bandits
PDF Bib Video Supplement
Influence Diagram Bandits: Variational Thompson Sampling for Structured Bandit Problems
Tong Yu • Branislav Kveton • Zheng Wen • Ruiyi Zhang • Ole J. Mengshoel
Keywords: Online Learning, Active Learning, and Bandits • Probabilistic Inference - Models and Probabilistic Programming
PDF Bib Video Supplement
Bisection-Based Pricing for Repeated Contextual Auctions against Strategic Buyer
Anton Zhiyanov • Alexey Drutsa
Keywords: Online Learning, Active Learning, and Bandits • Online Learning, Active Learning, and Bandits • Learning Theory
PDF Bib Video Supplement
Multinomial Logit Bandit with Low Switching Cost
Kefan Dong • Yingkai Li • Qin Zhang • Yuan Zhou
Keywords: Online Learning, Active Learning, and Bandits • Online Learning, Active Learning, and Bandits
PDF Bib Video Supplement
Dual Mirror Descent for Online Allocation Problems
Santiago Balseiro • Haihao Lu • Vahab Mirrokni
Keywords: Online Learning, Active Learning, and Bandits • Optimization - Convex
PDF Bib Video Supplement
Improved Sleeping Bandits with Stochastic Action Sets and Adversarial Rewards
Aadirupa Saha • Pierre Gaillard • Michal Valko
Keywords: Online Learning, Active Learning, and Bandits • Online Learning, Active Learning, and Bandits • Online Learning, Active Learning, and Bandits
PDF Bib Video Supplement
Concentration bounds for CVaR estimation: The cases of light-tailed and heavy-tailed distributions
Prashanth L.A. • Krishna Jagannathan • Ravi K Kolla
Keywords: Online Learning, Active Learning, and Bandits • Online Learning, Active Learning, and Bandits • Learning Theory
PDF Bib Video
From PAC to Instance-Optimal Sample Complexity in the Plackett-Luce Model
Aadirupa Saha • Aditya Gopalan
Keywords: Online Learning, Active Learning, and Bandits • Online Learning, Active Learning, and Bandits • Supervised Learning • Online Learning, Active Learning, and Bandits
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On conditional versus marginal bias in multi-armed bandits
Jaehyeok Shin • Aaditya Ramdas • Alessandro Rinaldo
Keywords: Online Learning, Active Learning, and Bandits
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Online Learning with Dependent Stochastic Feedback Graphs
Corinna Cortes • Giulia DeSalvo • Claudio Gentile • Mehryar Mohri • Ningshan Zhang
Keywords: Online Learning, Active Learning, and Bandits • Learning Theory
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Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles
Dylan Foster • Alexander Rakhlin
Keywords: Online Learning, Active Learning, and Bandits
PDF Bib Video Supplement
A simpler approach to accelerated optimization: iterative averaging meets optimism
Pooria Joulani • Anant Raj • András György • Csaba Szepesvári
Keywords: Online Learning, Active Learning, and Bandits • Optimization - Convex
PDF Bib Video Supplement
Near-linear time Gaussian process optimization with adaptive batching and resparsification
Daniele Calandriello • Luigi Carratino • Alessandro Lazaric • Michal Valko • Lorenzo Rosasco
Keywords: Online Learning, Active Learning, and Bandits • Optimization - Large Scale, Parallel and Distributed • Probabilistic Inference - Models and Probabilistic Programming • Gaussian Processes
PDF Bib Video Supplement
Online Learning for Active Cache Synchronization
Andrey Kolobov • Sébastien Bubeck • Julian Zimmert
Keywords: Online Learning, Active Learning, and Bandits • Applications - Other • Applications - Other • Reinforcement Learning - General
PDF Bib Video
Online Control of the False Coverage Rate and False Sign Rate
Asaf Weinstein • Aaditya Ramdas
Keywords: Online Learning, Active Learning, and Bandits
PDF Bib Video Supplement
Online Pricing with Offline Data: Phase Transition and Inverse Square Law
Jinzhi Bu • David Simchi-Levi • Yunzong Xu
Keywords: Online Learning, Active Learning, and Bandits • Online Learning, Active Learning, and Bandits • Learning Theory
PDF Bib Video
Adaptive Region-Based Active Learning
Corinna Cortes • Giulia DeSalvo • Claudio Gentile • Mehryar Mohri • Ningshan Zhang
Keywords: Online Learning, Active Learning, and Bandits
PDF Bib Video Supplement
My Fair Bandit: Distributed Learning of Max-Min Fairness with Multi-player Bandits
Ilai Bistritz • Tavor Z Baharav • Amir Leshem • Nicholas Bambos
Keywords: Online Learning, Active Learning, and Bandits • Planning, Control, and Multiagent Learning
PDF Bib Video
Linear bandits with Stochastic Delayed Feedback
Claire Vernade • Alexandra Carpentier • Tor Lattimore • Giovanni Zappella • Beyza Ermis • Michael Brückner
Keywords: Online Learning, Active Learning, and Bandits
PDF Bib Video Supplement
Projection-free Distributed Online Convex Optimization with $O(\sqrt{T})$ Communication Complexity
Yuanyu Wan • Wei-Wei Tu • Lijun Zhang
Keywords: Online Learning, Active Learning, and Bandits • Optimization - Convex • Optimization - Large Scale, Parallel and Distributed
PDF Bib Video Supplement
Online mirror descent and dual averaging: keeping pace in the dynamic case
Huang Fang • Nick Harvey • Victor Portella • Michael P Friedlander
Keywords: Online Learning, Active Learning, and Bandits • Optimization - Convex
PDF Bib Video Supplement
Bandits with Adversarial Scaling
Thodoris Lykouris • Vahab Mirrokni • Renato Paes Leme
Keywords: Online Learning, Active Learning, and Bandits • Learning Theory
PDF Bib Video
Adaptive Sampling for Estimating Probability Distributions
Shubhanshu Shekhar • Tara Javidi • Mohammad Ghavamzadeh
Keywords: Online Learning, Active Learning, and Bandits • Online Learning, Active Learning, and Bandits
PDF Bib Video
Optimization - Convex
On Convergence-Diagnostic based Step Sizes for Stochastic Gradient Descent
Scott Pesme • Aymeric Dieuleveut • Nicolas Flammarion
Keywords: Optimization - Convex • Optimization - Large Scale, Parallel and Distributed
PDF Bib Video Supplement
Lower Complexity Bounds for Finite-Sum Convex-Concave Minimax Optimization Problems
Guangzeng Xie • Luo Luo • Yijiang Lian • Zhihua Zhang
Keywords: Optimization - Convex • Optimization - Non-convex
PDF Bib Video Supplement
Continuous-time Lower Bounds for Gradient-based Algorithms
Michael Muehlebach • Michael Jordan
Keywords: Optimization - Convex • Optimization - Large Scale, Parallel and Distributed • Optimization - Non-convex
PDF Bib Video Supplement
Boosting Frank-Wolfe by Chasing Gradients
Cyrille W. Combettes • Sebastian Pokutta
Keywords: Optimization - Convex • Supervised Learning
PDF Bib Video Supplement
On the Convergence of Nesterov's Accelerated Gradient Method in Stochastic Settings
Mahmoud Assran • Mike Rabbat
Keywords: Optimization - Convex
PDF Bib Video Supplement
Almost Tune-Free Variance Reduction
Bingcong Li • Lingda Wang • Georgios B. Giannakis
Keywords: Optimization - Convex • Optimization - Large Scale, Parallel and Distributed
PDF Bib Video Supplement
Hybrid Stochastic-Deterministic Minibatch Proximal Gradient: Less-Than-Single-Pass Optimization with Nearly Optimal Generalization
Pan Zhou • Xiao-Tong Yuan
Keywords: Optimization - Convex • Optimization - Large Scale, Parallel and Distributed
PDF Bib Video Supplement
Random extrapolation for primal-dual coordinate descent
Ahmet Alacaoglu • Olivier Fercoq • Volkan Cevher
Keywords: Optimization - Convex • Optimization - Large Scale, Parallel and Distributed
PDF Bib Video Supplement
Closing the convergence gap of SGD without replacement
Shashank Rajput • Anant Gupta • Dimitris Papailiopoulos
Keywords: Optimization - Convex • Learning Theory • Optimization - Large Scale, Parallel and Distributed
PDF Bib Video Supplement
Stochastic Optimization for Regularized Wasserstein Estimators
Marin Ballu • Quentin Berthet • Francis Bach
Keywords: Optimization - Convex • Learning Theory
PDF Bib Video Supplement
Conditional gradient methods for stochastically constrained convex minimization
Maria-Luiza Vladarean • Ahmet Alacaoglu • Ya-Ping Hsieh • Volkan Cevher
Keywords: Optimization - Convex • Optimization - Large Scale, Parallel and Distributed
PDF Bib Video Supplement
Spectral Frank-Wolfe Algorithm: Strict Complementarity and Linear Convergence
Lijun Ding • Yingjie Fei • Qiantong Xu • Chengrun Yang
Keywords: Optimization - Convex • Optimization - Large Scale, Parallel and Distributed
PDF Bib Video Supplement
Efficiently Solving MDPs with Stochastic Mirror Descent
Yujia Jin • Aaron Sidford
Keywords: Optimization - Convex • Learning Theory • Reinforcement Learning - Theory
PDF Bib Video Supplement
Semismooth Newton Algorithm for Efficient Projections onto $\ell_{1, \infty}$-norm Ball
Dejun Chu • Changshui Zhang • Shiliang Sun • Qing Tao
Keywords: Optimization - Convex • Optimization - Large Scale, Parallel and Distributed • Optimization - General
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Stochastic Subspace Cubic Newton Method
Filip Hanzely • Nikita Doikov • Yurii Nesterov • Peter Richtarik
Keywords: Optimization - Convex • Learning Theory • Optimization - Large Scale, Parallel and Distributed
PDF Bib Video Supplement
Debiased Sinkhorn barycenters
Hicham Janati • Marco Cuturi • Alexandre Gramfort
Keywords: Optimization - Convex
PDF Bib Video Supplement
Variance Reduced Coordinate Descent with Acceleration: New Method With a Surprising Application to Finite-Sum Problems
Filip Hanzely • Dmitry Kovalev • Peter Richtárik
Keywords: Optimization - Convex • Learning Theory • Optimization - Large Scale, Parallel and Distributed
PDF Bib Video Supplement
A new regret analysis for Adam-type algorithms
Ahmet Alacaoglu • Yura Malitsky • Panayotis Mertikopoulos • Volkan Cevher
Keywords: Optimization - Convex • Deep Learning - General
PDF Bib Video Supplement
Universal Asymptotic Optimality of Polyak Momentum
Damien Scieur • Fabian Pedregosa
Keywords: Optimization - Convex • Deep Learning - General • Optimization - Large Scale, Parallel and Distributed
PDF Bib Video
Neural Networks are Convex Regularizers: Exact Polynomial-time Convex Optimization Formulations for Two-layer Networks
Mert Pilanci • Tolga Ergen
Keywords: Optimization - Convex • Learning Theory • Optimization - Non-convex • Optimization - General
PDF Bib Video Supplement
Adaptive Gradient Descent without Descent
Yura Malitsky • Konstantin Mishchenko
Keywords: Optimization - Convex
PDF Bib Video Supplement
Stochastic Coordinate Minimization with Progressive Precision for Stochastic Convex Optimization
Sudeep Salgia • Qing Zhao • Sattar Vakili
Keywords: Optimization - Convex • Learning Theory
PDF Bib Video Supplement
Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization
Geoffrey Negiar • Gideon Dresdner • Alicia Yi-Ting Tsai • Laurent El Ghaoui • Francesco Locatello • Robert Freund • Fabian Pedregosa
Keywords: Optimization - Convex • Optimization - Large Scale, Parallel and Distributed • Optimization - General
PDF Bib Video Supplement
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
Sai Praneeth Karimireddy • Satyen Kale • Mehryar Mohri • Sashank Reddi • Sebastian Stich • Ananda Theertha Suresh
Keywords: Optimization - Convex • Optimization - Non-convex • Optimization - Large Scale, Parallel and Distributed
PDF Bib Video Supplement
Self-Concordant Analysis of Frank-Wolfe Algorithms
Pavel Dvurechensky • Petr Ostroukhov • Kamil Safin • Shimrit Shtern • Mathias Staudigl
Keywords: Optimization - Convex • Optimization - Large Scale, Parallel and Distributed
PDF Bib Video Supplement
Inexact Tensor Methods with Dynamic Accuracies
Nikita Doikov • Yurii Nesterov
Keywords: Optimization - Convex • Optimization - General
PDF Bib Video Supplement
Anderson Acceleration of Proximal Gradient Methods
Vien Van Mai • Mikael Johansson
Keywords: Optimization - Convex • Optimization - Non-convex
PDF Bib Video Supplement
An Accelerated DFO Algorithm for Finite-sum Convex Functions
Yuwen Chen • Antonio Orvieto • Aurelien Lucchi
Keywords: Optimization - Convex
PDF Bib Video Supplement
Optimal Randomized First-Order Methods for Least-Squares Problems
Jonathan Lacotte • Mert Pilanci
Keywords: Optimization - Convex • Optimization - Large Scale, Parallel and Distributed
PDF Bib Video Supplement
Acceleration through spectral density estimation
Fabian Pedregosa • Damien Scieur
Keywords: Optimization - Convex • Deep Learning - Algorithms • Deep Learning - General • Optimization - Large Scale, Parallel and Distributed
PDF Bib Video Supplement
Distributed Online Optimization over a Heterogeneous Network
Nima Eshraghi • Ben Liang
Keywords: Optimization - Convex • Planning, Control, and Multiagent Learning • Optimization - Large Scale, Parallel and Distributed
PDF Bib Video
Optimization - General
Can Stochastic Zeroth-Order Frank-Wolfe Method Converge Faster for Non-Convex Problems?
Hongchang Gao • Heng Huang
Keywords: Optimization - General
PDF Bib Video Supplement
Compressive sensing with un-trained neural networks: Gradient descent finds a smooth approximation
Reinhard Heckel • Mahdi Soltanolkotabi
Keywords: Optimization - General • Deep Learning - Theory • Deep Learning - General • Optimization - Non-convex
PDF Bib Video Supplement
Inertial Block Proximal Methods for Non-Convex Non-Smooth Optimization
Hien Thi Khanh Le • Nicolas Gillis • Panagiotis Patrinos
Keywords: Optimization - General • General Machine Learning Techniques • Optimization - Non-convex
PDF Bib Video Supplement
Finite-Time Convergence in Continuous-Time Optimization
Orlando Romero • Mouhacine Benosman
Keywords: Optimization - General • Deep Learning - General • Optimization - Convex • Optimization - Non-convex
PDF Bib Video
Safe screening rules for L0-regression from Perspective Relaxations
Alper Atamtürk • Andrés Gómez
Keywords: Optimization - General • Unsupervised and Semi-Supervised Learning • Optimization - General
PDF Bib Video Supplement
Online Dense Subgraph Discovery via Blurred-Graph Feedback
Yuko Kuroki • Atsushi Miyauchi • Junya Honda • Masashi Sugiyama
Keywords: Optimization - General • Applications - Other • Online Learning, Active Learning, and Bandits
PDF Bib Video Supplement
Sample Complexity Bounds for 1-bit Compressive Sensing and Binary Stable Embeddings with Generative Priors
Zhaoqiang Liu • Selwyn Gomes • Avtansh Tiwari • Jonathan Scarlett
Keywords: Optimization - General • Learning Theory
PDF Bib Video Supplement
Recovery of Sparse Signals from a Mixture of Linear Samples
Soumyabrata Pal • Arya Mazumdar
Keywords: Optimization - General • Unsupervised and Semi-Supervised Learning • Learning Theory • Learning Theory
PDF Bib Video Supplement
On the Power of Compressed Sensing with Generative Models
Akshay Kamath • Eric Price • Sushrut Karmalkar
Keywords: Optimization - General • Deep Learning - Generative Models and Autoencoders
PDF Bib Video Supplement
Invertible generative models for inverse problems: mitigating representation error and dataset bias
Muhammad Asim • Max Daniels • Oscar F Leong • Ali Ahmed • Paul Hand
Keywords: Optimization - General
PDF Bib Video Supplement
Fully Parallel Hyperparameter Search: Reshaped Space-Filling
Marie-Liesse Cauwet • Camille Couprie • Julien Dehos • Pauline Luc • Jeremy Rapin • Morgane Riviere • Fabien Teytaud • Olivier Teytaud • Nicolas Usunier
Keywords: Optimization - General • Optimization - Large Scale, Parallel and Distributed
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Optimization from Structured Samples for Coverage Functions
Wei Chen • Xiaoming Sun • Jialin Zhang • Zhijie Zhang
Keywords: Optimization - General • Learning Theory • Optimization - Large Scale, Parallel and Distributed
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Streaming Submodular Maximization under a k-Set System Constraint
Ran Haba • Ehsan Kazemi • Moran Feldman • Amin Karbasi
Keywords: Optimization - General • Optimization - Large Scale, Parallel and Distributed
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Lifted Disjoint Paths with Application in Multiple Object Tracking
Andrea Hornakova • Roberto Henschel • Bodo Rosenhahn • Paul Swoboda
Keywords: Optimization - General • Applications - Computer Vision • Optimization - Convex
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Parallel Algorithm for Non-Monotone DR-Submodular Maximization
Alina Ene • Huy Nguyen
Keywords: Optimization - General • Optimization - Large Scale, Parallel and Distributed
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Hierarchical Verification for Adversarial Robustness
Cong Han Lim • Raquel Urtasun • Ersin Yumer
Keywords: Optimization - General • Deep Learning - General • Optimization - General • Adversarial Examples
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Fast Deterministic CUR Matrix Decomposition with Accuracy Assurance
Yasutoshi Ida • Sekitoshi Kanai • Yasuhiro Fujiwara • Tomoharu Iwata • Koh Takeuchi • Hisashi Kashima
Keywords: Optimization - General • General Machine Learning Techniques
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Coresets for Data-efficient Training of Machine Learning Models
Baharan Mirzasoleiman • Jeff Bilmes • Jure Leskovec
Keywords: Optimization - General • Optimization - Convex • Optimization - Large Scale, Parallel and Distributed
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Linear Lower Bounds and Conditioning of Differentiable Games
Adam Ibrahim • Waïss Azizian • Gauthier Gidel • Ioannis Mitliagkas
Keywords: Optimization - General • Learning Theory • Optimization - Convex • Optimization - Non-convex
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The FAST Algorithm for Submodular Maximization
Adam Breuer • Eric Balkanski • Yaron Singer
Keywords: Optimization - General
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Refined bounds for algorithm configuration: The knife-edge of dual class approximability
Maria-Florina Balcan • Tuomas Sandholm • Ellen Vitercik
Keywords: Optimization - General
PDF Bib Video Supplement
Sparse Convex Optimization via Adaptively Regularized Hard Thresholding
Kyriakos Axiotis • Maxim Sviridenko
Keywords: Optimization - General • Optimization - Convex
PDF Bib Video Supplement
Super-efficiency of automatic differentiation for functions defined as a minimum
Pierre Ablin • Gabriel Peyré • Thomas Moreau
Keywords: Optimization - General • Transfer, Multitask and Meta-learning • Optimization - Convex
PDF Bib Video Supplement
Obtaining Adjustable Regularization for Free via Iterate Averaging
Jingfeng Wu • Vladimir Braverman • Lin Yang
Keywords: Optimization - General • Deep Learning - General • Supervised Learning
PDF Bib Video Supplement
Approximation Guarantees of Local Search Algorithms via Localizability of Set Functions
Kaito Fujii
Keywords: Optimization - General • Optimization - General
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Optimal approximation for unconstrained non-submodular minimization
Marwa El Halabi • Stefanie Jegelka
Keywords: Optimization - General • Optimization - Non-convex • Optimization - General
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Customizing ML Predictions for Online Algorithms
Keerti Anand • Rong Ge • Debmalya Panigrahi
Keywords: Optimization - General • Learning Theory
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Upper bounds for Model-Free Row-Sparse Principal Component Analysis
Guanyi Wang • Santanu Dey
Keywords: Optimization - General • Optimization - Non-convex • Optimization - General
PDF Bib Video Supplement
Streaming k-Submodular Maximization under Noise subject to Size Constraint
Lan N Nguyen • My T. Thai
Keywords: Optimization - General • Streaming Algorithm
PDF Bib Video Supplement
On the Unreasonable Effectiveness of the Greedy Algorithm: Greedy Adapts to Sharpness
Sebastian Pokutta • Mohit Singh • Alfredo Torrico
Keywords: Optimization - General • Optimization - Non-convex
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Implicit differentiation of Lasso-type models for hyperparameter optimization
Quentin Bertrand • Quentin Klopfenstein • Mathieu Blondel • Samuel Vaiter • Alexandre Gramfort • Joseph Salmon
Keywords: Optimization - General
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Adaptive Sketching for Fast and Convergent Canonical Polyadic Decomposition
Alex Gittens • Kareem S Aggour • Bülent Yener
Keywords: Optimization - General • Optimization - Large Scale, Parallel and Distributed
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Optimization - Large Scale, Parallel and Distributed
Simultaneous Inference for Massive Data: Distributed Bootstrap
Yang Yu • Shih-Kang Chao • Guang Cheng
Keywords: Optimization - Large Scale, Parallel and Distributed • Supervised Learning • Optimization - Large Scale, Parallel and Distributed
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Communication-Efficient Distributed PCA by Riemannian Optimization
Long-Kai Huang • Sinno Pan
Keywords: Optimization - Large Scale, Parallel and Distributed • Optimization - Non-convex
PDF Bib Video Supplement
Fast OSCAR and OWL Regression via Safe Screening Rules
Runxue Bao • Bin Gu • Heng Huang
Keywords: Optimization - Large Scale, Parallel and Distributed • Optimization - General
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Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks
Zhishuai Guo • Mingrui Liu • Zhuoning Yuan • Li Shen • Wei Liu • Tianbao Yang
Keywords: Optimization - Large Scale, Parallel and Distributed • Deep Learning - Algorithms • Deep Learning - General • Optimization - Non-convex
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Linear Convergence of Randomized Primal-Dual Coordinate Method for Large-scale Linear Constrained Convex Programming
Daoli Zhu • Lei Zhao
Keywords: Optimization - Large Scale, Parallel and Distributed • Learning Theory • Optimization - Convex • Optimization - Large Scale, Parallel and Distributed
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Statistically Preconditioned Accelerated Gradient Method for Distributed Optimization
Hadrien Hendrikx • Lin Xiao • Sebastien Bubeck • Francis Bach • Laurent Massoulie
Keywords: Optimization - Large Scale, Parallel and Distributed • Optimization - Convex • Optimization - Large Scale, Parallel and Distributed
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Randomized Block-Diagonal Preconditioning for Parallel Learning
Celestine Mendler-Dünner • Aurelien Lucchi
Keywords: Optimization - Large Scale, Parallel and Distributed • Optimization - Convex
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FetchSGD: Communication-Efficient Federated Learning with Sketching
Daniel Rothchild • Ashwinee Panda • Enayat Ullah • Nikita Ivkin • Ion Stoica • Vladimir Braverman • Joseph Gonzalez • Raman Arora
Keywords: Optimization - Large Scale, Parallel and Distributed • Federated Learning
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The Non-IID Data Quagmire of Decentralized Machine Learning
Kevin Hsieh • Amar Phanishayee • Onur Mutlu • Phillip B Gibbons
Keywords: Optimization - Large Scale, Parallel and Distributed • General Machine Learning Techniques • Optimization - Large Scale, Parallel and Distributed
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On Coresets for Regularized Regression
Rachit Chhaya • Supratim Shit • Anirban Dasgupta
Keywords: Optimization - Large Scale, Parallel and Distributed • Unsupervised and Semi-Supervised Learning • General Machine Learning Techniques • Optimization - Convex
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Schatten Norms in Matrix Streams: Hello Sparsity, Goodbye Dimension
Vladimir Braverman • Robert Krauthgamer • Aditya R Krishnan • Roi Sinoff
Keywords: Optimization - Large Scale, Parallel and Distributed • General Machine Learning Techniques • Optimization - General
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Decoupled Greedy Learning of CNNs
Eugene Belilovsky • Michael Eickenberg • Edouard Oyallon
Keywords: Optimization - Large Scale, Parallel and Distributed • Applications - Neuroscience, Cognitive Science, Biology and Health • Deep Learning - Algorithms
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Moniqua: Modulo Quantized Communication in Decentralized SGD
Yucheng Lu • Christopher De Sa
Keywords: Optimization - Large Scale, Parallel and Distributed • Optimization - Non-convex
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Scalable Nearest Neighbor Search for Optimal Transport
Arturs Backurs • Yihe Dong • Piotr Indyk • Ilya Razenshteyn • Tal Wagner
Keywords: Optimization - Large Scale, Parallel and Distributed • Applications - Language, Speech and Dialog • General Machine Learning Techniques
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Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization
Zhize Li • Dmitry Kovalev • Xun Qian • Peter Richtárik
Keywords: Optimization - Large Scale, Parallel and Distributed • Deep Learning - Algorithms • Optimization - Convex • Optimization - Large Scale, Parallel and Distributed
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Manifold Identification for Ultimately Communication-Efficient Distributed Optimization
Yu-Sheng Li • Wei-Lin Chiang • Ching-pei Lee
Keywords: Optimization - Large Scale, Parallel and Distributed • Optimization - Large Scale, Parallel and Distributed • Optimization - General
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Don't Waste Your Bits! Squeeze Activations and Gradients for Deep Neural Networks via TinyScript
Fangcheng Fu • Yuzheng Hu • Yihan He • Jiawei Jiang • Yingxia Shao • Ce Zhang • Bin Cui
Keywords: Optimization - Large Scale, Parallel and Distributed • Optimization - Non-convex
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Extreme Multi-label Classification from Aggregated Labels
Yanyao Shen • Hsiang-Fu Yu • Sujay Sanghavi • Inderjit S. Dhillon
Keywords: Optimization - Large Scale, Parallel and Distributed • Unsupervised and Semi-Supervised Learning • Supervised Learning • Optimization - General
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Quantized Decentralized Stochastic Learning over Directed Graphs
Hossein Taheri • Aryan Mokhtari • Hamed Hassani • Ramtin Pedarsani
Keywords: Optimization - Large Scale, Parallel and Distributed • Optimization - Convex • Optimization - Non-convex
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Nearly Linear Row Sampling Algorithm for Quantile Regression
Yi Li • Ruosong Wang • Lin Yang • Hanrui Zhang
Keywords: Optimization - Large Scale, Parallel and Distributed • Optimization - Convex • Optimization - General
PDF Bib Video Supplement
Composable Sketches for Functions of Frequencies: Beyond the Worst Case
Edith Cohen • Ofir Geri • Rasmus Pagh
Keywords: Optimization - Large Scale, Parallel and Distributed • Optimization - Large Scale, Parallel and Distributed • Optimization - General
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Is Local SGD Better than Minibatch SGD?
Blake E Woodworth • Kumar Kshitij Patel • Sebastian Stich • Zhen Dai • Brian Bullins • Brendan McMahan • Ohad Shamir • Nathan Srebro
Keywords: Optimization - Large Scale, Parallel and Distributed • Optimization - Convex
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ECLIPSE: An Extreme-Scale Linear Program Solver for Web-Applications
Kinjal Basu • Amol Ghoting • Rahul Mazumder • Yao Pan
Keywords: Optimization - Large Scale, Parallel and Distributed • Applications - Other • Applications - Other • General Machine Learning Techniques
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One-shot Distributed Ridge Regression in High Dimensions
Yue Sheng • Edgar Dobriban
Keywords: Optimization - Large Scale, Parallel and Distributed • Supervised Learning • Optimization - Large Scale, Parallel and Distributed • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods
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AdaScale SGD: A User-Friendly Algorithm for Distributed Training
Tyler B. Johnson • Pulkit Agrawal • Haijie Gu • Carlos Guestrin
Keywords: Optimization - Large Scale, Parallel and Distributed • Optimization - Large Scale, Parallel and Distributed
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A Unified Theory of Decentralized SGD with Changing Topology and Local Updates
Anastasia Koloskova • Nicolas Loizou • Sadra Boreiri • Martin Jaggi • Sebastian Stich
Keywords: Optimization - Large Scale, Parallel and Distributed • Optimization - Convex • Optimization - Non-convex
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DINO: Distributed Newton-Type Optimization Method
Rixon Crane • Fred Roosta
Keywords: Optimization - Large Scale, Parallel and Distributed • Optimization - Non-convex
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Optimization - Non-convex
The Differentiable Cross-Entropy Method
Brandon Amos • Denis Yarats
Keywords: Optimization - Non-convex • Planning, Control, and Multiagent Learning • Transfer, Multitask and Meta-learning • Sequential, Network, and Time-Series Modeling
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Provably Efficient Exploration in Policy Optimization
Qi Cai • Zhuoran Yang • Chi Jin • Zhaoran Wang
Keywords: Optimization - Non-convex • Reinforcement Learning - Theory
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On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems
Tianyi Lin • Chi Jin • Michael Jordan
Keywords: Optimization - Non-convex • Optimization - Large Scale, Parallel and Distributed
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Complexity of Finding Stationary Points of Nonconvex Nonsmooth Functions
Jingzhao Zhang • Hongzhou Lin • Stefanie Jegelka • Suvrit Sra • Ali Jadbabaie
Keywords: Optimization - Non-convex • stochastic optimization
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Quadratically Regularized Subgradient Methods for Weakly Convex Optimization with Weakly Convex Constraints
Runchao Ma • Qihang Lin • Tianbao Yang
Keywords: Optimization - Non-convex
PDF Bib Video Supplement
Accelerated Stochastic Gradient-free and Projection-free Methods
Feihu Huang • Lue Tao • Songcan Chen
Keywords: Optimization - Non-convex • Adversarial Examples
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SGD Learns One-Layer Networks in WGANs
Qi Lei • Jason Lee • Alex Dimakis • Constantinos Daskalakis
Keywords: Optimization - Non-convex • Deep Learning - Theory • Learning Theory
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Understanding the Impact of Model Incoherence on Convergence of Incremental SGD with Random Reshuffle
Shaocong Ma • Yi Zhou
Keywords: Optimization - Non-convex • Deep Learning - General
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Robust One-Bit Recovery via ReLU Generative Networks: Near-Optimal Statistical Rate and Global Landscape Analysis
Shuang Qiu • Xiaohan Wei • Zhuoran Yang
Keywords: Optimization - Non-convex • Deep Learning - General
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Stochastic Gauss-Newton Algorithms for Nonconvex Compositional Optimization
Quoc Tran-Dinh • Nhan H. Pham • Lam M. Nguyen
Keywords: Optimization - Non-convex • Learning Theory
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Stochastic Hamiltonian Gradient Methods for Smooth Games
Nicolas Loizou • Hugo Berard • Alexia Jolicoeur-Martineau • Pascal Vincent • Simon Lacoste-Julien • Ioannis Mitliagkas
Keywords: Optimization - Non-convex • Deep Learning - General • Learning Theory • Optimization - Convex
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Stochastic Flows and Geometric Optimization on the Orthogonal Group
Krzysztof Choromanski • David Cheikhi • Jared Q Davis • Valerii Likhosherstov • Achille Nazaret • Achraf Bahamou • Xingyou Song • Mrugank Akarte • Jack Parker-Holder • Jacob Bergquist • Yuan Gao • Aldo Pacchiano • Tamas Sarlos • Adrian Weller • Vikas Sindhwani
Keywords: Optimization - Non-convex • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods
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Convergence of a Stochastic Gradient Method with Momentum for Non-Smooth Non-Convex Optimization
Vien Van Mai • Mikael Johansson
Keywords: Optimization - Non-convex • Deep Learning - General • Optimization - Convex
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History-Gradient Aided Batch Size Adaptation for Variance Reduced Algorithms
Kaiyi Ji • Zhe Wang • Bowen Weng • Yi Zhou • Wei Zhang • Yingbin Liang
Keywords: Optimization - Non-convex
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Min-Max Optimization without Gradients: Convergence and Applications to Black-Box Evasion and Poisoning Attacks
Sijia Liu • Songtao Lu • Xiangyi Chen • Yao Feng • Kaidi Xu • Abdullah Al-Dujaili • Mingyi Hong • Una-May O'Reilly
Keywords: Optimization - Non-convex • Adversarial Examples
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Momentum Improves Normalized SGD
Ashok Cutkosky • Harsh Mehta
Keywords: Optimization - Non-convex • Deep Learning - General
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Stochastic Optimization for Non-convex Inf-Projection Problems
Yan Yan • Yi Xu • Lijun Zhang • Wang Xiaoyu • Tianbao Yang
Keywords: Optimization - Non-convex • Optimization - Large Scale, Parallel and Distributed
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Improving the Sample and Communication Complexity for Decentralized Non-Convex Optimization: Joint Gradient Estimation and Tracking
Haoran Sun • Songtao Lu • Mingyi Hong
Keywords: Optimization - Non-convex • Optimization - Large Scale, Parallel and Distributed
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The Complexity of Finding Stationary Points with Stochastic Gradient Descent
Yoel Drori • Ohad Shamir
Keywords: Optimization - Non-convex • Learning Theory
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A Nearly-Linear Time Algorithm for Exact Community Recovery in Stochastic Block Model
Peng Wang • Zirui Zhou • Anthony Man-Cho So
Keywords: Optimization - Non-convex • Unsupervised and Semi-Supervised Learning • Optimization - General
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A Generic First-Order Algorithmic Framework for Bi-Level Programming Beyond Lower-Level Singleton
Risheng Liu • Pan Mu • Xiaoming Yuan • Shangzhi Zeng • Jin Zhang
Keywords: Optimization - Non-convex
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What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization?
Chi Jin • Praneeth Netrapalli • Michael Jordan
Keywords: Optimization - Non-convex
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Uncertainty quantification for nonconvex tensor completion: Confidence intervals, heteroscedasticity and optimality
Changxiao Cai • H. Vincent Poor • Yuxin Chen
Keywords: Optimization - Non-convex • General Machine Learning Techniques • Learning Theory • Optimization - General
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Planning, Control, and Multiagent Learning
Low-Variance and Zero-Variance Baselines for Extensive-Form Games
Trevor Davis • Martin Schmid • Michael Bowling
Keywords: Planning, Control, and Multiagent Learning • Learning Theory
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Generative Adversarial Imitation Learning with Neural Network Parameterization: Global Optimality and Convergence Rate
Yufeng Zhang • Qi Cai • Zhuoran Yang • Zhaoran Wang
Keywords: Planning, Control, and Multiagent Learning • Imitation Learning
PDF Bib Video Supplement
Kernel Methods for Cooperative Multi-Agent Contextual Bandits
Abhimanyu Dubey • Alex `Sandy' Pentland
Keywords: Planning, Control, and Multiagent Learning • Applications - Other • Applications - Other • Online Learning, Active Learning, and Bandits
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Task-Oriented Active Perception and Planning in Environments with Partially Known Semantics
Mahsa Ghasemi • Erdem A Bulgur • Ufuk Topcu
Keywords: Planning, Control, and Multiagent Learning
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Cooperative Multi-Agent Bandits with Heavy Tails
Abhimanyu Dubey • Alex `Sandy' Pentland
Keywords: Planning, Control, and Multiagent Learning • Applications - Other • Online Learning, Active Learning, and Bandits • Trustworthy Machine Learning
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Boosting for Control of Dynamical Systems
Naman Agarwal • Nataly Brukhim • Elad Hazan • Zhou Lu
Keywords: Planning, Control, and Multiagent Learning • Supervised Learning • Online Learning, Active Learning, and Bandits
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ROMA: Multi-Agent Reinforcement Learning with Emergent Roles
Tonghan Wang • Heng Dong • Victor Lesser • Chongjie Zhang
Keywords: Planning, Control, and Multiagent Learning • Reinforcement Learning - Deep RL
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CoMic: Complementary Task Learning & Mimicry for Reusable Skills
Leonard Hasenclever • Fabio Pardo • Raia Hadsell • Nicolas Heess • Josh Merel
Keywords: Planning, Control, and Multiagent Learning • Reinforcement Learning - Deep RL • Transfer, Multitask and Meta-learning
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Symbolic Network: Generalized Neural Policies for Relational MDPs
Sankalp Garg • Aniket Bajpai • Mausam
Keywords: Planning, Control, and Multiagent Learning • Reinforcement Learning - Deep RL • Sequential, Network, and Time-Series Modeling • Transfer, Multitask and Meta-learning
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Hallucinative Topological Memory for Zero-Shot Visual Planning
Kara Liu • Thanard Kurutach • Christine Tung • Pieter Abbeel • Aviv Tamar
Keywords: Planning, Control, and Multiagent Learning • Applications - Other • Reinforcement Learning - Deep RL • Unsupervised and Semi-Supervised Learning
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Q-value Path Decomposition for Deep Multiagent Reinforcement Learning
Yaodong Yang • Jianye Hao • Guangyong Chen • Hongyao Tang • Yingfeng Chen • Yujing Hu • Changjie Fan • Zhongyu Wei
Keywords: Planning, Control, and Multiagent Learning • Reinforcement Learning - Deep RL
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Optimally Solving Two-Agent Decentralized POMDPs Under One-Sided Information Sharing
Yuxuan Xie • Jilles Steeve Dibangoye • Olivier Buffet
Keywords: Planning, Control, and Multiagent Learning • Planning, Control, and Multiagent Learning • Reinforcement Learning - General
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Multi-Agent Determinantal Q-Learning
Yaodong Yang • Ying Wen • Jun Wang • Liheng Chen • Kun Shao • David H Mguni • Weinan Zhang
Keywords: Planning, Control, and Multiagent Learning • Reinforcement Learning - General
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“Other-Play” for Zero-Shot Coordination
Hengyuan Hu • Alex Peysakhovich • Adam Lerer • Jakob Foerster
Keywords: Planning, Control, and Multiagent Learning • Reinforcement Learning - General
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Privacy-preserving Statistics and Machine Learning
Context Aware Local Differential Privacy
Jayadev Acharya • Keith Bonawitz • Peter Kairouz • Daniel Ramage • Ziteng Sun
Keywords: Privacy-preserving Statistics and Machine Learning • Learning Theory • Learning Theory
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Oracle Efficient Private Non-Convex Optimization
Seth Neel • Aaron Roth • Giuseppe Vietri • Steven Wu
Keywords: Privacy-preserving Statistics and Machine Learning • Learning Theory • Learning Theory • Optimization - Non-convex
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InstaHide: Instance-hiding Schemes for Private Distributed Learning
Yangsibo Huang • Zhao Song • Kai Li • Sanjeev Arora
Keywords: Privacy-preserving Statistics and Machine Learning • Deep Learning - Theory
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Privately detecting changes in unknown distributions
Rachel Cummings • Sara Krehbiel • Yuliia Lut • Wanrong Zhang
Keywords: Privacy-preserving Statistics and Machine Learning • Learning Theory
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Certified Data Removal from Machine Learning Models
Chuan Guo • Tom Goldstein • Awni Hannun • Laurens van der Maaten
Keywords: Privacy-preserving Statistics and Machine Learning
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An end-to-end Differentially Private Latent Dirichlet Allocation Using a Spectral Algorithm
Chris DeCarolis • Mukul Ram • Seyed A Esmaeili • Yu-Xiang Wang • Furong Huang
Keywords: Privacy-preserving Statistics and Machine Learning • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods
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On Differentially Private Stochastic Convex Optimization with Heavy-tailed Data
Di Wang • Hanshen Xiao • Srinivas Devadas • Jinhui Xu
Keywords: Privacy-preserving Statistics and Machine Learning
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Radioactive data: tracing through training
Alexandre Sablayrolles • Matthijs Douze • Cordelia Schmid • Hervé Jégou
Keywords: Privacy-preserving Statistics and Machine Learning • Accountability, Transparency and Interpretability
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New Oracle-Efficient Algorithms for Private Synthetic Data Release
Giuseppe Vietri • Grace M Tian • Mark Bun • Thomas Steinke • Steven Wu
Keywords: Privacy-preserving Statistics and Machine Learning • Learning Theory
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Data-Dependent Differentially Private Parameter Learning for Directed Graphical Models
Amrita Roy Chowdhury • Theodoros Rekatsinas • Somesh Jha
Keywords: Privacy-preserving Statistics and Machine Learning • Probabilistic Inference - Models and Probabilistic Programming
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Scalable Differential Privacy with Certified Robustness in Adversarial Learning
Hai Phan • My T. Thai • Han Hu • Ruoming Jin • Tong Sun • Dejing Dou
Keywords: Privacy-preserving Statistics and Machine Learning • Adversarial Examples • Trustworthy Machine Learning
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Alleviating Privacy Attacks via Causal Learning
Shruti Tople • Amit Sharma • Aditya Nori
Keywords: Privacy-preserving Statistics and Machine Learning • Causality
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Fast and Private Submodular and $k$-Submodular Functions Maximization with Matroid Constraints
Akbar Rafiey • Yuichi Yoshida
Keywords: Privacy-preserving Statistics and Machine Learning • Optimization - General
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Privately Learning Markov Random Fields
Huanyu Zhang • Gautam Kamath • Janardhan D Kulkarni • Steven Wu
Keywords: Privacy-preserving Statistics and Machine Learning • Learning Theory • Learning Theory • Probabilistic Inference - Models and Probabilistic Programming
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Optimal Differential Privacy Composition for Exponential Mechanisms
Jinshuo Dong • David Durfee • Ryan Rogers
Keywords: Privacy-preserving Statistics and Machine Learning • Applications - Other
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From Local SGD to Local Fixed-Point Methods for Federated Learning
Grigory Malinovskiy • Dmitry Kovalev • Elnur Gasanov • Laurent CONDAT • Peter Richtarik
Keywords: Privacy-preserving Statistics and Machine Learning • Optimization - General • Optimization - Large Scale, Parallel and Distributed
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Differentially Private Set Union
Sivakanth Gopi • Pankaj Gulhane • Janardhan Kulkarni • Judy Hanwen Shen • Milad Shokouhi • Sergey Yekhanin
Keywords: Privacy-preserving Statistics and Machine Learning • Differential Privacy
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Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead
Badih Ghazi • Ravi Kumar • Pasin Manurangsi • Rasmus Pagh
Keywords: Privacy-preserving Statistics and Machine Learning
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Bayesian Differential Privacy for Machine Learning
Aleksei Triastcyn • Boi Faltings
Keywords: Privacy-preserving Statistics and Machine Learning • General Machine Learning Techniques • Trustworthy Machine Learning
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Sharp Composition Bounds for Gaussian Differential Privacy via Edgeworth Expansion
Qinqing Zheng • Jinshuo Dong • Qi Long • Weijie Su
Keywords: Privacy-preserving Statistics and Machine Learning
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Private Query Release Assisted by Public Data
Raef Bassily • Albert Cheu • Shay Moran • Aleksandar Nikolov • Jonathan Ullman • Steven Wu
Keywords: Privacy-preserving Statistics and Machine Learning • Learning Theory
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(Locally) Differentially Private Combinatorial Semi-Bandits
Xiaoyu Chen • Kai Zheng • Zixin Zhou • Yunchang Yang • Wei Chen • Liwei Wang
Keywords: Privacy-preserving Statistics and Machine Learning • Online Learning, Active Learning, and Bandits
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Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods
Non-convex Learning via Replica Exchange Stochastic Gradient MCMC
Wei Deng • Qi Feng • Liyao Gao • Faming Liang • Guang Lin
Keywords: Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods • Deep Learning - Algorithms • Deep Learning - General • Deep Learning - General
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On the (In)tractability of Computing Normalizing Constants for the Product of Determinantal Point Processes
Naoto Ohsaka • Tatsuya Matsuoka
Keywords: Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods
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Batch Stationary Distribution Estimation
Junfeng Wen • Bo Dai • Lihong Li • Dale Schuurmans
Keywords: Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods • Reinforcement Learning - General
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From Sets to Multisets: Provable Variational Inference for Probabilistic Integer Submodular Models
Aytunc Sahin • Yatao Bian • Joachim Buhmann • Andreas Krause
Keywords: Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods • Optimization - Non-convex
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Error Estimation for Sketched SVD via the Bootstrap
Miles E Lopes • N. Benjamin Erichson • Michael Mahoney
Keywords: Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods
PDF Bib Video Supplement
Double-Loop Unadjusted Langevin Algorithm
Paul Rolland • Armin Eftekhari • Ali Kavis • Volkan Cevher
Keywords: Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods
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Undirected Graphical Models as Approximate Posteriors
Arash Vahdat • Evgeny Andriyash • William G. Macready
Keywords: Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods • Deep Learning - Generative Models and Autoencoders • Probabilistic Inference - Models and Probabilistic Programming • Probabilistic Inference - Models and Probabilistic Programming
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On Contrastive Learning for Likelihood-free Inference
Conor Durkan • Iain Murray • George Papamakarios
Keywords: Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods • Deep Learning - General • Deep Learning - Generative Models and Autoencoders • Probabilistic Inference - Models and Probabilistic Programming
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Likelihood-free MCMC with Amortized Approximate Ratio Estimators
Joeri Hermans • Volodimir Begy • Gilles Louppe
Keywords: Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods • Deep Learning - General • Probabilistic Inference - Models and Probabilistic Programming • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods
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Stochastic Gradient and Langevin Processes
Xiang Cheng • Dong Yin • Peter Bartlett • Michael Jordan
Keywords: Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods • Deep Learning - General • Gaussian Processes
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Variational Inference for Sequential Data with Future Likelihood Estimates
Geon-Hyeong Kim • Youngsoo Jang • Hongseok Yang • Kee-Eung Kim
Keywords: Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods • Probabilistic Inference - Models and Probabilistic Programming
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Accelerating the diffusion-based ensemble sampling by non-reversible dynamics
Futoshi Futami • Issei Sato • Masashi Sugiyama
Keywords: Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods
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Black-Box Variational Inference as a Parametric Approximation to Langevin Dynamics
Matthew D Hoffman • Yian Ma
Keywords: Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods • Probabilistic Inference - Models and Probabilistic Programming • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods
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Meta-learning for Mixed Linear Regression
Weihao Kong • Raghav Somani • Zhao Song • Sham Kakade • Sewoong Oh
Keywords: Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods • Transfer, Multitask and Meta-learning • Transfer, Multitask and Meta-learning • Probabilistic Inference - Models and Probabilistic Programming
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Spectral Subsampling MCMC for Stationary Time Series
Robert Salomone • Matias Quiroz • Robert Kohn • Mattias Villani • Minh-Ngoc Tran
Keywords: Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods • Probabilistic Inference - Models and Probabilistic Programming • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods
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Amortized Population Gibbs Samplers with Neural Sufficient Statistics
Hao Wu • Heiko Zimmermann • Eli Sennesh • Tuan Anh Le • Jan-Willem van de Meent
Keywords: Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods • Deep Learning - Generative Models and Autoencoders • Unsupervised and Semi-Supervised Learning • Probabilistic Inference - Models and Probabilistic Programming
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Provable Smoothness Guarantees for Black-Box Variational Inference
Justin Domke
Keywords: Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods
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Faster Graph Embeddings via Coarsening
Matthew Fahrbach • Gramoz Goranci • Richard Peng • Sushant Sachdeva • Chi Wang
Keywords: Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods • Unsupervised and Semi-Supervised Learning • Unsupervised and Semi-Supervised Learning • Optimization - Large Scale, Parallel and Distributed
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Involutive MCMC: a Unifying Framework
Kirill Neklyudov • Max Welling • Evgenii Egorov • Dmitry P Vetrov
Keywords: Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods
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Variance Reduction and Quasi-Newton for Particle-Based Variational Inference
Michael H. Zhu • Chang Liu • Jun Zhu
Keywords: Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods
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Handling the Positive-Definite Constraint in the Bayesian Learning Rule
Wu Lin • Mark Schmidt • Mohammad Emtiyaz Khan
Keywords: Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods
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Model Fusion with Kullback--Leibler Divergence
Sebastian Claici • Mikhail Yurochkin • Soumya Ghosh • Justin M Solomon
Keywords: Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods • Optimization - Large Scale, Parallel and Distributed • Probabilistic Inference - Models and Probabilistic Programming
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Spectral Graph Matching and Regularized Quadratic Relaxations: Algorithm and Theory
Zhou Fan • Cheng Mao • Yihong Wu • Jiaming Xu
Keywords: Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods • Sequential, Network, and Time-Series Modeling • Optimization - Convex
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Estimating the Error of Randomized Newton Methods: A Bootstrap Approach
Miles E Lopes • Jessie X.T. Chen
Keywords: Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods • Optimization - Convex • Optimization - Large Scale, Parallel and Distributed
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The Boomerang Sampler
Joris Bierkens • Sebastiano Grazzi • Kengo Kamatani • Gareth Roberts
Keywords: Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods • Sequential, Network, and Time-Series Modeling • Probabilistic Inference - Models and Probabilistic Programming
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Probabilistic Inference - Models and Probabilistic Programming
Online Bayesian Moment Matching based SAT Solver Heuristics
Haonan Duan • Saeed Nejati • George Trimponias • Pascal Poupart • Vijay Ganesh
Keywords: Probabilistic Inference - Models and Probabilistic Programming • Online Learning, Active Learning, and Bandits • General Machine Learning Techniques
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Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting
Niccolo Dalmasso • Rafael Izbicki • Ann Lee
Keywords: Probabilistic Inference - Models and Probabilistic Programming • Learning Theory • Learning Theory • Learning Theory
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Recurrent Hierarchical Topic-Guided RNN for Language Generation
Dandan Guo • Bo Chen • Ruiying Lu • Mingyuan Zhou
Keywords: Probabilistic Inference - Models and Probabilistic Programming • Deep Learning - Generative Models and Autoencoders • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods
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Automatic Reparameterisation of Probabilistic Programs
Maria I Gorinova • Dave Moore • Matthew D Hoffman
Keywords: Probabilistic Inference - Models and Probabilistic Programming • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods
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The Usual Suspects? Reassessing Blame for VAE Posterior Collapse
Bin Dai • Ziyu Wang • David Wipf
Keywords: Probabilistic Inference - Models and Probabilistic Programming • Deep Learning - Generative Models and Autoencoders • Deep Learning - Generative Models and Autoencoders • Probabilistic Inference - Models and Probabilistic Programming
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Accelerated Message Passing for Entropy-Regularized MAP Inference
Jonathan Lee • Aldo Pacchiano • Peter Bartlett • Michael Jordan
Keywords: Probabilistic Inference - Models and Probabilistic Programming • Optimization - General • Optimization - Convex
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Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits
Robert Peharz • Steven Lang • Antonio Vergari • Karl Stelzner • Alejandro Molina • Martin Trapp • Guy Van den Broeck • Kristian Kersting • Zoubin Ghahramani
Keywords: Probabilistic Inference - Models and Probabilistic Programming • Probabilistic Inference - Models and Probabilistic Programming
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All in the Exponential Family: Bregman Duality in Thermodynamic Variational Inference
Rob Brekelmans • Vaden W Masrani • Frank Wood • Greg Ver Steeg • Aram Galstyan
Keywords: Probabilistic Inference - Models and Probabilistic Programming • Deep Learning - Generative Models and Autoencoders • Unsupervised and Semi-Supervised Learning
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Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling
Will Grathwohl • Kuan-Chieh Wang • Jörn-Henrik Jacobsen • David Duvenaud • Richard Zemel
Keywords: Probabilistic Inference - Models and Probabilistic Programming • Deep Learning - Generative Models and Autoencoders • Learning Theory
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Empirical Study of the Benefits of Overparameterization in Learning Latent Variable Models
Rares-Darius Buhai • Yoni Halpern • Yoon Kim • Andrej Risteski • David Sontag
Keywords: Probabilistic Inference - Models and Probabilistic Programming • Unsupervised and Semi-Supervised Learning • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods
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Continuous Time Bayesian Networks with Clocks
Nicolai Engelmann • Dominik Linzner • Heinz Koeppl
Keywords: Probabilistic Inference - Models and Probabilistic Programming • Probabilistic Inference - Models and Probabilistic Programming
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Energy-Based Processes for Exchangeable Data
Mengjiao Yang • Bo Dai • Hanjun Dai • Dale Schuurmans
Keywords: Probabilistic Inference - Models and Probabilistic Programming • Deep Learning - Generative Models and Autoencoders • Representation Learning
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Computational and Statistical Tradeoffs in Inferring Combinatorial Structures of Ising Model
Ying Jin • Zhaoran Wang • Junwei Lu
Keywords: Probabilistic Inference - Models and Probabilistic Programming • Learning Theory • Learning Theory
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The continuous categorical: a novel simplex-valued exponential family
Elliott Gordon-Rodriguez • Gabriel Loaiza-Ganem • John Cunningham
Keywords: Probabilistic Inference - Models and Probabilistic Programming • Deep Learning - Generative Models and Autoencoders • Deep Learning - Generative Models and Autoencoders • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods
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Thompson Sampling via Local Uncertainty
Zhendong Wang • Mingyuan Zhou
Keywords: Probabilistic Inference - Models and Probabilistic Programming • Deep Learning - General • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods
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Fractal Gaussian Networks: A sparse random graph model based on Gaussian Multiplicative Chaos
Subhroshekhar Ghosh • Krishna Balasubramanian • Xiaochuan Yang
Keywords: Probabilistic Inference - Models and Probabilistic Programming • Applications - Other • Applications - Other
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Multi-objective Bayesian Optimization using Pareto-frontier Entropy
Shinya Suzuki • Shion Takeno • Tomoyuki Tamura • Kazuki Shitara • Masayuki Karasuyama
Keywords: Probabilistic Inference - Models and Probabilistic Programming • Optimization - Non-convex • Gaussian Processes
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How to Train Your Neural ODE: the World of Jacobian and Kinetic Regularization
Chris Finlay • Joern-Henrik Jacobsen • Levon Nurbekyan • Adam Oberman
Keywords: Probabilistic Inference - Models and Probabilistic Programming • Deep Learning - General • Unsupervised and Semi-Supervised Learning • Learning Theory
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Fast and Three-rious: Speeding Up Weak Supervision with Triplet Methods
Daniel Y Fu • Mayee Chen • Frederic Sala • Sarah Hooper • Kayvon Fatahalian • Christopher Re
Keywords: Probabilistic Inference - Models and Probabilistic Programming • Online Learning, Active Learning, and Bandits
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On Semi-parametric Inference for BART
Veronika Rocková
Keywords: Probabilistic Inference - Models and Probabilistic Programming • Probabilistic Inference - Models and Probabilistic Programming
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Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation
Steven Kleinegesse • Michael U. Gutmann
Keywords: Probabilistic Inference - Models and Probabilistic Programming
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Stochastic Differential Equations with Variational Wishart Diffusions
Martin Jørgensen • Marc Deisenroth • Hugh Salimbeni
Keywords: Probabilistic Inference - Models and Probabilistic Programming • Sequential, Network, and Time-Series Modeling • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods • Gaussian Processes
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Scaling up Hybrid Probabilistic Inference with Logical and Arithmetic Constraints via Message Passing
Zhe Zeng • Paolo Morettin • Fanqi Yan • Antonio Vergari • Guy Van den Broeck
Keywords: Probabilistic Inference - Models and Probabilistic Programming • Sequential, Network, and Time-Series Modeling • Probabilistic Inference - Models and Probabilistic Programming • Probabilistic Inference - Models and Probabilistic Programming
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Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support
Yuan Zhou • Hongseok Yang • Yee Whye Teh • Tom Rainforth
Keywords: Probabilistic Inference - Models and Probabilistic Programming • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods • Probabilistic Inference - Models and Probabilistic Programming • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods
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Multi-fidelity Bayesian Optimization with Max-value Entropy Search and its Parallelization
Shion Takeno • Hitoshi Fukuoka • Yuhki Tsukada • Toshiyuki Koyama • Motoki Shiga • Ichiro Takeuchi • Masayuki Karasuyama
Keywords: Probabilistic Inference - Models and Probabilistic Programming • Optimization - Non-convex • Gaussian Processes
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Sequential Cooperative Bayesian Inference
Junqi Wang • Pei Wang • Patrick Shafto
Keywords: Probabilistic Inference - Models and Probabilistic Programming • Planning, Control, and Multiagent Learning
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Optimal transport mapping via input convex neural networks
Ashok V Makkuva • Amirhossein Taghvaei • Sewoong Oh • Jason Lee
Keywords: Probabilistic Inference - Models and Probabilistic Programming • Deep Learning - Generative Models and Autoencoders
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Reinforcement Learning - Deep RL
Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot Control
Jie Xu • Yunsheng Tian • Pingchuan Ma • Daniela Rus • Shinjiro Sueda • Wojciech Matusik
Keywords: Reinforcement Learning - Deep RL • Applications - Other • Deep Learning - General • Transfer, Multitask and Meta-learning
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Responsive Safety in Reinforcement Learning by PID Lagrangian Methods
Adam Stooke • Joshua Achiam • Pieter Abbeel
Keywords: Reinforcement Learning - Deep RL • Optimization - General • Fairness, Equity, Justice, and Safety
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Learning Compound Tasks without Task-specific Knowledge via Imitation and Self-supervised Learning
Sang-Hyun Lee • Seung-Woo Seo
Keywords: Reinforcement Learning - Deep RL • Applications - Other • Deep Learning - General
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What Can Learned Intrinsic Rewards Capture?
Zeyu Zheng • Junhyuk Oh • Matteo Hessel • Zhongwen Xu • Manuel Kroiss • Hado van Hasselt • David Silver • Satinder Singh
Keywords: Reinforcement Learning - Deep RL • Reinforcement Learning - General
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A Game Theoretic Framework for Model Based Reinforcement Learning
Aravind Rajeswaran • Igor Mordatch • Vikash Kumar
Keywords: Reinforcement Learning - Deep RL • Applications - Other
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Variational Imitation Learning with Diverse-quality Demonstrations
Voot Tangkaratt • Bo Han • Mohammad Emtiyaz Khan • Masashi Sugiyama
Keywords: Reinforcement Learning - Deep RL • Planning, Control, and Multiagent Learning • Reinforcement Learning - General
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Evolutionary Reinforcement Learning for Sample-Efficient Multiagent Coordination
Somdeb Majumdar • Shauharda Khadka • Santiago Miret • Stephen Mcaleer • Kagan Tumer
Keywords: Reinforcement Learning - Deep RL • Planning, Control, and Multiagent Learning • Applications - Other • Reinforcement Learning - General
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Implicit Generative Modeling for Efficient Exploration
Neale Ratzlaff • Qinxun Bai • Li Fuxin • Wei Xu
Keywords: Reinforcement Learning - Deep RL • Deep Learning - General • Reinforcement Learning - General
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Generalization to New Actions in Reinforcement Learning
Ayush Jain • Andrew Szot • Joseph J. Lim
Keywords: Reinforcement Learning - Deep RL • Representation Learning • Transfer, Multitask and Meta-learning • Unsupervised and Semi-Supervised Learning
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Learning Fair Policies in Multi-Objective (Deep) Reinforcement Learning with Average and Discounted Rewards
Umer Siddique • Paul Weng • Matthieu Zimmer
Keywords: Reinforcement Learning - Deep RL • Reinforcement Learning - General • Reinforcement Learning - Theory • Fairness, Equity, Justice, and Safety
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Context-aware Dynamics Model for Generalization in Model-Based Reinforcement Learning
Kimin Lee • Younggyo Seo • Seunghyun Lee • Honglak Lee • Jinwoo Shin
Keywords: Reinforcement Learning - Deep RL
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Intrinsic Reward Driven Imitation Learning via Generative Model
Xingrui Yu • Yueming LYU • Ivor Tsang
Keywords: Reinforcement Learning - Deep RL
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Leveraging Procedural Generation to Benchmark Reinforcement Learning
Karl w Cobbe • Chris Hesse • Jacob Hilton • John Schulman
Keywords: Reinforcement Learning - Deep RL • Reinforcement Learning - General • Transfer, Multitask and Meta-learning
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A distributional view on multi-objective policy optimization
Abbas Abdolmaleki • Sandy H Huang • Leonard Hasenclever • Michael Neunert • Francis Song • Martina Zambelli • Murilo Martins • Nicolas Heess • Raia Hadsell • Martin Riedmiller
Keywords: Reinforcement Learning - Deep RL • Applications - Other
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Deep Coordination Graphs
Wendelin Boehmer • Vitaly Kurin • Shimon Whiteson
Keywords: Reinforcement Learning - Deep RL • Planning, Control, and Multiagent Learning
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OPtions as REsponses: Grounding behavioural hierarchies in multi-agent reinforcement learning
Alexander Vezhnevets • Yuhuai Wu • Maria Eckstein • Rémi Leblond • Joel Z Leibo
Keywords: Reinforcement Learning - Deep RL • Planning, Control, and Multiagent Learning • Reinforcement Learning - General
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An Optimistic Perspective on Offline Deep Reinforcement Learning
Rishabh Agarwal • Dale Schuurmans • Mohammad Norouzi
Keywords: Reinforcement Learning - Deep RL • Batch Reinforcement Learning
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CURL: Contrastive Unsupervised Representations for Reinforcement Learning
Michael Laskin • Pieter Abbeel • Aravind Srinivas
Keywords: Reinforcement Learning - Deep RL • Deep Learning - Algorithms • Representation Learning
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Revisiting Fundamentals of Experience Replay
William Fedus • Prajit Ramachandran • Rishabh Agarwal • Yoshua Bengio • Hugo Larochelle • Mark Rowland • Will Dabney
Keywords: Reinforcement Learning - Deep RL • Deep Learning - General • Reinforcement Learning - General
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Agent57: Outperforming the Atari Human Benchmark
Adrià Puigdomènech Badia • Bilal Piot • Steven Kapturowski • Pablo Sprechmann • Alex Vitvitskyi • Zhaohan Guo • Charles Blundell
Keywords: Reinforcement Learning - Deep RL • Reinforcement Learning - General
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Off-Policy Actor-Critic with Shared Experience Replay
Simon Schmitt • Matteo Hessel • Karen Simonyan
Keywords: Reinforcement Learning - Deep RL • Reinforcement Learning - General
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Stabilizing Transformers for Reinforcement Learning
Emilio Parisotto • Francis Song • Jack Rae • Razvan Pascanu • Caglar Gulcehre • Siddhant Jayakumar • Max Jaderberg • Raphaël Lopez Kaufman • Aidan Clark • Seb Noury • Matthew Botvinick • Nicolas Heess • Raia Hadsell
Keywords: Reinforcement Learning - Deep RL • Representation Learning • Sequential, Network, and Time-Series Modeling • Transfer, Multitask and Meta-learning
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Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences
Daniel S. Brown • Russell Coleman • Ravi Srinivasan • Scott Niekum
Keywords: Reinforcement Learning - Deep RL • Supervised Learning • Fairness, Equity, Justice, and Safety
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One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control
Wenlong Huang • Igor Mordatch • Deepak Pathak
Keywords: Reinforcement Learning - Deep RL • Planning, Control, and Multiagent Learning • Applications - Other • Reinforcement Learning - General
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Reinforcement Learning for Integer Programming: Learning to Cut
Yunhao Tang • Shipra Agrawal • Yuri Faenza
Keywords: Reinforcement Learning - Deep RL • Reinforcement Learning - General • Optimization - General
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Goal-Aware Prediction: Learning to Model What Matters
Suraj Nair • Silvio Savarese • Chelsea Finn
Keywords: Reinforcement Learning - Deep RL • Applications - Other
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Provably Efficient Model-based Policy Adaptation
Yuda Song • Aditi Mavalankar • Wen Sun • Sicun Gao
Keywords: Reinforcement Learning - Deep RL • Reinforcement Learning - General
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Hierarchically Decoupled Imitation For Morphological Transfer
Donald J Hejna • Lerrel Pinto • Pieter Abbeel
Keywords: Reinforcement Learning - Deep RL • Applications - Other
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Fast Adaptation to New Environments via Policy-Dynamics Value Functions
Roberta Raileanu • Max Goldstein • Arthur Szlam • Rob Fergus
Keywords: Reinforcement Learning - Deep RL • Transfer, Multitask and Meta-learning • Representation Learning • Transfer, Multitask and Meta-learning
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Ready Policy One: World Building Through Active Learning
Philip Ball • Jack Parker-Holder • Aldo Pacchiano • Krzysztof Choromanski • Stephen Roberts
Keywords: Reinforcement Learning - Deep RL • Online Learning, Active Learning, and Bandits
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Learning Human Objectives by Evaluating Hypothetical Behavior
Siddharth Reddy • Anca Dragan • Sergey Levine • Shane Legg • Jan Leike
Keywords: Reinforcement Learning - Deep RL
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Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning
Aleksei Petrenko • Zhehui Huang • Tushar Kumar • Gaurav Sukhatme • Vladlen Koltun
Keywords: Reinforcement Learning - Deep RL • General Machine Learning Techniques
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Learning What to Defer for Maximum Independent Sets
Sungsoo Ahn • Younggyo Seo • Jinwoo Shin
Keywords: Reinforcement Learning - Deep RL • Sequential, Network, and Time-Series Modeling • Optimization - General
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Taylor Expansion Policy Optimization
Yunhao Tang • Michal Valko • Remi Munos
Keywords: Reinforcement Learning - Deep RL • Reinforcement Learning - General
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Skew-Fit: State-Covering Self-Supervised Reinforcement Learning
Vitchyr H Pong • Murtaza Dalal • Steven Lin • Ashvin V Nair • Shikhar Bahl • Sergey Levine
Keywords: Reinforcement Learning - Deep RL • Applications - Other
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Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics
Arsenii Kuznetsov • Pavel Shvechikov • Alexander Grishin • Dmitry P Vetrov
Keywords: Reinforcement Learning - Deep RL
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Interference and Generalization in Temporal Difference Learning
Emmanuel Bengio • Joelle Pineau • Doina Precup
Keywords: Reinforcement Learning - Deep RL • Deep Learning - General • Reinforcement Learning - General
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Bidirectional Model-based Policy Optimization
Hang Lai • Jian Shen • Weinan Zhang • Yong Yu
Keywords: Reinforcement Learning - Deep RL • Reinforcement Learning - General
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Planning to Explore via Self-Supervised World Models
Ramanan Sekar • Oleh Rybkin • Kostas Daniilidis • Pieter Abbeel • Danijar Hafner • Deepak Pathak
Keywords: Reinforcement Learning - Deep RL • Planning, Control, and Multiagent Learning • Reinforcement Learning - General • Unsupervised and Semi-Supervised Learning
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Striving for Simplicity and Performance in Off-Policy DRL: Output Normalization and Non-Uniform Sampling
Che Wang • Yanqiu Wu • Quan Vuong • Keith W Ross
Keywords: Reinforcement Learning - Deep RL • Reinforcement Learning - General
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Maximum Entropy Gain Exploration for Long Horizon Multi-goal Reinforcement Learning
Silviu Pitis • Harris Chan • Stephen Zhao • Bradly C Stadie • Jimmy Ba
Keywords: Reinforcement Learning - Deep RL • Reinforcement Learning - General • Transfer, Multitask and Meta-learning
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Enhanced POET: Open-ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions
Rui Wang • Joel Lehman • Aditya Rawal • Jiale Zhi • Yulun Li • Jeffrey Clune • Kenneth O Stanley
Keywords: Reinforcement Learning - Deep RL • Deep Learning - General
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Working Memory Graphs
Ricky Loynd • Roland Fernandez • Asli Celikyilmaz • Adith Swaminathan • Matthew Hausknecht
Keywords: Reinforcement Learning - Deep RL • Deep Learning - General • Sequential, Network, and Time-Series Modeling
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Learning Efficient Multi-agent Communication: An Information Bottleneck Approach
Rundong Wang • Xu He • Runsheng Yu • Wei Qiu • Bo An • Zinovi Rabinovich
Keywords: Reinforcement Learning - Deep RL • Planning, Control, and Multiagent Learning
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Cautious Adaptation For Reinforcement Learning in Safety-Critical Settings
Jesse Zhang • Brian Cheung • Chelsea Finn • Sergey Levine • Dinesh Jayaraman
Keywords: Reinforcement Learning - Deep RL • Reinforcement Learning - General
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Growing Action Spaces
Gregory Farquhar • Laura Gustafson • Zeming Lin • Shimon Whiteson • Nicolas Usunier • Gabriel Synnaeve
Keywords: Reinforcement Learning - Deep RL • Planning, Control, and Multiagent Learning • Reinforcement Learning - General
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Explore, Discover and Learn: Unsupervised Discovery of State-Covering Skills
Victor Campos • Alexander Trott • Caiming Xiong • Richard Socher • Xavier Giro-i-Nieto • Jordi Torres
Keywords: Reinforcement Learning - Deep RL • Unsupervised and Semi-Supervised Learning
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Can Increasing Input Dimensionality Improve Deep Reinforcement Learning?
Kei Ota • Tomoaki Oiki • Devesh K. Jha • Toshisada Mariyama • Daniel Nikovski
Keywords: Reinforcement Learning - Deep RL • Reinforcement Learning - General
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Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning
Zhaohan Guo • Bernardo Avila Pires • Bilal Piot • Jean-Bastien Grill • Florent Altché • Remi Munos • Mohammad Gheshlaghi Azar
Keywords: Reinforcement Learning - Deep RL • Reinforcement Learning - General • Representation Learning
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Deep Reinforcement Learning with Smooth Policy
Qianli Shen • Yan Li • Haoming Jiang • Zhaoran Wang • Tuo Zhao
Keywords: Reinforcement Learning - Deep RL • Planning, Control, and Multiagent Learning • Reinforcement Learning - General • Unsupervised and Semi-Supervised Learning
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Reinforcement Learning - General
Representations for Stable Off-Policy Reinforcement Learning
Dibya Ghosh • Marc G. Bellemare
Keywords: Reinforcement Learning - General • Reinforcement Learning - Theory
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Safe Reinforcement Learning in Constrained Markov Decision Processes
Akifumi Wachi • Yanan Sui
Keywords: Reinforcement Learning - General • Planning, Control, and Multiagent Learning
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Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning
Lingxiao Wang • Zhuoran Yang • Zhaoran Wang
Keywords: Reinforcement Learning - General
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Lookahead-Bounded Q-learning
Ibrahim El Shar • Daniel Jiang
Keywords: Reinforcement Learning - General • Planning, Control, and Multiagent Learning
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Structured Policy Iteration for Linear Quadratic Regulator
Youngsuk Park • Ryan A. Rossi • Zheng Wen • Gang Wu • Handong Zhao
Keywords: Reinforcement Learning - General • Reinforcement Learning - Theory • Optimization - Convex
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Does the Markov Decision Process Fit the Data: Testing for the Markov Property in Sequential Decision Making
Chengchun Shi • Runzhe Wan • Rui Song • Wenbin Lu • Ling Leng
Keywords: Reinforcement Learning - General • Applications - Neuroscience, Cognitive Science, Biology and Health • Reinforcement Learning - Theory
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Momentum-Based Policy Gradient Methods
Feihu Huang • Shangqian Gao • Jian Pei • Heng Huang
Keywords: Reinforcement Learning - General • Reinforcement Learning - Theory
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Batch Reinforcement Learning with Hyperparameter Gradients
Byung-Jun Lee • Jongmin Lee • Peter Vrancx • Dongho Kim • Kee-Eung Kim
Keywords: Reinforcement Learning - General • Reinforcement Learning - Deep RL
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Learning Portable Representations for High-Level Planning
Steven James • Benjamin Rosman • George Konidaris
Keywords: Reinforcement Learning - General • Planning, Control, and Multiagent Learning • Representation Learning • Transfer, Multitask and Meta-learning
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Flexible and Efficient Long-Range Planning Through Curious Exploration
Aidan Curtis • Minjian Xin • Dilip Arumugam • Kevin Feigelis • Daniel Yamins
Keywords: Reinforcement Learning - General • Planning, Control, and Multiagent Learning • Applications - Other
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Asynchronous Coagent Networks
James Kostas • Chris Nota • Philip Thomas
Keywords: Reinforcement Learning - General • Reinforcement Learning - Theory
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Identifying the Reward Function by Anchor Actions
Sinong Geng • Houssam Nassif • Carlos Manzanares • Max Reppen • Ronnie Sircar
Keywords: Reinforcement Learning - General • Reinforcement Learning - Theory
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Discount Factor as a Regularizer in Reinforcement Learning
Ron Amit • Ron Meir • Kamil Ciosek
Keywords: Reinforcement Learning - General • Reinforcement Learning - Deep RL • Reinforcement Learning - Theory
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What can I do here? A Theory of Affordances in Reinforcement Learning
Khimya Khetarpal • Zafarali Ahmed • Gheorghe Comanici • David Abel • Doina Precup
Keywords: Reinforcement Learning - General • Planning, Control, and Multiagent Learning
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A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation
Pan Xu • Quanquan Gu
Keywords: Reinforcement Learning - General • Online Learning, Active Learning, and Bandits
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Domain Adaptive Imitation Learning
Kuno Kim • Yihong Gu • Jiaming Song • Shengjia Zhao • Stefano Ermon
Keywords: Reinforcement Learning - General • Transfer, Multitask and Meta-learning
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Reducing Sampling Error in Batch Temporal Difference Learning
Brahma S Pavse • Ishan P Durugkar • Josiah Hanna • Peter Stone
Keywords: Reinforcement Learning - General • Reinforcement Learning - Theory
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Sub-Goal Trees -- a Framework for Goal-Based Reinforcement Learning
Tom Jurgenson • Or Avner • Edward Groshev • Aviv Tamar
Keywords: Reinforcement Learning - General • Planning, Control, and Multiagent Learning • Deep Learning - Algorithms • Reinforcement Learning - Theory
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Policy Teaching via Environment Poisoning: Training-time Adversarial Attacks against Reinforcement Learning
Amin Rakhsha • Goran Radanovic • Rati Devidze • Xiaojin Zhu • Adish Singla
Keywords: Reinforcement Learning - General • Reinforcement Learning - Theory • Trustworthy Machine Learning
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Control Frequency Adaptation via Action Persistence in Batch Reinforcement Learning
Alberto Maria Metelli • Flavio Mazzolini • Lorenzo Bisi • Luca Sabbioni • Marcello Restelli
Keywords: Reinforcement Learning - General
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Tightening Exploration in Upper Confidence Reinforcement Learning
Hippolyte Bourel • Odalric Maillard • Mohammad Sadegh Talebi
Keywords: Reinforcement Learning - General • Reinforcement Learning - Theory • Learning Theory
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ConQUR: Mitigating Delusional Bias in Deep Q-Learning
DiJia Su • Jayden Ooi • Tyler Lu • Dale Schuurmans • Craig Boutilier
Keywords: Reinforcement Learning - General • Planning, Control, and Multiagent Learning • Reinforcement Learning - Deep RL • Reinforcement Learning - Theory
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FormulaZero: Distributionally Robust Online Adaptation via Offline Population Synthesis
Aman Sinha • Matthew O'Kelly • Hongrui Zheng • Rahul Mangharam • John Duchi • Russ Tedrake
Keywords: Reinforcement Learning - General • Planning, Control, and Multiagent Learning • Applications - Other • Online Learning, Active Learning, and Bandits
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Invariant Causal Prediction for Block MDPs
Amy Zhang • Clare Lyle • Shagun Sodhani • Angelos Filos • Marta Kwiatkowska • Joelle Pineau • Yarin Gal • Doina Precup
Keywords: Reinforcement Learning - General • Representation Learning • Reinforcement Learning - Theory • Causality
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Constrained Markov Decision Processes via Backward Value Functions
harsh satija • Philip Amortila • Joelle Pineau
Keywords: Reinforcement Learning - General • Reinforcement Learning - Deep RL
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Option Discovery in the Absence of Rewards with Manifold Analysis
Amitay Bar • Ronen Talmon • Ron Meir
Keywords: Reinforcement Learning - General • Representation Learning • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods
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Efficient Optimistic Exploration in Linear-Quadratic Regulators via Lagrangian Relaxation
Marc Abeille • Alessandro Lazaric
Keywords: Reinforcement Learning - General • Reinforcement Learning - Theory
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Estimating Q(s,s') with Deep Deterministic Dynamics Gradients
Ashley D. Edwards • Himanshu Sahni • Rosanne Liu • Jane Hung • Ankit Jain • Rui Wang • Adrien Ecoffet • Thomas Miconi • Charles Isbell • Jason Yosinski
Keywords: Reinforcement Learning - General • Reinforcement Learning - Deep RL
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Monte-Carlo Tree Search as Regularized Policy Optimization
Jean-Bastien Grill • Florent Altché • Yunhao Tang • Thomas Hubert • Michal Valko • Ioannis Antonoglou • Remi Munos
Keywords: Reinforcement Learning - General • Planning, Control, and Multiagent Learning
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Learning to Score Behaviors for Guided Policy Optimization
Aldo Pacchiano • Jack Parker-Holder • Yunhao Tang • Krzysztof Choromanski • Anna Choromanska • Michael Jordan
Keywords: Reinforcement Learning - General • Reinforcement Learning - Deep RL
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Private Reinforcement Learning with PAC and Regret Guarantees
Giuseppe Vietri • Borja Balle • Akshay Krishnamurthy • Steven Wu
Keywords: Reinforcement Learning - General • Privacy-preserving Statistics and Machine Learning
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No-Regret Exploration in Goal-Oriented Reinforcement Learning
Jean Tarbouriech • Evrard Garcelon • Michal Valko • Matteo Pirotta • Alessandro Lazaric
Keywords: Reinforcement Learning - General • Reinforcement Learning - Theory
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Accountable Off-Policy Evaluation With Kernel Bellman Statistics
Yihao Feng • Tongzheng Ren • Ziyang Tang • Qiang Liu
Keywords: Reinforcement Learning - General • Accountability, Transparency and Interpretability • Fairness, Equity, Justice, and Safety
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Sequential Transfer in Reinforcement Learning with a Generative Model
Andrea Tirinzoni • Riccardo Poiani • Marcello Restelli
Keywords: Reinforcement Learning - General • Transfer, Multitask and Meta-learning
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Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions
Michael Chang • Sid Kaushik • S. Matthew Weinberg • Tom Griffiths • Sergey Levine
Keywords: Reinforcement Learning - General • Learning Theory
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Evaluating the Performance of Reinforcement Learning Algorithms
Scott M Jordan • Yash Chandak • Daniel Cohen • Mengxue Zhang • Philip Thomas
Keywords: Reinforcement Learning - General • General Machine Learning Techniques
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Selective Dyna-style Planning Under Limited Model Capacity
Muhammad Zaheer • Samuel Sokota • Erin J. Talvitie • Martha White
Keywords: Reinforcement Learning - General
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Optimizing for the Future in Non-Stationary MDPs
Yash Chandak • Georgios Theocharous • Shiv Shankar • Martha White • Sridhar Mahadevan • Philip Thomas
Keywords: Reinforcement Learning - General
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Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions
Omer Gottesman • Joseph Futoma • Yao Liu • Sonali Parbhoo • Leo Celi • Emma Brunskill • Finale Doshi-Velez
Keywords: Reinforcement Learning - General • Applications - Neuroscience, Cognitive Science, Biology and Health • Accountability, Transparency and Interpretability
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Gradient Temporal-Difference Learning with Regularized Corrections
Sina Ghiassian • Andrew Patterson • Shivam Garg • Dhawal Gupta • Adam White • Martha White
Keywords: Reinforcement Learning - General
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Multi-step Greedy Reinforcement Learning Algorithms
Manan Tomar • Yonathan Efroni • Mohammad Ghavamzadeh
Keywords: Reinforcement Learning - General • Reinforcement Learning - Deep RL
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Reinforcement Learning - Theory
Learning Near Optimal Policies with Low Inherent Bellman Error
Andrea Zanette • Alessandro Lazaric • Mykel Kochenderfer • Emma Brunskill
Keywords: Reinforcement Learning - Theory • Reinforcement Learning - General
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Naive Exploration is Optimal for Online LQR
Max Simchowitz • Dylan Foster
Keywords: Reinforcement Learning - Theory • Online Learning, Active Learning, and Bandits
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GradientDICE: Rethinking Generalized Offline Estimation of Stationary Values
Shangtong Zhang • Bo Liu • Shimon Whiteson
Keywords: Reinforcement Learning - Theory • Reinforcement Learning - Deep RL
PDF Bib Video Supplement
Minimax Weight and Q-Function Learning for Off-Policy Evaluation
Masatoshi Uehara • Jiawei Huang • Nan Jiang
Keywords: Reinforcement Learning - Theory
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On the Global Convergence Rates of Softmax Policy Gradient Methods
Jincheng Mei • Chenjun Xiao • Csaba Szepesvári • Dale Schuurmans
Keywords: Reinforcement Learning - Theory
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Double Reinforcement Learning for Efficient and Robust Off-Policy Evaluation
Nathan Kallus • Masatoshi Uehara
Keywords: Reinforcement Learning - Theory • Reinforcement Learning - General • Causality
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Reward-Free Exploration for Reinforcement Learning
Chi Jin • Akshay Krishnamurthy • Max Simchowitz • Tiancheng Yu
Keywords: Reinforcement Learning - Theory
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On the Expressivity of Neural Networks for Deep Reinforcement Learning
Kefan Dong • Yuping Luo • Tianhe Yu • Chelsea Finn • Tengyu Ma
Keywords: Reinforcement Learning - Theory
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Reinforcement Learning in Feature Space: Matrix Bandit, Kernels, and Regret Bound
Lin Yang • Mengdi Wang
Keywords: Reinforcement Learning - Theory
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Optimistic Policy Optimization with Bandit Feedback
Lior Shani • Yonathan Efroni • Aviv Rosenberg • Shie Mannor
Keywords: Reinforcement Learning - Theory • Reinforcement Learning - General
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Provable Self-Play Algorithms for Competitive Reinforcement Learning
Yu Bai • Chi Jin
Keywords: Reinforcement Learning - Theory
PDF Bib Video Supplement
Logarithmic Regret for Adversarial Online Control
Dylan Foster • Max Simchowitz
Keywords: Reinforcement Learning - Theory • Online Learning, Active Learning, and Bandits
PDF Bib Video Supplement
Provably Convergent Two-Timescale Off-Policy Actor-Critic with Function Approximation
Shangtong Zhang • Bo Liu • Hengshuai Yao • Shimon Whiteson
Keywords: Reinforcement Learning - Theory • Reinforcement Learning - Deep RL
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Provable Representation Learning for Imitation Learning via Bi-level Optimization
Sanjeev Arora • Simon S. Du • Sham Kakade • Yuping Luo • Nikunj Saunshi
Keywords: Reinforcement Learning - Theory • Representation Learning
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Model-free Reinforcement Learning in Infinite-horizon Average-reward Markov Decision Processes
Chen-Yu Wei • Mehdi Jafarnia Jahromi • Haipeng Luo • Hiteshi Sharma • Rahul Jain
Keywords: Reinforcement Learning - Theory • Online Learning, Active Learning, and Bandits
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From Importance Sampling to Doubly Robust Policy Gradient
Jiawei Huang • Nan Jiang
Keywords: Reinforcement Learning - Theory
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Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning
Dipendra Misra • Mikael Henaff • Akshay Krishnamurthy • John Langford
Keywords: Reinforcement Learning - Theory • Reinforcement Learning - General
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Statistically Efficient Off-Policy Policy Gradients
Nathan Kallus • Masatoshi Uehara
Keywords: Reinforcement Learning - Theory
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Learning with Good Feature Representations in Bandits and in RL with a Generative Model
Tor Lattimore • Csaba Szepesvári • Gellért Weisz
Keywords: Reinforcement Learning - Theory • Reinforcement Learning - General • Representation Learning • Online Learning, Active Learning, and Bandits
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Understanding the Curse of Horizon in Off-Policy Evaluation via Conditional Importance Sampling
Yao Liu • Pierre-Luc Bacon • Emma Brunskill
Keywords: Reinforcement Learning - Theory • Reinforcement Learning - General
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Adaptive Estimator Selection for Off-Policy Evaluation
Yi Su • Pavithra Srinath • Akshay Krishnamurthy
Keywords: Reinforcement Learning - Theory • Applications - Other • Reinforcement Learning - General
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Logarithmic Regret for Learning Linear Quadratic Regulators Efficiently
Asaf B Cassel • Alon Cohen • Tomer Koren
Keywords: Reinforcement Learning - Theory • Online Learning, Active Learning, and Bandits
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Model-Based Reinforcement Learning with Value-Targeted Regression
Alex Ayoub • Zeyu Jia • Csaba Szepesvari • Mengdi Wang • Lin Yang
Keywords: Reinforcement Learning - Theory • Online Learning, Active Learning, and Bandits
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Learning Adversarial Markov Decision Processes with Bandit Feedback and Unknown Transition
Chi Jin • Tiancheng Jin • Haipeng Luo • Suvrit Sra • Tiancheng Yu
Keywords: Reinforcement Learning - Theory • Online Learning, Active Learning, and Bandits
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Representation Learning
Incidence Networks for Geometric Deep Learning
Marjan Albooyeh • Daniele Bertolini • Siamak Ravanbakhsh
Keywords: Representation Learning • Equivariance
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Learnable Group Transform For Time-Series
Romain Cosentino • Behnaam Aazhang
Keywords: Representation Learning • Sequential, Network, and Time-Series Modeling
PDF Bib Video Supplement
Constant Curvature Graph Convolutional Networks
Gregor Bachmann • Gary Becigneul • Octavian Ganea
Keywords: Representation Learning • Applications - Other • Deep Learning - General • Sequential, Network, and Time-Series Modeling
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Differentiating through the Fréchet Mean
Aaron Lou • Isay Katsman • Qingxuan Jiang • Serge Belongie • Ser-Nam Lim • Christopher De Sa
Keywords: Representation Learning • Deep Learning - General • Deep Learning - General • Optimization - Convex
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Contrastive Multi-View Representation Learning on Graphs
Kaveh Hassani • Amir Hosein Khasahmadi
Keywords: Representation Learning • Unsupervised and Semi-Supervised Learning
PDF Bib Video Supplement
Simple and Deep Graph Convolutional Networks
Ming Chen • Zhewei Wei • Zengfeng Huang • Bolin Ding • Yaliang Li
Keywords: Representation Learning
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Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere
Tongzhou Wang • Phillip Isola
Keywords: Representation Learning • Unsupervised and Semi-Supervised Learning • Learning Theory
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Semiparametric Nonlinear Bipartite Graph Representation Learning with Provable Guarantees
Sen Na • Yuwei Luo • Zhuoran Yang • Zhaoran Wang • Mladen Kolar
Keywords: Representation Learning • Optimization - Non-convex
PDF Bib Video Supplement
Learning and Evaluating Contextual Embedding of Source Code
Aditya Kanade • Petros Maniatis • Gogul Balakrishnan • Kensen Shi
Keywords: Representation Learning • Program understanding
PDF Bib Video Supplement
Graph Random Neural Features for Distance-Preserving Graph Representations
Daniele Zambon • Cesare Alippi • Lorenzo Livi
Keywords: Representation Learning • General Machine Learning Techniques • Sequential, Network, and Time-Series Modeling
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A Simple Framework for Contrastive Learning of Visual Representations
Ting Chen • Simon Kornblith • Mohammad Norouzi • Geoffrey Hinton
Keywords: Representation Learning • Deep Learning - Algorithms • Unsupervised and Semi-Supervised Learning • Unsupervised and Semi-Supervised Learning
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A Free-Energy Principle for Representation Learning
Yansong Gao • Pratik Chaudhari
Keywords: Representation Learning • Deep Learning - General • Transfer, Multitask and Meta-learning • Learning Theory
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On Variational Learning of Controllable Representations for Text without Supervision
Peng Xu • Jackie Chi Kit Cheung • Yanshuai Cao
Keywords: Representation Learning • Applications - Language, Speech and Dialog • Sequential, Network, and Time-Series Modeling • Unsupervised and Semi-Supervised Learning
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Automatic Shortcut Removal for Self-Supervised Representation Learning
Matthias Minderer • Olivier Bachem • Neil Houlsby • Michael Tschannen
Keywords: Representation Learning • Unsupervised and Semi-Supervised Learning
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Learning De-biased Representations with Biased Representations
Hyojin Bahng • Sanghyuk Chun • Sangdoo Yun • Jaegul Choo • Seong Joon Oh
Keywords: Representation Learning
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Nested Subspace Arrangement for Representation of Relational Data
Nozomi Hata • Shizuo Kaji • Akihiro Yoshida • Katsuki Fujisawa
Keywords: Representation Learning • Sequential, Network, and Time-Series Modeling
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Predictive Coding for Locally-Linear Control
Rui Shu • Tung D Nguyen • Yinlam Chow • Tuan Pham • Khoat Than • Mohammad Ghavamzadeh • Stefano Ermon • Hung Bui
Keywords: Representation Learning • Planning, Control, and Multiagent Learning • Deep Learning - Generative Models and Autoencoders • Reinforcement Learning - Deep RL
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Weakly-Supervised Disentanglement Without Compromises
Francesco Locatello • Ben Poole • Gunnar Rätsch • Bernhard Schölkopf • Olivier Bachem • Michael Tschannen
Keywords: Representation Learning • Deep Learning - Generative Models and Autoencoders
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Continuous Graph Neural Networks
Louis-Pascal A. C. Xhonneux • Meng Qu • Jian Tang
Keywords: Representation Learning • Applications - Other • Sequential, Network, and Time-Series Modeling
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Representation Learning via Adversarially-Contrastive Optimal Transport
Anoop Cherian • Shuchin Aeron
Keywords: Representation Learning • Deep Learning - General
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InfoGAN-CR and ModelCentrality: Self-supervised Model Training and Selection for Disentangling GANs
Zinan Lin • Kiran K Thekumparampil • Giulia Fanti • Sewoong Oh
Keywords: Representation Learning • Deep Learning - Generative Models and Autoencoders
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Convex Representation Learning for Generalized Invariance in Semi-Inner-Product Space
Yingyi Ma • Vignesh Ganapathiraman • Yaoliang Yu • Xinhua Zhang
Keywords: Representation Learning • General Machine Learning Techniques
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Sequential, Network, and Time-Series Modeling
Encoding Musical Style with Transformer Autoencoders
Kristy Choi • Curtis Hawthorne • Ian Simon • Monica Dinculescu • Jesse Engel
Keywords: Sequential, Network, and Time-Series Modeling • Applications - Other
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Structured Prediction with Partial Labelling through the Infimum Loss
Vivien A Cabannnes • Alessandro Rudi • Francis Bach
Keywords: Sequential, Network, and Time-Series Modeling • General Machine Learning Techniques • Learning Theory
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Scalable Identification of Partially Observed Systems with Certainty-Equivalent EM
Kunal R Menda • Jean de Becdelievre • Jayesh K Gupta • Ilan Kroo • Mykel Kochenderfer • Zachary Manchester
Keywords: Sequential, Network, and Time-Series Modeling • Applications - Other • Unsupervised and Semi-Supervised Learning • Optimization - Non-convex
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Stochastic Latent Residual Video Prediction
Jean-Yves Franceschi • Edouard Delasalles • Mickaël Chen • Sylvain Lamprier • Patrick Gallinari
Keywords: Sequential, Network, and Time-Series Modeling • Applications - Computer Vision • Deep Learning - Generative Models and Autoencoders • Representation Learning
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On the Theoretical Properties of the Network Jackknife
Qiaohui Lin • Robert Lunde • Purnamrita Sarkar
Keywords: Sequential, Network, and Time-Series Modeling • Applications - Other • Learning Theory
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CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods
Wei Zhang • Thomas Panum • Somesh Jha • Prasad CHALASANI • David Page
Keywords: Sequential, Network, and Time-Series Modeling • Accountability, Transparency and Interpretability
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Inductive Relation Prediction by Subgraph Reasoning
Komal Teru • Etienne Denis • Will Hamilton
Keywords: Sequential, Network, and Time-Series Modeling • Graph Neural Networks
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Unsupervised Transfer Learning for Spatiotemporal Predictive Networks
Zhiyu Yao • Yunbo Wang • Mingsheng Long • Jianmin Wang
Keywords: Sequential, Network, and Time-Series Modeling • Transfer, Multitask and Meta-learning
PDF Bib Video
Robustifying Sequential Neural Processes
Jaesik Yoon • Gautam Singh • Sungjin Ahn
Keywords: Sequential, Network, and Time-Series Modeling • Deep Learning - Generative Models and Autoencoders • Transfer, Multitask and Meta-learning • Transfer, Multitask and Meta-learning
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Learning Quadratic Games on Networks
Yan Leng • Xiaowen Dong • Junfeng Wu • Alex `Sandy' Pentland
Keywords: Sequential, Network, and Time-Series Modeling • Applications - Other • Learning Theory
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Neural Datalog Through Time: Informed Temporal Modeling via Logical Specification
Hongyuan Mei • Guanghui Qin • Minjie Xu • Jason Eisner
Keywords: Sequential, Network, and Time-Series Modeling • Deep Learning - General • Sequential, Network, and Time-Series Modeling • Probabilistic Inference - Models and Probabilistic Programming
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A Flexible Latent Space Model for Multilayer Networks
Xuefei Zhang • Songkai Xue • Ji Zhu
Keywords: Sequential, Network, and Time-Series Modeling • Applications - Other • General Machine Learning Techniques
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LP-SparseMAP: Differentiable Relaxed Optimization for Sparse Structured Prediction
Vlad Niculae • Andre Martins
Keywords: Sequential, Network, and Time-Series Modeling • Applications - Language, Speech and Dialog
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The Buckley-Osthus model and the block preferential attachment model: statistical analysis and application
Wenpin Tang • Xin Guo • Fengmin Tang
Keywords: Sequential, Network, and Time-Series Modeling • Applications - Other • Optimization - Large Scale, Parallel and Distributed
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Towards non-parametric drift detection via Dynamic Adapting Window Independence Drift Detection (DAWIDD)
Fabian Hinder • André Artelt • Barbara Hammer
Keywords: Sequential, Network, and Time-Series Modeling • Online Learning, Active Learning, and Bandits • Learning Theory
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Fast and Consistent Learning of Hidden Markov Models by Incorporating Non-Consecutive Correlations
Robert Mattila • Cristian R. Rojas • Eric Moulines • Vikram Krishnamurthy • Bo Wahlberg
Keywords: Sequential, Network, and Time-Series Modeling • Probabilistic Inference - Models and Probabilistic Programming • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods
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Improving the Gating Mechanism of Recurrent Neural Networks
Albert Gu • Caglar Gulcehre • Thomas L Paine • Matt Hoffman • Razvan Pascanu
Keywords: Sequential, Network, and Time-Series Modeling • Sequential, Network, and Time-Series Modeling
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Learning Mixtures of Graphs from Epidemic Cascades
Jessica Hoffmann • Soumya Basu • Surbhi Goel • Constantine Caramanis
Keywords: Sequential, Network, and Time-Series Modeling • Learning Theory
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VideoOneNet: Bidirectional Convolutional Recurrent OneNet with Trainable Data Steps for Video Processing
Zoltán Á. Milacski • Barnabás Póczos • András Lorincz
Keywords: Sequential, Network, and Time-Series Modeling • Deep Learning - General • Transfer, Multitask and Meta-learning • Optimization - General
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Incremental Sampling Without Replacement for Sequence Models
Kensen Shi • David Bieber • Charles Sutton
Keywords: Sequential, Network, and Time-Series Modeling • Program Understanding and Generation
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Approximating Stacked and Bidirectional Recurrent Architectures with the Delayed Recurrent Neural Network
Javier S Turek • Shailee Jain • Vy Vo • Mihai Capotă • Alexander Huth • Theodore L Willke
Keywords: Sequential, Network, and Time-Series Modeling • Sequential, Network, and Time-Series Modeling
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Transformation of ReLU-based recurrent neural networks from discrete-time to continuous-time
Zahra Monfared • Daniel Durstewitz
Keywords: Sequential, Network, and Time-Series Modeling • General Machine Learning Techniques
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Self-Attentive Hawkes Process
Qiang Zhang • Aldo Lipani • Omer Kirnap • Emine Yilmaz
Keywords: Sequential, Network, and Time-Series Modeling • Sequential, Network, and Time-Series Modeling
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Temporal Logic Point Processes
Shuang Li • Lu Wang • Ruizhi Zhang • Xiaofu Chang • Xuqin Liu • Yao Xie • Yuan Qi • Le Song
Keywords: Sequential, Network, and Time-Series Modeling
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Do RNN and LSTM have Long Memory?
Jingyu Zhao • Feiqing Huang • Jia Lv • Yanjie Duan • Zhen Qin • Guodong Li • Guangjian Tian
Keywords: Sequential, Network, and Time-Series Modeling
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Time-aware Large Kernel Convolutions
Vasileios Lioutas • Yuhong Guo
Keywords: Sequential, Network, and Time-Series Modeling • Applications - Language, Speech and Dialog • Deep Learning - General
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Learning to Simulate Complex Physics with Graph Networks
Alvaro Sanchez-Gonzalez • Jonathan Godwin • Tobias Pfaff • Rex Ying • Jure Leskovec • Peter Battaglia
Keywords: Sequential, Network, and Time-Series Modeling • Model learning
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Self-Attentive Associative Memory
Hung T Le • Truyen Tran • Svetha Venkatesh
Keywords: Sequential, Network, and Time-Series Modeling • Deep Learning - General • Sequential, Network, and Time-Series Modeling • Supervised Learning
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GraphOpt: Learning Optimization Models of Graph Formation
Rakshit Trivedi • Jiachen Yang • Hongyuan Zha
Keywords: Sequential, Network, and Time-Series Modeling • Deep Learning - Generative Models and Autoencoders • Reinforcement Learning - Deep RL
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Forecasting Sequential Data Using Consistent Koopman Autoencoders
Omri Azencot • N. Benjamin Erichson • Vanessa Lin • Michael Mahoney
Keywords: Sequential, Network, and Time-Series Modeling • Deep Learning - Generative Models and Autoencoders
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Set Functions for Time Series
Max Horn • Michael Moor • Christian Bock • Bastian A Rieck • Karsten Borgwardt
Keywords: Sequential, Network, and Time-Series Modeling • Applications - Neuroscience, Cognitive Science, Biology and Health • Sequential, Network, and Time-Series Modeling • Supervised Learning
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Learning the piece-wise constant graph structure of a varying Ising model
Batiste Le Bars • Pierre Humbert • Argyris Kalogeratos • Nicolas Vayatis
Keywords: Sequential, Network, and Time-Series Modeling • Probabilistic Inference - Models and Probabilistic Programming
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Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules
Sarthak Mittal • Alex M Lamb • Anirudh Goyal • Vikram Voleti • Murray Shanahan • Guillaume Lajoie • Michael C Mozer • Yoshua Bengio
Keywords: Sequential, Network, and Time-Series Modeling • Deep Learning - General
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Learning Reasoning Strategies in End-to-End Differentiable Proving
Pasquale Minervini • Sebastian Riedel • Pontus Stenetorp • Edward Grefenstette • Tim Rocktäschel
Keywords: Sequential, Network, and Time-Series Modeling • Probabilistic Inference - Models and Probabilistic Programming
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Sequence Generation with Mixed Representations
Lijun Wu • Shufang Xie • Yingce Xia • Yang Fan • Jian-Huang Lai • Tao Qin • Tieyan Liu
Keywords: Sequential, Network, and Time-Series Modeling • Applications - Language, Speech and Dialog • Deep Learning - General • Supervised Learning
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Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More
Aleksandar Bojchevski • Johannes Klicpera • Stephan Günnemann
Keywords: Sequential, Network, and Time-Series Modeling • Applications - Other • Adversarial Examples • Trustworthy Machine Learning
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Imputer: Sequence Modelling via Imputation and Dynamic Programming
William Chan • Chitwan Saharia • Geoffrey Hinton • Mohammad Norouzi • Navdeep Jaitly
Keywords: Sequential, Network, and Time-Series Modeling • Applications - Language, Speech and Dialog • Applications - Language, Speech and Dialog
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Improving Transformer Optimization Through Better Initialization
Xiao Shi Huang • Felipe Pérez • Jimmy Ba • Maksims Volkovs
Keywords: Sequential, Network, and Time-Series Modeling • Applications - Language, Speech and Dialog • Deep Learning - General
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Consistent Structured Prediction with Max-Min Margin Markov Networks
Alex Nowak • Francis Bach • Alessandro Rudi
Keywords: Sequential, Network, and Time-Series Modeling • Supervised Learning • Learning Theory • Learning Theory
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Collapsed Amortized Variational Inference for Switching Nonlinear Dynamical Systems
Zhe Dong • Bryan Seybold • Kevin Murphy • Hung H Bui
Keywords: Sequential, Network, and Time-Series Modeling • Sequential, Network, and Time-Series Modeling • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods • Probabilistic Inference - Models and Probabilistic Programming
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Predicting deliberative outcomes
Vikas K Garg • Tommi Jaakkola
Keywords: Sequential, Network, and Time-Series Modeling • Planning, Control, and Multiagent Learning
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Fast Differentiable Sorting and Ranking
Mathieu Blondel • Olivier Teboul • Quentin Berthet • Josip Djolonga
Keywords: Sequential, Network, and Time-Series Modeling • Optimization - Convex
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Spectral Clustering with Graph Neural Networks for Graph Pooling
Filippo Maria Bianchi • Daniele Grattarola • Cesare Alippi
Keywords: Sequential, Network, and Time-Series Modeling • Deep Learning - General • Unsupervised and Semi-Supervised Learning • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods
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Supervised Learning
Improving generalization by controlling label-noise information in neural network weights
Hrayr Harutyunyan • Kyle Reing • Greg Ver Steeg • Aram Galstyan
Keywords: Supervised Learning • Deep Learning - Algorithms • Learning Theory • Trustworthy Machine Learning
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Normalized Loss Functions for Deep Learning with Noisy Labels
Xingjun Ma • Hanxun Huang • Yisen Wang • Simone Romano • Sarah Erfani • James Bailey
Keywords: Supervised Learning
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Variational Label Enhancement
Ning Xu • Jun Shu • Yun-Peng Liu • Xin Geng
Keywords: Supervised Learning • General Machine Learning Techniques
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Optimizing Black-box Metrics with Adaptive Surrogates
Qijia Jiang • Olaoluwa A Adigun • Harikrishna Narasimhan • Mahdi Milani Fard • Maya Gupta
Keywords: Supervised Learning • Optimization - Convex • Fairness, Equity, Justice, and Safety
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Minimax Rate for Learning From Pairwise Comparisons in the BTL Model
Julien Hendrickx • Alexander Olshevsky • Venkatesh Saligrama
Keywords: Supervised Learning
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The Sample Complexity of Best-$k$ Items Selection from Pairwise Comparisons
Wenbo Ren • Jia Liu • Ness Shroff
Keywords: Supervised Learning • Online Learning, Active Learning, and Bandits • Learning Theory
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IPBoost – Non-Convex Boosting via Integer Programming
Marc Pfetsch • Sebastian Pokutta
Keywords: Supervised Learning • Supervised Learning • Optimization - General • Optimization - Non-convex
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Generalization Error of Generalized Linear Models in High Dimensions
Melikasadat Emami • Mojtaba Sahraee-Ardakan • Parthe Pandit • Sundeep Rangan • Alyson K. Fletcher
Keywords: Supervised Learning • Learning Theory • Learning Theory
PDF Bib Video Supplement
Robust Bayesian Classification Using An Optimistic Score Ratio
Viet Anh Nguyen • Nian Si • Jose Blanchet
Keywords: Supervised Learning • Optimization - Non-convex • Probabilistic Inference - Models and Probabilistic Programming • Probabilistic Inference - Models and Probabilistic Programming
PDF Bib Video Supplement
The Tree Ensemble Layer: Differentiability meets Conditional Computation
Hussein Hazimeh • Natalia Ponomareva • Petros Mol • Zhenyu Tan • Rahul Mazumder
Keywords: Supervised Learning • Deep Learning - Algorithms • Deep Learning - General • Deep Learning - General
PDF Bib Video Supplement
It's Not What Machines Can Learn, It's What We Cannot Teach
Gal Yehuda • Moshe Gabel • Assaf Schuster
Keywords: Supervised Learning • Unsupervised and Semi-Supervised Learning • Trustworthy Machine Learning
PDF Bib Video Supplement
Fiedler Regularization: Learning Neural Networks with Graph Sparsity
Edric Tam • David Dunson
Keywords: Supervised Learning • Applications - Other • Deep Learning - Algorithms • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods
PDF Bib Video
Preference Modeling with Context-Dependent Salient Features
Amanda Bower • Laura Balzano
Keywords: Supervised Learning
PDF Bib Video Supplement
Uncertainty Estimation Using a Single Deep Deterministic Neural Network
Joost van Amersfoort • Lewis SG Smith • Yee Whye Teh • Yarin Gal
Keywords: Supervised Learning • Deep Learning - Algorithms • Deep Learning - General • Representation Learning
PDF Bib Video Supplement
Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates
Yang Liu • Hongyi Guo
Keywords: Supervised Learning • Applications -> Crowdsourcing • Learning Theory • Trustworthy Machine Learning
PDF Bib Video Supplement
Deep Streaming Label Learning
Zhen Zohn Wang • Liu Liu • Dacheng Tao
Keywords: Supervised Learning • Deep Learning - General
PDF Bib Video Supplement
Scalable and Efficient Comparison-based Search without Features
Daniyar Chumbalov • Lucas Maystre • Matthias Grossglauser
Keywords: Supervised Learning • Applications - Other • Online Learning, Active Learning, and Bandits
PDF Bib Video Supplement
Feature Selection using Stochastic Gates
Yutaro Yamada • Ofir Lindenbaum • Sahand Negahban • Yuval Kluger
Keywords: Supervised Learning • Applications - Neuroscience, Cognitive Science, Biology and Health • Unsupervised and Semi-Supervised Learning
PDF Bib Video Supplement
StochasticRank: Global Optimization of Scale-Free Discrete Functions
Aleksei Ustimenko • Liudmila Prokhorenkova
Keywords: Supervised Learning • Applications - Other • Supervised Learning • Optimization - Non-convex
PDF Bib Video Supplement
The Implicit Regularization of Stochastic Gradient Flow for Least Squares
Alnur Ali • Edgar Dobriban • Ryan Tibshirani
Keywords: Supervised Learning • Optimization - Convex • Optimization - Large Scale, Parallel and Distributed
PDF Bib Video Supplement
Optimistic Bounds for Multi-output Learning
Henry W J Reeve • Ata Kaban
Keywords: Supervised Learning • Supervised Learning • Learning Theory • Learning Theory
PDF Bib Video Supplement
Predicting Choice with Set-Dependent Aggregation
Nir Rosenfeld • Kojin Oshiba • Yaron Singer
Keywords: Supervised Learning • Applications - Other
PDF Bib Video Supplement
Optimal Sequential Maximization: One Interview is Enough!
Moein Falahatgar • Alon Orlitsky • Venkatadheeraj Pichapati
Keywords: Supervised Learning • Online Learning, Active Learning, and Bandits
PDF Bib Video Supplement
Convex Calibrated Surrogates for the Multi-Label F-Measure
Mingyuan Zhang • Harish Guruprasad Ramaswamy • Shivani Agarwal
Keywords: Supervised Learning • Learning Theory
PDF Bib Video Supplement
NGBoost: Natural Gradient Boosting for Probabilistic Prediction
Tony Duan • Avati Anand • Daisy Yi Ding • Khanh K. Thai • Sanjay Basu • Andrew Ng • Alejandro Schuler
Keywords: Supervised Learning • Supervised Learning • Learning Theory
PDF Bib Video
On Lp-norm Robustness of Ensemble Decision Stumps and Trees
Yihan Wang • Huan Zhang • Hongge Chen • Duane Boning • Cho-Jui Hsieh
Keywords: Supervised Learning • Adversarial Examples • Fairness, Equity, Justice, and Safety
PDF Bib Video Supplement
Sparse Shrunk Additive Models
Guodong Liu • Hong Chen • Heng Huang
Keywords: Supervised Learning • Learning Theory
PDF Bib Video Supplement
Decision Trees for Decision-Making under the Predict-then-Optimize Framework
Adam Elmachtoub • Jason Cheuk Nam Liang • Ryan McNellis
Keywords: Supervised Learning • Applications - Other • General Machine Learning Techniques • Optimization - General
PDF Bib Video Supplement
Smaller, more accurate regression forests using tree alternating optimization
Arman Zharmagambetov • Miguel Á. Carreira-Perpiñán
Keywords: Supervised Learning • Supervised Learning • Optimization - General
PDF Bib Video Supplement
Aggregation of Multiple Knockoffs
Tuan-Binh Tuan Nguyen • Jerome-Alexis Chevalier • Bertrand Thirion • Sylvain Arlot
Keywords: Supervised Learning • Applications - Neuroscience, Cognitive Science, Biology and Health • Applications - Neuroscience, Cognitive Science, Biology and Health • Trustworthy Machine Learning
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Learning with Feature and Distribution Evolvable Streams
Zhen-Yu Zhang • Peng Zhao • Yuan Jiang • Zhi-Hua Zhou
Keywords: Supervised Learning • Sequential, Network, and Time-Series Modeling
PDF Bib Video Supplement
Graph Homomorphism Convolution
Hoang T Nguyen • Takanori Maehara
Keywords: Supervised Learning • Applications - Other • General Machine Learning Techniques
PDF Bib Video
Choice Set Optimization Under Discrete Choice Models of Group Decisions
Kiran Tomlinson • Austin R. Benson
Keywords: Supervised Learning • Optimization - General
PDF Bib Video Supplement
Efficient Domain Generalization via Common-Specific Low-Rank Decomposition
Vihari Piratla • Praneeth Netrapalli • Sunita Sarawagi
Keywords: Supervised Learning • Transfer, Multitask and Meta-learning
PDF Bib Video
Enhancing Simple Models by Exploiting What They Already Know
Amit Dhurandhar • Karthikeyan Shanmugam • Ronny Luss
Keywords: Supervised Learning
PDF Bib Video Supplement
Being Bayesian about Categorical Probability
Taejong Joo • Uijung Chung • Min-Gwan Seo
Keywords: Supervised Learning • Deep Learning - General • Probabilistic Inference - Models and Probabilistic Programming
PDF Bib Video
Self-PU: Self Boosted and Calibrated Positive-Unlabeled Training
Xuxi Chen • Wuyang Chen • Tianlong Chen • Ye Yuan • Chen Gong • Kewei Chen • Zhangyang Wang
Keywords: Supervised Learning • Unsupervised and Semi-Supervised Learning
PDF Bib Video
Growing Adaptive Multi-hyperplane Machines
Nemanja Djuric • Zhuang Wang • Slobodan Vucetic
Keywords: Supervised Learning • Optimization - Large Scale, Parallel and Distributed
PDF Bib Video Supplement
Rank Aggregation from Pairwise Comparisons in the Presence of Adversarial Corruptions
Arpit Agarwal • Shivani Agarwal • Sanjeev Khanna • Prathamesh Patil
Keywords: Supervised Learning • Adversarial Examples • Trustworthy Machine Learning
PDF Bib Video Supplement
Single Point Transductive Prediction
Nilesh Tripuraneni • Lester Mackey
Keywords: Supervised Learning • Learning Theory
PDF Bib Video Supplement
Transfer, Multitask and Meta-learning
Searching to Exploit Memorization Effect in Learning with Noisy Labels
Quanming Yao • Hansi Yang • Bo Han • Gang Niu • James Tin-Yau Kwok
Keywords: Transfer, Multitask and Meta-learning • Deep Learning - General • Unsupervised and Semi-Supervised Learning • Optimization - Non-convex
PDF Bib Video Supplement
Meta-Learning with Shared Amortized Variational Inference
Ekaterina Iakovleva • Jakob Verbeek • Karteek Alahari
Keywords: Transfer, Multitask and Meta-learning • Deep Learning - Generative Models and Autoencoders • Transfer, Multitask and Meta-learning • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods
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Multi-Task Learning with User Preferences: Gradient Descent with Controlled Ascent in Pareto Optimization
Debabrata Mahapatra • Vaibhav Rajan
Keywords: Transfer, Multitask and Meta-learning • Multiobjective Optimization
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On the Global Optimality of Model-Agnostic Meta-Learning
Lingxiao Wang • Qi Cai • Zhuoran Yang • Zhaoran Wang
Keywords: Transfer, Multitask and Meta-learning • Reinforcement Learning - General
PDF Bib Video Supplement
Learning to Learn Kernels with Variational Random Features
Xiantong Zhen • Haoliang Sun • Yingjun Du • Jun Xu • Yilong Yin • Ling Shao • Cees Snoek
Keywords: Transfer, Multitask and Meta-learning • General Machine Learning Techniques • Probabilistic Inference - Models and Probabilistic Programming
PDF Bib Video
Which Tasks Should Be Learned Together in Multi-task Learning?
Trevor S Standley • Amir Zamir • Dawn Chen • Leonidas Guibas • Jitendra Malik • Silvio Savarese
Keywords: Transfer, Multitask and Meta-learning • Applications - Computer Vision • Supervised Learning
PDF Bib Video Supplement
Robust Learning with the Hilbert-Schmidt Independence Criterion
Daniel Greenfeld • Uri Shalit
Keywords: Transfer, Multitask and Meta-learning • Supervised Learning
PDF Bib Video Supplement
Learning to Branch for Multi-Task Learning
Pengsheng Guo • Chen-Yu Lee • Daniel Ulbricht
Keywords: Transfer, Multitask and Meta-learning • Deep Learning - General • Transfer, Multitask and Meta-learning
PDF Bib Video Supplement
On the Generalization Effects of Linear Transformations in Data Augmentation
Sen Wu • Hongyang R Zhang • Gregory Valiant • Christopher Re
Keywords: Transfer, Multitask and Meta-learning • Deep Learning - Algorithms
PDF Bib Video Supplement
Optimizing Dynamic Structures with Bayesian Generative Search
Minh Hoang • Carleton Kingsford
Keywords: Transfer, Multitask and Meta-learning • Sequential, Network, and Time-Series Modeling
PDF Bib Video
LEEP: A New Measure to Evaluate Transferability of Learned Representations
Cuong V Nguyen • Tal Hassner • Matthias Seeger • Cedric Archambeau
Keywords: Transfer, Multitask and Meta-learning • Applications - Computer Vision • Transfer, Multitask and Meta-learning
PDF Bib Video Supplement
Online Continual Learning from Imbalanced Data
Aristotelis Chrysakis • Marie-Francine Moens
Keywords: Transfer, Multitask and Meta-learning • Representation Learning • Supervised Learning
PDF Bib Video Supplement
Domain Aggregation Networks for Multi-Source Domain Adaptation
Junfeng Wen • Russell Greiner • Dale Schuurmans
Keywords: Transfer, Multitask and Meta-learning • Unsupervised and Semi-Supervised Learning
PDF Bib Video
Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks
Micah Goldblum • Steven Reich • Liam Fowl • Renkun Ni • Valeriia Cherepanova • Tom Goldstein
Keywords: Transfer, Multitask and Meta-learning • Representation Learning
PDF Bib Video Supplement
TaskNorm: Rethinking Batch Normalization for Meta-Learning
John Bronskill • Jonathan Gordon • James R Requeima • Sebastian Nowozin • Richard E. Turner
Keywords: Transfer, Multitask and Meta-learning • Deep Learning - Algorithms
PDF Bib Video Supplement
AutoML-Zero: Evolving Machine Learning Algorithms From Scratch
Esteban Real • Chen Liang • David So • Quoc Le
Keywords: Transfer, Multitask and Meta-learning
PDF Bib Video Supplement
Stabilizing Differentiable Architecture Search via Perturbation-based Regularization
Xiangning Chen • Cho-Jui Hsieh
Keywords: Transfer, Multitask and Meta-learning • Deep Learning - General
PDF Bib Video Supplement
Meta-learning with Stochastic Linear Bandits
Leonardo Cella • Alessandro Lazaric • Massimiliano Pontil
Keywords: Transfer, Multitask and Meta-learning • Online Learning, Active Learning, and Bandits
PDF Bib Video Supplement
Efficient Continuous Pareto Exploration in Multi-Task Learning
Pingchuan Ma • Tao Du • Wojciech Matusik
Keywords: Transfer, Multitask and Meta-learning • Deep Learning - General • Supervised Learning • Optimization - General
PDF Bib Video Supplement
On the Iteration Complexity of Hypergradient Computation
Riccardo Grazzi • Luca Franceschi • Massimiliano Pontil • Saverio Salzo
Keywords: Transfer, Multitask and Meta-learning • Optimization - Non-convex • Optimization - General
PDF Bib Video Supplement
Learning to Rank Learning Curves
Martin Wistuba • Tejaswini Pedapati
Keywords: Transfer, Multitask and Meta-learning • Applications - Computer Vision • Supervised Learning
PDF Bib Video Supplement
Meta Variance Transfer: Learning to Augment from the Others
Seong-Jin Park • Seungju Han • Jiwon Baek • Insoo Kim • Juhwan Song • Hae Beom Lee • Jae-Joon Han • Sung Ju Hwang
Keywords: Transfer, Multitask and Meta-learning • Applications - Computer Vision • Deep Learning - Algorithms • Transfer, Multitask and Meta-learning
PDF Bib Video Supplement
Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation
Jian Liang • Dapeng Hu • Jiashi Feng
Keywords: Transfer, Multitask and Meta-learning • Applications - Computer Vision
PDF Bib Video
MetaFun: Meta-Learning with Iterative Functional Updates
Jin Xu • Jean-Francois Ton • Hyunjik Kim • Adam R. Kosiorek • Yee Whye Teh
Keywords: Transfer, Multitask and Meta-learning
PDF Bib Video Supplement
T-GD: Transferable GAN-generated Images Detection Framework
Hyeonseong Jeon • Young Oh Bang • Junyaup Kim • Simon Woo
Keywords: Transfer, Multitask and Meta-learning • Applications - Computer Vision • Applications - Other
PDF Bib Video
XtarNet: Learning to Extract Task-Adaptive Representation for Incremental Few-Shot Learning
Sung Whan Yoon • Do-Yeon Kim • Jun Seo • Jaekyun Moon
Keywords: Transfer, Multitask and Meta-learning • Few-shot Learning
PDF Bib Video Supplement
LTF: A Label Transformation Framework for Correcting Label Shift
Jiaxian Guo • Mingming Gong • Tongliang Liu • Kun Zhang • Dacheng Tao
Keywords: Transfer, Multitask and Meta-learning • Unsupervised and Semi-Supervised Learning
PDF Bib Video Supplement
Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data
Felipe Petroski Such • Aditya Rawal • Joel Lehman • Kenneth O Stanley • Jeffrey Clune
Keywords: Transfer, Multitask and Meta-learning
PDF Bib Video Supplement
Estimating Generalization under Distribution Shifts via Domain-Invariant Representations
Ching-Yao Chuang • Antonio Torralba • Stefanie Jegelka
Keywords: Transfer, Multitask and Meta-learning • Deep Learning - Algorithms • Representation Learning
PDF Bib Video Supplement
Margin-aware Adversarial Domain Adaptation with Optimal Transport
Sofien Dhouib • Ievgen Redko • Carole Lartizien
Keywords: Transfer, Multitask and Meta-learning
PDF Bib Video Supplement
Learning To Stop While Learning To Predict
Xinshi Chen • Hanjun Dai • Yu Li • Xin Gao • Le Song
Keywords: Transfer, Multitask and Meta-learning • Deep Learning - Generative Models and Autoencoders • Transfer, Multitask and Meta-learning
PDF Bib Video Supplement
Few-shot Domain Adaptation by Causal Mechanism Transfer
Takeshi Teshima • Issei Sato • Masashi Sugiyama
Keywords: Transfer, Multitask and Meta-learning • Learning Theory • Causality
PDF Bib Video Supplement
Adaptive Adversarial Multi-task Representation Learning
YUREN MAO • Weiwei Liu • Xuemin Lin
Keywords: Transfer, Multitask and Meta-learning
PDF Bib Video Supplement
Automated Synthetic-to-Real Generalization
Wuyang Chen • Zhiding Yu • Zhangyang Wang • Animashree Anandkumar
Keywords: Transfer, Multitask and Meta-learning • Transfer, Multitask and Meta-learning
PDF Bib Video
Mutual Transfer Learning for Massive Data
Ching-Wei Cheng • Xingye Qiao • Guang Cheng
Keywords: Transfer, Multitask and Meta-learning • Transfer, Multitask and Meta-learning • Supervised Learning
PDF Bib Video Supplement
Transfer Learning without Knowing: Reprogramming Black-box Machine Learning Models with Scarce Data and Limited Resources
Yun-Yun Tsai • Pin-Yu Chen • Tsung-Yi Ho
Keywords: Transfer, Multitask and Meta-learning • Adversarial Examples
PDF Bib Video Supplement
Trustworthy Machine Learning
Optimal Robust Learning of Discrete Distributions from Batches
Ayush Jain • Alon Orlitsky
Keywords: Trustworthy Machine Learning • Learning Theory • Learning Theory
PDF Bib Video Supplement
Maximum Likelihood with Bias-Corrected Calibration is Hard-To-Beat at Label Shift Adaptation
Amr M Alexandari • Anshul Kundaje • Avanti Shrikumar
Keywords: Trustworthy Machine Learning • Applications - Neuroscience, Cognitive Science, Biology and Health
PDF Bib Video Supplement
Certified Robustness to Label-Flipping Attacks via Randomized Smoothing
Elan Rosenfeld • Ezra Winston • Pradeep Ravikumar • Zico Kolter
Keywords: Trustworthy Machine Learning • Deep Learning - Algorithms • Supervised Learning
PDF Bib Video Supplement
On the Sample Complexity of Adversarial Multi-Source PAC Learning
Nikola Konstantinov • Elias Frantar • Dan Alistarh • Christoph H Lampert
Keywords: Trustworthy Machine Learning • Supervised Learning • Learning Theory
PDF Bib Video Supplement
NADS: Neural Architecture Distribution Search for Uncertainty Awareness
Randy Ardywibowo • Shahin Boluki • Xinyu Gong • Zhangyang Wang • Xiaoning Qian
Keywords: Trustworthy Machine Learning • Deep Learning - General • Transfer, Multitask and Meta-learning • Fairness, Equity, Justice, and Safety
PDF Bib Video Supplement
Adaptive Reward-Poisoning Attacks against Reinforcement Learning
Xuezhou Zhang • Yuzhe Ma • Adish Singla • Xiaojin Zhu
Keywords: Trustworthy Machine Learning • Reinforcement Learning - General
PDF Bib Video Supplement
A Flexible Framework for Nonparametric Graphical Modeling that Accommodates Machine Learning
Yunhua Xiang • Noah Simon
Keywords: Trustworthy Machine Learning • Learning Theory • Probabilistic Inference - Models and Probabilistic Programming
PDF Bib Video Supplement
Individual Calibration with Randomized Forecasting
Shengjia Zhao • Tengyu Ma • Stefano Ermon
Keywords: Trustworthy Machine Learning • Learning Theory • Fairness, Equity, Justice, and Safety
PDF Bib Video Supplement
An Investigation of Why Overparameterization Exacerbates Spurious Correlations
Shiori Sagawa • Aditi Raghunathan • Pang Wei Koh • Percy Liang
Keywords: Trustworthy Machine Learning
PDF Bib Video Supplement
Interpreting Robust Optimization via Adversarial Influence Functions
Zhun Deng • Cynthia Dwork • Jialiang Wang • Linjun Zhang
Keywords: Trustworthy Machine Learning • Learning Theory • Optimization - Convex • Adversarial Examples
PDF Bib Video
Improving Robustness of Deep-Learning-Based Image Reconstruction
Ankit Raj • Yoram Bresler • Bo Li
Keywords: Trustworthy Machine Learning • Applications - Computer Vision • Optimization - General • Adversarial Examples
PDF Bib Video Supplement
Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift
Alex J Chan • Ahmed Alaa • Zhaozhi Qian • Mihaela van der Schaar
Keywords: Trustworthy Machine Learning • Applications - Other
PDF Bib Video Supplement
Identifying Statistical Bias in Dataset Replication
Logan Engstrom • Andrew Ilyas • Shibani Santurkar • Dimitris Tsipras • Jacob Steinhardt • Aleksander Madry
Keywords: Trustworthy Machine Learning • Trustworthy Machine Learning
PDF Bib Video Supplement
Robustness to Spurious Correlations via Human Annotations
Megha B. Srivastava • Tatsunori Hashimoto • Percy Liang
Keywords: Trustworthy Machine Learning • Applications -> Crowdsourcing
PDF Bib Video Supplement
Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?
Angelos Filos • Panagiotis Tigas • Rowan McAllister • Nicholas Rhinehart • Sergey Levine • Yarin Gal
Keywords: Trustworthy Machine Learning • Applications - Other • Deep Learning - General
PDF Bib Video
Evaluating Machine Accuracy on ImageNet
Vaishaal Shankar • Rebecca Roelofs • Horia Mania • Alex Fang • Benjamin Recht • Ludwig Schmidt
Keywords: Trustworthy Machine Learning
PDF Bib Video Supplement
Zeno++: Robust Fully Asynchronous SGD
Cong Xie • Sanmi Koyejo • Indranil Gupta
Keywords: Trustworthy Machine Learning • Fairness, Equity, Justice, and Safety
PDF Bib Video Supplement
Test-Time Training with Self-Supervision for Generalization under Distribution Shifts
Yu Sun • Xiaolong Wang • Zhuang Liu • John P Miller • Alexei A Efros • Moritz Hardt
Keywords: Trustworthy Machine Learning • Deep Learning - Algorithms
PDF Bib Video
The Effect of Natural Distribution Shift on Question Answering Models
John P Miller • Karl Krauth • Benjamin Recht • Ludwig Schmidt
Keywords: Trustworthy Machine Learning
PDF Bib Video Supplement
Stochastically Dominant Distributional Reinforcement Learning
John D. Martin • Michal Lyskawinski • Xiaohu Li • Brendan Englot
Keywords: Trustworthy Machine Learning • Planning, Control, and Multiagent Learning • Applications - Other • Reinforcement Learning - General
PDF Bib Video Supplement
Online metric algorithms with untrusted predictions
Antonios Antoniadis • Christian Coester • Marek Eliáš • Adam Polak • Bertrand Simon
Keywords: Trustworthy Machine Learning • Optimization - General
PDF Bib Video Supplement
Unsupervised and Semi-Supervised Learning
p-Norm Flow Diffusion for Local Graph Clustering
Kimon Fountoulakis • Di Wang • Shenghao Yang
Keywords: Unsupervised and Semi-Supervised Learning • Applications - Other • Optimization - Convex • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods
PDF Bib Video Supplement
Unbiased Risk Estimators Can Mislead: A Case Study of Learning with Complementary Labels
Yu-Ting Chou • Gang Niu • Hsuan-Tien Lin • Masashi Sugiyama
Keywords: Unsupervised and Semi-Supervised Learning • Deep Learning - Algorithms
PDF Bib Video
Learning with Bounded Instance- and Label-dependent Label Noise
Jiacheng Cheng • Tongliang Liu • Kotagiri Ramamohanarao • Dacheng Tao
Keywords: Unsupervised and Semi-Supervised Learning
PDF Bib Video Supplement
Correlation Clustering with Asymmetric Classification Errors
Jafar Jafarov • Sanchit Kalhan • Konstantin Makarychev • Yury Makarychev
Keywords: Unsupervised and Semi-Supervised Learning • Optimization - General
PDF Bib Video
When Does Self-Supervision Help Graph Convolutional Networks?
Yuning You • Tianlong Chen • Zhangyang Wang • Yang Shen
Keywords: Unsupervised and Semi-Supervised Learning • Graph Convolutional Networks
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Training Binary Neural Networks through Learning with Noisy Supervision
Kai Han • Yunhe Wang • Yixing Xu • Chunjing Xu • Enhua Wu • Chang Xu
Keywords: Unsupervised and Semi-Supervised Learning • Deep Learning - General
PDF Bib Video
Input-Sparsity Low Rank Approximation in Schatten Norm
Yi Li • David Woodruff
Keywords: Unsupervised and Semi-Supervised Learning • General Machine Learning Techniques • Optimization - Non-convex
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Sets Clustering
Ibrahim Jubran • Murad Tukan • Alaa Maalouf • Dan Feldman
Keywords: Unsupervised and Semi-Supervised Learning • Optimization - Large Scale, Parallel and Distributed • Optimization - General
PDF Bib Video Supplement
Safe Deep Semi-Supervised Learning for Unseen-Class Unlabeled Data
Lan-Zhe Guo • Zhen-Yu Zhang • Yuan Jiang • Yu-Feng Li • Zhi-Hua Zhou
Keywords: Unsupervised and Semi-Supervised Learning • Trustworthy Machine Learning
PDF Bib Video Supplement
Time-Consistent Self-Supervision for Semi-Supervised Learning
Tianyi Zhou • Shengjie Wang • Jeff Bilmes
Keywords: Unsupervised and Semi-Supervised Learning
PDF Bib Video Supplement
Poisson Learning: Graph Based Semi-Supervised Learning At Very Low Label Rates
Jeff Calder • Brendan Cook • Matthew Thorpe • Dejan Slepčev
Keywords: Unsupervised and Semi-Supervised Learning • Applications - Computer Vision • Learning Theory • Learning Theory
PDF Bib Video Supplement
Negative Sampling in Semi-Supervised learning
John Chen • Vatsal Shah • Anastasios Kyrillidis
Keywords: Unsupervised and Semi-Supervised Learning • Deep Learning - Algorithms
PDF Bib Video Supplement
Sparse Subspace Clustering with Entropy-Norm
Liang Bai • Jiye Liang
Keywords: Unsupervised and Semi-Supervised Learning
PDF Bib Video
Data-Efficient Image Recognition with Contrastive Predictive Coding
Olivier Henaff
Keywords: Unsupervised and Semi-Supervised Learning • Applications - Computer Vision • Representation Learning • Transfer, Multitask and Meta-learning
PDF Bib Video Supplement
Topological Autoencoders
Michael Moor • Max Horn • Bastian A Rieck • Karsten Borgwardt
Keywords: Unsupervised and Semi-Supervised Learning • Deep Learning - Generative Models and Autoencoders • Representation Learning • Unsupervised and Semi-Supervised Learning
PDF Bib Video Supplement
Amortised Learning by Wake-Sleep
Li K Wenliang • Theodore Moskovitz • Heishiro Kanagawa • Maneesh Sahani
Keywords: Unsupervised and Semi-Supervised Learning • Probabilistic Inference - Models and Probabilistic Programming • Probabilistic Inference - Models and Probabilistic Programming
PDF Bib Video Supplement
On Efficient Low Distortion Ultrametric Embedding
Vincent Cohen-Addad • Karthik C. S. • Guillaume Lagarde
Keywords: Unsupervised and Semi-Supervised Learning • General Machine Learning Techniques • Unsupervised and Semi-Supervised Learning • Optimization - General
PDF Bib Video
Simple and sharp analysis of k-means||
Václav Rozhon
Keywords: Unsupervised and Semi-Supervised Learning
PDF Bib Video
Subspace Fitting Meets Regression: The Effects of Supervision and Orthonormality Constraints on Double Descent of Generalization Errors
Yehuda Dar • Paul M Mayer • Lorenzo Luzi • Richard Baraniuk
Keywords: Unsupervised and Semi-Supervised Learning • Unsupervised and Semi-Supervised Learning • Unsupervised and Semi-Supervised Learning
PDF Bib Video Supplement
Laplacian Regularized Few-Shot Learning
Imtiaz Masud Ziko • Jose Dolz • Eric Granger • Ismail Ben Ayed
Keywords: Unsupervised and Semi-Supervised Learning • Unsupervised and Semi-Supervised Learning • Sequential, Network, and Time-Series Modeling • Transfer, Multitask and Meta-learning
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Quantum Expectation-Maximization for Gaussian mixture models
Alessandro Luongo • Iordanis Kerenidis • Anupam Prakash
Keywords: Unsupervised and Semi-Supervised Learning • Applications - Language, Speech and Dialog
PDF Bib Video Supplement
Individual Fairness for k-Clustering
Sepideh Mahabadi • Ali Vakilian
Keywords: Unsupervised and Semi-Supervised Learning • Optimization - General • Fairness, Equity, Justice, and Safety
PDF Bib Video Supplement
DROCC: Deep Robust One-Class Classification
Sachin Goyal • Aditi Raghunathan • Moksh Jain • Harsha Vardhan Simhadri • Prateek Jain
Keywords: Unsupervised and Semi-Supervised Learning • Representation Learning
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Spread Divergence
Mingtian Zhang • Peter N Hayes • Thomas Bird • Raza Habib • David Barber
Keywords: Unsupervised and Semi-Supervised Learning • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods • Probabilistic Inference - Models and Probabilistic Programming
PDF Bib Video Supplement
Learning with Multiple Complementary Labels
LEI FENG • Takuo Kaneko • Bo Han • Gang Niu • Bo An • Masashi Sugiyama
Keywords: Unsupervised and Semi-Supervised Learning
PDF Bib Video Supplement
Topic Modeling via Full Dependence Mixtures
Dan Fisher • Mark Kozdoba • Shie Mannor
Keywords: Unsupervised and Semi-Supervised Learning • Applications - Other • Unsupervised and Semi-Supervised Learning • Optimization - Non-convex
PDF Bib Video Supplement
Progressive Identification of True Labels for Partial-Label Learning
Jiaqi Lv • Miao Xu • LEI FENG • Gang Niu • Xin Geng • Masashi Sugiyama
Keywords: Unsupervised and Semi-Supervised Learning • Deep Learning - Algorithms
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Learning Discrete Structured Representations by Adversarially Maximizing Mutual Information
Karl Stratos • Sam Wiseman
Keywords: Unsupervised and Semi-Supervised Learning • Applications - Language, Speech and Dialog • Sequential, Network, and Time-Series Modeling • Learning Theory
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A Pairwise Fair and Community-preserving Approach to k-Center Clustering
Brian Brubach • Darshan Chakrabarti • John P. Dickerson • Samir Khuller • Aravind Srinivasan • Leonidas Tsepenekas
Keywords: Unsupervised and Semi-Supervised Learning • Fairness, Equity, Justice, and Safety
PDF Bib Video Supplement
Coresets for Clustering in Graphs of Bounded Treewidth
Daniel Baker • Vladimir Braverman • Lingxiao Huang • Shaofeng H.-C. Jiang • Robert Krauthgamer • Xuan Wu
Keywords: Unsupervised and Semi-Supervised Learning • Optimization - Large Scale, Parallel and Distributed
PDF Bib Video Supplement
Multiclass Neural Network Minimization via Tropical Newton Polytope Approximation
Georgios Smyrnis • Petros Maragos
Keywords: Unsupervised and Semi-Supervised Learning • Supervised Learning
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Deep Reasoning Networks for Unsupervised Pattern De-mixing with Constraint Reasoning
Di Chen • Yiwei Bai • Wenting Zhao • Sebastian Ament • John Gregoire • Carla P Gomes
Keywords: Unsupervised and Semi-Supervised Learning
PDF Bib Video
k-means++: few more steps yield constant approximation
Davin Choo • Christoph Grunau • Julian Portmann • Václav Rozhon
Keywords: Unsupervised and Semi-Supervised Learning • Unsupervised and Semi-Supervised Learning • Unsupervised and Semi-Supervised Learning
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Label-Noise Robust Domain Adaptation
Xiyu Yu • Tongliang Liu • Mingming Gong • Kun Zhang • Kayhan Batmanghelich • Dacheng Tao
Keywords: Unsupervised and Semi-Supervised Learning • Transfer, Multitask and Meta-learning • Learning Theory
PDF Bib Video
On hyperparameter tuning in general clustering problemsm
Xinjie Fan • Yuguang Yue • Purnamrita Sarkar • Y. X. Rachel Wang
Keywords: Unsupervised and Semi-Supervised Learning • Sequential, Network, and Time-Series Modeling • Learning Theory
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T-Basis: a Compact Representation for Neural Networks
Anton Obukhov • Maxim Rakhuba • Stamatios Georgoulis • Menelaos Kanakis • Dengxin Dai • Luc Van Gool
Keywords: Unsupervised and Semi-Supervised Learning • Applications - Computer Vision • Deep Learning - General • General Machine Learning Techniques
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Layered Sampling for Robust Optimization Problems
Hu Ding • Zixiu Wang
Keywords: Unsupervised and Semi-Supervised Learning • Optimization - General
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Missing Data Imputation using Optimal Transport
Boris Muzellec • Julie Josse • Claire Boyer • Marco Cuturi
Keywords: Unsupervised and Semi-Supervised Learning
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Interferometric Graph Transform: a Deep Unsupervised Graph Representation
Edouard Oyallon
Keywords: Unsupervised and Semi-Supervised Learning • Deep Learning - Algorithms
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