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


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
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Problems with Shapley-value-based explanations as feature importance measures
I. Elizabeth Kumar • Suresh Venkatasubramanian • Carlos Scheidegger • Sorelle Friedler
Keywords: Accountability, Transparency and Interpretability
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When Explanations Lie: Why Many Modified BP Attributions Fail
Leon Sixt • Maximilian Granz • Tim Landgraf
Keywords: Accountability, Transparency and Interpretability • Deep Learning - Theory
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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
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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
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Interpolation between Residual and Non-Residual Networks
Zonghan Yang • Yang Liu • Chenglong Bao • Zuoqiang Shi
Keywords: Accountability, Transparency and Interpretability • Deep Learning - Algorithms
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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
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The Many Shapley Values for Model Explanation
Mukund Sundararajan • Amir Najmi
Keywords: Accountability, Transparency and Interpretability • Learning Theory
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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
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Invariant Rationalization
Shiyu Chang • Yang Zhang • Mo Yu • Tommi Jaakkola
Keywords: Accountability, Transparency and Interpretability • Applications - Language, Speech and Dialog
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Causal Strategic Linear Regression
Yonadav Shavit • Benjamin L Edelman • Brian Axelrod
Keywords: Accountability, Transparency and Interpretability • Learning Theory • Causality • Trustworthy Machine Learning
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Predictive Multiplicity in Classification
Charles Marx • Flavio Calmon • Berk Ustun
Keywords: Accountability, Transparency and Interpretability • Fairness, Equity, Justice, and Safety
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On Second-Order Group Influence Functions for Black-Box Predictions
Samyadeep Basu • Xuchen You • Soheil Feizi
Keywords: Accountability, Transparency and Interpretability • Trustworthy Machine Learning
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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
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Inverse Active Sensing: Modeling and Understanding Timely Decision-Making
Daniel Jarrett • Mihaela van der Schaar
Keywords: Accountability, Transparency and Interpretability • Decision-Making
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Robust and Stable Black Box Explanations
Himabindu Lakkaraju • Nino Arsov • Osbert Bastani
Keywords: Accountability, Transparency and Interpretability • Supervised Learning
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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
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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
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The Shapley Taylor Interaction Index
Mukund Sundararajan • Kedar Dhamdhere • Ashish Agarwal
Keywords: Accountability, Transparency and Interpretability • Learning Theory
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Efficient nonparametric statistical inference on population feature importance using Shapley values
Brian Williamson • Jean Feng
Keywords: Accountability, Transparency and Interpretability • Learning Theory
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Reverse-engineering deep ReLU networks
David Rolnick • Konrad Kording
Keywords: Accountability, Transparency and Interpretability • Deep Learning - Theory
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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
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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
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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
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Transparency Promotion with Model-Agnostic Linear Competitors
Hassan Rafique • Tong Wang • Qihang Lin • Arshia Sighani
Keywords: Accountability, Transparency and Interpretability
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Born-again Tree Ensembles
Thibaut Vidal • Maximilian Schiffer
Keywords: Accountability, Transparency and Interpretability • Supervised Learning • Optimization - General
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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
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Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations
Florian Tramèr • Jens Behrmann • Nicholas Carlini • Nicolas Papernot • Jörn-Henrik Jacobsen
Keywords: Adversarial Examples
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Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
Francesco Croce • Matthias Hein
Keywords: Adversarial Examples
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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
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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
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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
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Robustness to Programmable String Transformations via Augmented Abstract Training
Yuhao Zhang • Aws Albarghouthi • Loris D'Antoni
Keywords: Adversarial Examples • Applications - Language, Speech and Dialog
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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
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Adversarial Robustness via Runtime Masking and Cleansing
Yi-Hsuan Wu • Chia-Hung Yuan • Shan-Hung Wu
Keywords: Adversarial Examples
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Adversarial Neural Pruning with Latent Vulnerability Suppression
Divyam Madaan • Jinwoo Shin • Sung Ju Hwang
Keywords: Adversarial Examples • Trustworthy Machine Learning
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Adversarial Robustness Against the Union of Multiple Perturbation Models
Pratyush Maini • Eric Wong • Zico Kolter
Keywords: Adversarial Examples
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Stronger and Faster Wasserstein Adversarial Attacks
Kaiwen Wu • Allen Houze Wang • Yaoliang Yu
Keywords: Adversarial Examples • Optimization - Convex
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More Data Can Expand The Generalization Gap Between Adversarially Robust and Standard Models
Lin Chen • Yifei Min • Mingrui Zhang • Amin Karbasi
Keywords: Adversarial Examples
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Adversarial Robustness for Code
Pavol Bielik • Martin Vechev
Keywords: Adversarial Examples • Applications - Other
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Defense Through Diverse Directions
Christopher M Bender • Yang Li • Yifeng Shi • Michael K. Reiter • Junier Oliva
Keywords: Adversarial Examples • Deep Learning - General
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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
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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
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Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack
Francesco Croce • Matthias Hein
Keywords: Adversarial Examples
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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
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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
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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
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Second-Order Provable Defenses against Adversarial Attacks
Sahil Singla • Soheil Feizi
Keywords: Adversarial Examples • Deep Learning - Algorithms • Deep Learning - Theory
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Towards Understanding the Regularization of Adversarial Robustness on Neural Networks
Yuxin Wen • Shuai Li • Kui Jia
Keywords: Adversarial Examples • Learning Theory
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Adversarial Risk via Optimal Transport and Optimal Couplings
Muni Sreenivas Pydi • Varun Jog
Keywords: Adversarial Examples • Learning Theory • Trustworthy Machine Learning
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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
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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
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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
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PackIt: A Virtual Environment for Geometric Planning
Akit Goyal • Jia Deng
Keywords: Applications - Computer Vision
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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
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Loss Function Search for Face Recognition
Xiaobo Wang • Shuo Wang • Cheng Chi • Shifeng Zhang • Tao Mei
Keywords: Applications - Computer Vision • Deep Learning - Algorithms
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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
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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
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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
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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
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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
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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
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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
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One Size Fits All: Can We Train One Denoiser for All Noise Levels?
Abhiram Gnanasambandam • Stanley Chan
Keywords: Applications - Computer Vision
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Analytic Marching: An Analytic Meshing Solution from Deep Implicit Surface Networks
Jiabao Lei • Kui Jia
Keywords: Applications - Computer Vision • Deep Learning - General
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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
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Message Passing Least Squares Framework and its Application to Rotation Synchronization
Yunpeng Shi • Gilad Lerman
Keywords: Applications - Computer Vision • Learning Theory • Optimization - Non-convex
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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
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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
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Operation-Aware Soft Channel Pruning using Differentiable Masks
Minsoo Kang • Bohyung Han
Keywords: Applications - Computer Vision • Deep Learning - General • Deep Learning - General
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Adversarial Nonnegative Matrix Factorization
lei luo • Yanfu Zhang • Heng Huang
Keywords: Applications - Computer Vision • General Machine Learning Techniques
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Learning Factorized Weight Matrix for Joint Filtering
Xiangyu Xu • Yongrui Ma • Wenxiu Sun
Keywords: Applications - Computer Vision
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Implicit Geometric Regularization for Learning Shapes
Amos Gropp • Lior Yariv • Niv Haim • Matan Atzmon • Yaron Lipman
Keywords: Applications - Computer Vision • Deep Learning - General
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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
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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
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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
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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
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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
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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
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Discriminative Adversarial Search for Abstractive Summarization
Thomas Scialom • Paul-Alexis Dray • Sylvain Lamprier • Benjamin Piwowarski • Jacopo Staiano
Keywords: Applications - Language, Speech and Dialog
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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
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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
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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
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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
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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
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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
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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
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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
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Explainable and Discourse Topic-aware Neural Language Understanding
Yatin Chaudhary • Hinrich Schütze • Pankaj Gupta
Keywords: Applications - Language, Speech and Dialog • Representation Learning
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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
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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
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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
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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
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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
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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
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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
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Structural Language Models of Code
Uri Alon • Roy Sadaka • Omer Levy • Eran Yahav
Keywords: Applications - Language, Speech and Dialog • Code Generation
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Non-Autoregressive Neural Text-to-Speech
Kainan Peng • Wei Ping • Zhao Song • Kexin Zhao
Keywords: Applications - Language, Speech and Dialog
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Voice Separation with an Unknown Number of Multiple Speakers
Eliya Nachmani • Yossi Adi • Lior Wolf
Keywords: Applications - Language, Speech and Dialog • Deep Learning - General
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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
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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
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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
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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
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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
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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
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Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models
Yuta Saito • Shota Yasui
Keywords: Causality • Evaluation metrics
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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
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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
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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
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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
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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
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On the Generalization Benefit of Noise in Stochastic Gradient Descent
Samuel L. Smith • Erich Elsen • Soham De
Keywords: Deep Learning - General • Applications - Computer Vision
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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
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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
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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
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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
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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
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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
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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
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Low-loss connection of weight vectors: distribution-based approaches
Ivan Anokhin • Dmitry Yarotsky
Keywords: Deep Learning - Theory • Applications - Other • Supervised Learning
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Constructive Universal High-Dimensional Distribution Generation through Deep ReLU Networks
Dmytro Perekrestenko • Stephan Müller • Helmut Bölcskei
Keywords: Deep Learning - Theory • Learning Theory
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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
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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
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Topologically Densified Distributions
Christoph Hofer • Florian Graf • Marc Niethammer • Roland Kwitt
Keywords: Deep Learning - Theory • Representation Learning • Learning Theory • Learning Theory
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Generalization and Representational Limits of Graph Neural Networks
Vikas K Garg • Stefanie Jegelka • Tommi Jaakkola
Keywords: Deep Learning - Theory • Applications - Other
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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
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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
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Bounding the fairness and accuracy of classifiers from population statistics
Sivan Sabato • Elad Yom-Tov
Keywords: Fairness, Equity, Justice, and Safety
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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
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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
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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
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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
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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
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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
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Sparse Gaussian Processes with Spherical Harmonic Features
Vincent Dutordoir • Nicolas Durrande • James Hensman
Keywords: Gaussian Processes • Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods
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Private Outsourced Bayesian Optimization
Dmitrii Kharkovskii • Zhongxiang Dai • Bryan Kian Hsiang Low
Keywords: Gaussian Processes • Online Learning, Active Learning, and Bandits
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Parametric Gaussian Process Regressors
Martin Jankowiak • Geoff Pleiss • Jacob R. Gardner
Keywords: Gaussian Processes
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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
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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
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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
PDF      Bib  Video  Supplement 

 

 


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
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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
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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
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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
PDF      Bib  Video  Supplement 

 

 


Harmonic Decompositions of Convolutional Networks
Meyer Scetbon • Zaid Harchaoui
Keywords: General Machine Learning Techniques • Deep Learning - Theory • Learning Theory
PDF      Bib  Video 

 

 


 

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
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Decentralised Learning with Random Features and Distributed Gradient Descent
Dominic Richards • Patrick Rebeschini • Lorenzo Rosasco
Keywords: Learning Theory • Learning Theory
PDF      Bib  Video  Supplement 

 

 


Sharp Statistical Guaratees for Adversarially Robust Gaussian Classification
Chen Dan • Yuting Wei • Pradeep Ravikumar
Keywords: Learning Theory • Adversarial Examples
PDF      Bib  Video  Supplement 

 

 


Stochastic Regret Minimization in Extensive-Form Games
Gabriele Farina • Christian Kroer • Tuomas Sandholm
Keywords: Learning Theory
PDF      Bib  Video  Supplement 

 

 


Recht-Re Noncommutative Arithmetic-Geometric Mean Conjecture is False
Zehua Lai • Lek-Heng Lim
Keywords: Learning Theory • General Machine Learning Techniques • Optimization - General
PDF      Bib  Video  Supplement 

 

 


Optimal Estimator for Unlabeled Linear Regression
hang zhang • Ping Li
Keywords: Learning Theory • Optimization - General
PDF      Bib  Video  Supplement 

 

 


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
PDF      Bib  Video  Supplement 

 

 


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
PDF      Bib  Video 

 

 


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
PDF      Bib  Video  Supplement 

 

 


Efficiently Learning Adversarially Robust Halfspaces with Noise
Omar Montasser • Surbhi Goel • Ilias Diakonikolas • Nathan Srebro
Keywords: Learning Theory • Learning Theory • Adversarial Examples
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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
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Estimating the Number and Effect Sizes of Non-null Hypotheses
Jennifer R Brennan • Ramya Korlakai Vinayak • Kevin Jamieson
Keywords: Learning Theory
PDF      Bib  Video  Supplement 

 

 


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
PDF      Bib  Video  Supplement 

 

 


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
PDF      Bib  Video  Supplement 

 

 


Strategyproof Mean Estimation from Multiple-Choice Questions
Anson Kahng • Gregory Kehne • Ariel D. Procaccia
Keywords: Learning Theory
PDF      Bib  Video  Supplement 

 

 


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
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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
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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
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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
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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
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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
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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
PDF      Bib  Video  Supplement 

 

 


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
PDF      Bib  Video  Supplement 

 

 


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
PDF      Bib  Video  Supplement 

 

 


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
PDF      Bib  Video 

 

 


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
PDF      Bib  Video  Supplement 

 


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
PDF      Bib  Video  Supplement 

 

 


Robust Outlier Arm Identification
Yinglun Zhu • Sumeet Katariya • Robert Nowak
Keywords: Online Learning, Active Learning, and Bandits • Trustworthy Machine Learning
PDF      Bib  Video  Supplement 

 

 


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
PDF      Bib  Video  Supplement 

 

 


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
PDF      Bib  Video 

 

 


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
PDF      Bib  Video  Supplement 

 

 


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
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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
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Improved Optimistic Algorithms for Logistic Bandits
Louis Faury • Marc Abeille • Clément Calauzènes • Olivier Fercoq
Keywords: Online Learning, Active Learning, and Bandits
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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
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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
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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
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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
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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
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Preselection Bandits
Viktor Bengs • Eyke Hüllermeier
Keywords: Online Learning, Active Learning, and Bandits • Supervised Learning • Optimization - General
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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
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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
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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
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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
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Dual Mirror Descent for Online Allocation Problems
Santiago Balseiro • Haihao Lu • Vahab Mirrokni
Keywords: Online Learning, Active Learning, and Bandits • Optimization - Convex
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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
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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
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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
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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
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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
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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
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Online Control of the False Coverage Rate and False Sign Rate
Asaf Weinstein • Aaditya Ramdas
Keywords: Online Learning, Active Learning, and Bandits
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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
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Adaptive Region-Based Active Learning
Corinna Cortes • Giulia DeSalvo • Claudio Gentile • Mehryar Mohri • Ningshan Zhang
Keywords: Online Learning, Active Learning, and Bandits
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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
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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
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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
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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
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Bandits with Adversarial Scaling
Thodoris Lykouris • Vahab Mirrokni • Renato Paes Leme
Keywords: Online Learning, Active Learning, and Bandits • Learning Theory
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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
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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
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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
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Continuous-time Lower Bounds for Gradient-based Algorithms
Michael Muehlebach • Michael Jordan
Keywords: Optimization - Convex • Optimization - Large Scale, Parallel and Distributed • Optimization - Non-convex
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Boosting Frank-Wolfe by Chasing Gradients
Cyrille W. Combettes • Sebastian Pokutta
Keywords: Optimization - Convex • Supervised Learning
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On the Convergence of Nesterov's Accelerated Gradient Method in Stochastic Settings
Mahmoud Assran • Mike Rabbat
Keywords: Optimization - Convex
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Almost Tune-Free Variance Reduction
Bingcong Li • Lingda Wang • Georgios B. Giannakis
Keywords: Optimization - Convex • Optimization - Large Scale, Parallel and Distributed
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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
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Random extrapolation for primal-dual coordinate descent
Ahmet Alacaoglu • Olivier Fercoq • Volkan Cevher
Keywords: Optimization - Convex • Optimization - Large Scale, Parallel and Distributed
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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
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Stochastic Optimization for Regularized Wasserstein Estimators
Marin Ballu • Quentin Berthet • Francis Bach
Keywords: Optimization - Convex • Learning Theory
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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
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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
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Efficiently Solving MDPs with Stochastic Mirror Descent
Yujia Jin • Aaron Sidford
Keywords: Optimization - Convex • Learning Theory • Reinforcement Learning - Theory
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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
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Debiased Sinkhorn barycenters
Hicham Janati • Marco Cuturi • Alexandre Gramfort
Keywords: Optimization - Convex
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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
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A new regret analysis for Adam-type algorithms
Ahmet Alacaoglu • Yura Malitsky • Panayotis Mertikopoulos • Volkan Cevher
Keywords: Optimization - Convex • Deep Learning - General
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Universal Asymptotic Optimality of Polyak Momentum
Damien Scieur • Fabian Pedregosa
Keywords: Optimization - Convex • Deep Learning - General • Optimization - Large Scale, Parallel and Distributed
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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
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Adaptive Gradient Descent without Descent
Yura Malitsky • Konstantin Mishchenko
Keywords: Optimization - Convex
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Stochastic Coordinate Minimization with Progressive Precision for Stochastic Convex Optimization
Sudeep Salgia • Qing Zhao • Sattar Vakili
Keywords: Optimization - Convex • Learning Theory
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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
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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
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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
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Inexact Tensor Methods with Dynamic Accuracies
Nikita Doikov • Yurii Nesterov
Keywords: Optimization - Convex • Optimization - General
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Anderson Acceleration of Proximal Gradient Methods
Vien Van Mai • Mikael Johansson
Keywords: Optimization - Convex • Optimization - Non-convex
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An Accelerated DFO Algorithm for Finite-sum Convex Functions
Yuwen Chen • Antonio Orvieto • Aurelien Lucchi
Keywords: Optimization - Convex
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Optimal Randomized First-Order Methods for Least-Squares Problems
Jonathan Lacotte • Mert Pilanci
Keywords: Optimization - Convex • Optimization - Large Scale, Parallel and Distributed
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Acceleration through spectral density estimation
Fabian Pedregosa • Damien Scieur
Keywords: Optimization - Convex • Deep Learning - Algorithms • Deep Learning - General • Optimization - Large Scale, Parallel and Distributed
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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
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Optimization - General


Can Stochastic Zeroth-Order Frank-Wolfe Method Converge Faster for Non-Convex Problems?
Hongchang Gao • Heng Huang
Keywords: Optimization - General
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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
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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
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Finite-Time Convergence in Continuous-Time Optimization
Orlando Romero • Mouhacine Benosman
Keywords: Optimization - General • Deep Learning - General • Optimization - Convex • Optimization - Non-convex
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Safe screening rules for L0-regression from Perspective Relaxations
Alper Atamtürk • Andrés Gómez
Keywords: Optimization - General • Unsupervised and Semi-Supervised Learning • Optimization - General
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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
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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
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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
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On the Power of Compressed Sensing with Generative Models
Akshay Kamath • Eric Price • Sushrut Karmalkar
Keywords: Optimization - General • Deep Learning - Generative Models and Autoencoders
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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
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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
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Sparse Convex Optimization via Adaptively Regularized Hard Thresholding
Kyriakos Axiotis • Maxim Sviridenko
Keywords: Optimization - General • Optimization - Convex
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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
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Obtaining Adjustable Regularization for Free via Iterate Averaging
Jingfeng Wu • Vladimir Braverman • Lin Yang
Keywords: Optimization - General • Deep Learning - General • Supervised Learning
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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
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Streaming k-Submodular Maximization under Noise subject to Size Constraint
Lan N Nguyen • My T. Thai
Keywords: Optimization - General • Streaming Algorithm
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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
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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