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Timezone: Europe/Vienna
Full Schedule Mon 7/13 Tue 7/14 Wed 7/15 Thu 7/16 Fri 7/17 Sat 7/18
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Poster
12:00 AM - 12:45 AM
9 Events in this session
Deep Streaming Label Learning
Zhen Wang · Liu Liu · Dacheng Tao
Stochastic Optimization for Regularized Wasserstein Estimators
Marin Ballu · Quentin Berthet · Francis Bach
Curse of Dimensionality on Randomized Smoothing for Certifiable Robustness
Aounon Kumar · Alexander Levine · Tom Goldstein · Soheil Feizi
Learning Factorized Weight Matrix for Joint Filtering
Xiangyu Xu · Yongrui Ma · Wenxiu Sun
Neural Network Control Policy Verification With Persistent Adversarial Perturbation
Yuh-Shyang Wang · Tsui-Wei Weng · Luca Daniel
Off-Policy Actor-Critic with Shared Experience Replay
Simon Schmitt · Matteo Hessel · Karen Simonyan
Randomization matters How to defend against strong adversarial attacks
Rafael Pinot · Raphael Ettedgui · Geovani Rizk · Yann Chevaleyre · Jamal Atif
Reliable Fidelity and Diversity Metrics for Generative Models
Muhammad Ferjad Naeem · Seong Joon Oh · Yunjey Choi · Youngjung Uh · Jaejun Yoo
Spectral Subsampling MCMC for Stationary Time Series
Robert Salomone · Matias Quiroz · Robert kohn · Mattias Villani · Minh-Ngoc Tran
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Poster
2:00 AM - 2:45 AM
82 Events in this session
Active Learning on Attributed Graphs via Graph Cognizant Logistic Regression and Preemptive Query Generation
Florence Regol · Soumyasundar Pal · Yingxue Zhang · Mark Coates
Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODE
Juntang Zhuang · Nicha Dvornek · Xiaoxiao Li · Sekhar Tatikonda · Xenophon Papademetris · James Duncan
Born-again Tree Ensembles
Thibaut Vidal · Maximilian Schiffer
Empirical Study of the Benefits of Overparameterization in Learning Latent Variable Models
Rares-Darius Buhai · Yoni Halpern · Yoon Kim · Andrej Risteski · David Sontag
Enhancing Simple Models by Exploiting What They Already Know
Amit Dhurandhar · Karthikeyan Shanmugam · Ronny Luss
Error-Bounded Correction of Noisy Labels
Songzhu Zheng · Pengxiang Wu · Aman Goswami · Mayank Goswami · Dimitris Metaxas · Chao Chen
FACT: A Diagnostic for Group Fairness Trade-offs
Joon Kim · Jiahao Chen · Ameet Talwalkar
Improving Robustness of Deep-Learning-Based Image Reconstruction
Ankit Raj · Yoram Bresler · Bo Li
Learning Selection Strategies in Buchberger’s Algorithm
Dylan Peifer · Michael Stillman · Daniel Halpern-Leistner
Learning the Valuations of a $k$-demand Agent
Hanrui Zhang · Vincent Conitzer
Linear Convergence of Randomized Primal-Dual Coordinate Method for Large-scale Linear Constrained Convex Programming
Daoli Zhu · Lei Zhao
New Oracle-Efficient Algorithms for Private Synthetic Data Release
Giuseppe Vietri · Grace Tian · Mark Bun · Thomas Steinke · Steven Wu
p-Norm Flow Diffusion for Local Graph Clustering
Kimon Fountoulakis · Di Wang · Shenghao Yang
Privately detecting changes in unknown distributions
Rachel Cummings · Sara Krehbiel · Yuliia Lut · Wanrong Zhang
Robust Pricing in Dynamic Mechanism Design
Yuan Deng · Sébastien Lahaie · Vahab Mirrokni
Stochastic Flows and Geometric Optimization on the Orthogonal Group
Krzysztof Choromanski · David Cheikhi · Jared Quincy Davis · Valerii Likhosherstov · Achille Nazaret · Achraf Bahamou · Xingyou Song · Mrugank Akarte · Jack Parker-Holder · Jacob Bergquist · Yuan Gao · Aldo Pacchiano · Tamas Sarlos · Adrian Weller · Vikas Sindhwani
A Flexible Framework for Nonparametric Graphical Modeling that Accommodates Machine Learning
Yunhua Xiang · Noah Simon
Almost Tune-Free Variance Reduction
Bingcong Li · Lingda Wang · Georgios B. Giannakis
A Pairwise Fair and Community-preserving Approach to k-Center Clustering
Brian Brubach · Darshan Chakrabarti · John P Dickerson · Samir Khuller · Aravind Srinivasan · Leonidas Tsepenekas
BINOCULARS for efficient, nonmyopic sequential experimental design
Shali Jiang · Henry Chai · Javier Gonzalez · Roman Garnett
Black-Box Variational Inference as a Parametric Approximation to Langevin Dynamics
Matthew Hoffman · Yian Ma
Calibration, Entropy Rates, and Memory in Language Models
Mark Braverman · Xinyi Chen · Sham Kakade · Karthik Narasimhan · Cyril Zhang · Yi Zhang
CLUB: A Contrastive Log-ratio Upper Bound of Mutual Information
Pengyu Cheng · Weituo Hao · Shuyang Dai · Jiachang Liu · Zhe Gan · Lawrence Carin
Concise Explanations of Neural Networks using Adversarial Training
Prasad Chalasani · Jiefeng Chen · Amrita Roy Chowdhury · Xi Wu · Somesh Jha
Constrained Markov Decision Processes via Backward Value Functions
Harsh Satija · Philip Amortila · Joelle Pineau
Continuous Graph Neural Networks
Louis-Pascal Xhonneux · Meng Qu · Jian Tang
Estimating Q(s,s') with Deep Deterministic Dynamics Gradients
Ashley Edwards · Himanshu Sahni · Rosanne Liu · Jane Hung · Ankit Jain · Rui Wang · Adrien Ecoffet · Thomas Miconi · Charles Isbell · Jason Yosinski
Estimation of Bounds on Potential Outcomes For Decision Making
Maggie Makar · Fredrik Johansson · John Guttag · David Sontag
Fiedler Regularization: Learning Neural Networks with Graph Sparsity
Edric Tam · David Dunson
From ImageNet to Image Classification: Contextualizing Progress on Benchmarks
Dimitris Tsipras · Shibani Santurkar · Logan Engstrom · Andrew Ilyas · Aleksander Madry
Graph Homomorphism Convolution
Hoang NT · Takanori Maehara
GraphOpt: Learning Optimization Models of Graph Formation
Rakshit Trivedi · Jiachen Yang · Hongyuan Zha
History-Gradient Aided Batch Size Adaptation for Variance Reduced Algorithms
Kaiyi Ji · Zhe Wang · Bowen Weng · Yi Zhou · Wei Zhang · Yingbin LIANG
How to Train Your Neural ODE: the World of Jacobian and Kinetic Regularization
Chris Finlay · Joern-Henrik Jacobsen · Levon Nurbekyan · Adam Oberman
Improved Bounds on Minimax Regret under Logarithmic Loss via Self-Concordance
Blair Bilodeau · Dylan Foster · Daniel Roy
Interpreting Robust Optimization via Adversarial Influence Functions
Zhun Deng · Cynthia Dwork · Jialiang Wang · Linjun Zhang
Invariant Rationalization
Shiyu Chang · Yang Zhang · Mo Yu · Tommi Jaakkola
Latent Variable Modelling with Hyperbolic Normalizing Flows
Joey Bose · Ariella Smofsky · Renjie Liao · Prakash Panangaden · Will Hamilton
Learning Robot Skills with Temporal Variational Inference
Tanmay Shankar · Abhinav Gupta
Learning with Bounded Instance- and Label-dependent Label Noise
Jiacheng Cheng · Tongliang Liu · Kotagiri Ramamohanarao · Dacheng Tao
Low-Rank Bottleneck in Multi-head Attention Models
Srinadh Bhojanapalli · Chulhee Yun · Ankit Singh Rawat · Sashank Jakkam Reddi · Sanjiv Kumar
Maximum Entropy Gain Exploration for Long Horizon Multi-goal Reinforcement Learning
Silviu Pitis · Harris Chan · Stephen Zhao · Bradly Stadie · Jimmy Ba
Minimax-Optimal Off-Policy Evaluation with Linear Function Approximation
Yaqi Duan · Zeyu Jia · Mengdi Wang
Multi-Agent Routing Value Iteration Network
Quinlan Sykora · Mengye Ren · Raquel Urtasun
Neural Datalog Through Time: Informed Temporal Modeling via Logical Specification
Hongyuan Mei · Guanghui Qin · Minjie Xu · Jason Eisner
Online Learning with Dependent Stochastic Feedback Graphs
Corinna Cortes · Giulia DeSalvo · Claudio Gentile · Mehryar Mohri · Ningshan Zhang
On the Expressivity of Neural Networks for Deep Reinforcement Learning
Kefan Dong · Yuping Luo · Tianhe (Kevin) Yu · Chelsea Finn · Tengyu Ma
Optimizing Dynamic Structures with Bayesian Generative Search
Minh Hoang · Carleton Kingsford
Piecewise Linear Regression via a Difference of Convex Functions
Ali Siahkamari · Aditya Gangrade · Brian Kulis · Venkatesh Saligrama
Reward-Free Exploration for Reinforcement Learning
Chi Jin · Akshay Krishnamurthy · Max Simchowitz · Tiancheng Yu
Rigging the Lottery: Making All Tickets Winners
Utku Evci · Trevor Gale · Jacob Menick · Pablo Samuel Castro · Erich Elsen
Scalable Differentiable Physics for Learning and Control
Yi-Ling Qiao · Junbang Liang · Vladlen Koltun · Ming Lin
Skew-Fit: State-Covering Self-Supervised Reinforcement Learning
Vitchyr Pong · Murtaza Dalal · Steven Lin · Ashvin Nair · Shikhar Bahl · Sergey Levine
Sparse Convex Optimization via Adaptively Regularized Hard Thresholding
Kyriakos Axiotis · Maxim Sviridenko
Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks
Blake Bordelon · Abdulkadir Canatar · Cengiz Pehlevan
Strategic Classification is Causal Modeling in Disguise
John Miller · Smitha Milli · Moritz Hardt
Transparency Promotion with Model-Agnostic Linear Competitors
Hassan Rafique · Tong Wang · Qihang Lin · Arshia Singhani
Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere
Tongzhou Wang · Phillip Isola
Universal Equivariant Multilayer Perceptrons
Siamak Ravanbakhsh
Upper bounds for Model-Free Row-Sparse Principal Component Analysis
Guanyi Wang · Santanu Dey
Projection-free Distributed Online Convex Optimization with $O(\sqrt{T})$ Communication Complexity
Yuanyu Wan · Wei-Wei Tu · Lijun Zhang
Representation Learning via Adversarially-Contrastive Optimal Transport
Anoop Cherian · Shuchin Aeron
A Graph to Graphs Framework for Retrosynthesis Prediction
Chence Shi · Minkai Xu · Hongyu Guo · Ming Zhang · Jian Tang
A Tree-Structured Decoder for Image-to-Markup Generation
Jianshu Zhang · Jun Du · Yongxin Yang · Yi-Zhe Song · Si Wei · Lirong Dai
Cost-effectively Identifying Causal Effects When Only Response Variable is Observable
Tian-Zuo Wang · Xi-Zhu Wu · Sheng-Jun Huang · Zhi-Hua Zhou
Does the Markov Decision Process Fit the Data: Testing for the Markov Property in Sequential Decision Making
Chengchun Shi · Runzhe Wan · Rui Song · Wenbin Lu · Ling Leng
Do We Need Zero Training Loss After Achieving Zero Training Error?
Takashi Ishida · Ikko Yamane · Tomoya Sakai · Gang Niu · Masashi Sugiyama
DropNet: Reducing Neural Network Complexity via Iterative Pruning
Chong Min John Tan · Mehul Motani
Dual-Path Distillation: A Unified Framework to Improve Black-Box Attacks
Yonggang Zhang · Ya Li · Tongliang Liu · Xinmei Tian
Learning Autoencoders with Relational Regularization
Hongteng Xu · Dixin Luo · Ricardo Henao · Svati Shah · Lawrence Carin
Minimax Weight and Q-Function Learning for Off-Policy Evaluation
Masatoshi Uehara · Jiawei Huang · Nan Jiang
More Information Supervised Probabilistic Deep Face Embedding Learning
Ying Huang · Shangfeng Qiu · Wenwei Zhang · Xianghui Luo · Jinzhuo Wang
Multinomial Logit Bandit with Low Switching Cost
Kefan Dong · Yingkai Li · Qin Zhang · Yuan Zhou
Multi-objective Bayesian Optimization using Pareto-frontier Entropy
Shinya Suzuki · Shion Takeno · Tomoyuki Tamura · Kazuki Shitara · Masayuki Karasuyama
Non-autoregressive Machine Translation with Disentangled Context Transformer
Jungo Kasai · James Cross · Marjan Ghazvininejad · Jiatao Gu
On the (In)tractability of Computing Normalizing Constants for the Product of Determinantal Point Processes
Naoto Ohsaka · Tatsuya Matsuoka
On the Power of Compressed Sensing with Generative Models
Akshay Kamath · Eric Price · Sushrut Karmalkar
Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup
Jang-Hyun Kim · Wonho Choo · Hyun Oh Song
Sample Complexity Bounds for 1-bit Compressive Sensing and Binary Stable Embeddings with Generative Priors
Zhaoqiang Liu · Selwyn Gomes · Avtansh Tiwari · Jonathan Scarlett
Self-Attentive Associative Memory
Hung Le · Truyen Tran · Svetha Venkatesh
Sparse Subspace Clustering with Entropy-Norm
Liang Bai · Jiye Liang
Transformer Hawkes Process
Simiao Zuo · Haoming Jiang · Zichong Li · Tuo Zhao · Hongyuan Zha
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Poster
3:00 AM - 3:45 AM
85 Events in this session
Acceleration through spectral density estimation
Fabian Pedregosa · Damien Scieur
Adaptive Adversarial Multi-task Representation Learning
YUREN MAO · Weiwei Liu · Xuemin Lin
Amortized Finite Element Analysis for Fast PDE-Constrained Optimization
Tianju Xue · Alex Beatson · Sigrid Adriaenssens · Ryan P. Adams
Analytic Marching: An Analytic Meshing Solution from Deep Implicit Surface Networks
Jiabao Lei · Kui Jia
Differentiable Product Quantization for End-to-End Embedding Compression
Ting Chen · Lala Li · Yizhou Sun
Do GANs always have Nash equilibria?
Farzan Farnia · Asuman Ozdaglar
FR-Train: A Mutual Information-Based Approach to Fair and Robust Training
Yuji Roh · Kangwook Lee · Steven Whang · Changho Suh
Improving Transformer Optimization Through Better Initialization
Xiao Shi Huang · Felipe Perez · Jimmy Ba · Maksims Volkovs
On a projective ensemble approach to two sample test for equality of distributions
Zhimei Li · Yaowu Zhang
Sharp Statistical Guaratees for Adversarially Robust Gaussian Classification
Chen Dan · Yuting Wei · Pradeep Ravikumar
Sparsified Linear Programming for Zero-Sum Equilibrium Finding
Brian Zhang · Tuomas Sandholm
Task Understanding from Confusing Multi-task Data
Xin Su · Yizhou Jiang · Shangqi Guo · Feng Chen
Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks
Micah Goldblum · Steven Reich · Liam Fowl · Renkun Ni · Valeriia Cherepanova · Tom Goldstein
Adaptive Region-Based Active Learning
Corinna Cortes · Giulia DeSalvo · Claudio Gentile · Mehryar Mohri · Ningshan Zhang
A Generic First-Order Algorithmic Framework for Bi-Level Programming Beyond Lower-Level Singleton
Risheng Liu · Pan Mu · Xiaoming Yuan · Shangzhi Zeng · Jin Zhang
Bandits for BMO Functions
Tianyu Wang · Cynthia Rudin
Black-Box Methods for Restoring Monotonicity
Evangelia Gergatsouli · Brendan Lucier · Christos Tzamos
Bridging the Gap Between f-GANs and Wasserstein GANs
Jiaming Song · Stefano Ermon
Causal Inference using Gaussian Processes with Structured Latent Confounders
Sam Witty · Kenta Takatsu · David Jensen · Vikash Mansinghka
Convex Calibrated Surrogates for the Multi-Label F-Measure
Mingyuan Zhang · Harish Guruprasad Ramaswamy · Shivani Agarwal
Convex Representation Learning for Generalized Invariance in Semi-Inner-Product Space
Yingyi Ma · Vignesh Ganapathiraman · Yaoliang Yu · Xinhua Zhang
Countering Language Drift with Seeded Iterated Learning
Yuchen Lu · Soumye Singhal · Florian Strub · Aaron Courville · Olivier Pietquin
Deep Reinforcement Learning with Smooth Policy
Qianli Shen · Yan Li · Haoming Jiang · Zhaoran Wang · Tuo Zhao
Does label smoothing mitigate label noise?
Michal Lukasik · Srinadh Bhojanapalli · Aditya Menon · Sanjiv Kumar
Fast Deterministic CUR Matrix Decomposition with Accuracy Assurance
Yasutoshi Ida · Sekitoshi Kanai · Yasuhiro Fujiwara · Tomoharu Iwata · Koh Takeuchi · Hisashi Kashima
Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters
Wenhui Yu · Zheng Qin
Input-Sparsity Low Rank Approximation in Schatten Norm
Yi Li · David Woodruff
Linear Lower Bounds and Conditioning of Differentiable Games
Adam Ibrahim · Waïss Azizian · Gauthier Gidel · Ioannis Mitliagkas
Mapping natural-language problems to formal-language solutions using structured neural representations
Kezhen Chen · Qiuyuan Huang · Hamid Palangi · Paul Smolensky · Ken Forbus · Jianfeng Gao
Meta Variance Transfer: Learning to Augment from the Others
Seong-Jin Park · Seungju Han · Ji-won Baek · Insoo Kim · Juhwan Song · Hae Beom Lee · Jae-Joon Han · Sung Ju Hwang
Multi-Task Learning with User Preferences: Gradient Descent with Controlled Ascent in Pareto Optimization
Debabrata Mahapatra · Vaibhav Rajan
On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems
Tianyi Lin · Chi Jin · Michael Jordan
PDO-eConvs: Partial Differential Operator Based Equivariant Convolutions
Zhengyang Shen · Lingshen He · Zhouchen Lin · Jinwen Ma
Refined bounds for algorithm configuration: The knife-edge of dual class approximability
Nina Balcan · Tuomas Sandholm · Ellen Vitercik
Robustifying Sequential Neural Processes
Jaesik Yoon · Gautam Singh · Sungjin Ahn
Robustness to Programmable String Transformations via Augmented Abstract Training
Yuhao Zhang · Aws Albarghouthi · Loris D'Antoni
Sequence Generation with Mixed Representations
Lijun Wu · Shufang Xie · Yingce Xia · Yang Fan · Jian-Huang Lai · Tao Qin · Tie-Yan Liu
Sharp Composition Bounds for Gaussian Differential Privacy via Edgeworth Expansion
Qinqing Zheng · Jinshuo Dong · Qi Long · Weijie Su
Stochastic Coordinate Minimization with Progressive Precision for Stochastic Convex Optimization
Sudeep Salgia · Qing Zhao · Sattar Vakili
Stochastic Gauss-Newton Algorithms for Nonconvex Compositional Optimization
Quoc Tran-Dinh · Nhan H Pham · Lam Nguyen
Understanding the Impact of Model Incoherence on Convergence of Incremental SGD with Random Reshuffle
Shaocong Ma · Yi Zhou
Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies
Shengpu Tang · Aditya Modi · Michael Sjoding · Jenna Wiens
ConQUR: Mitigating Delusional Bias in Deep Q-Learning
DiJia Su · Jayden Ooi · Tyler Lu · Dale Schuurmans · Craig Boutilier
Educating Text Autoencoders: Latent Representation Guidance via Denoising
Tianxiao Shen · Jonas Mueller · Regina Barzilay · Tommi Jaakkola
Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation
Xiang Jiang · Qicheng Lao · Stan Matwin · Mohammad Havaei
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 Varshney
Multi-Objective Molecule Generation using Interpretable Substructures
Wengong Jin · Regina Barzilay · Tommi Jaakkola
On Efficient Constructions of Checkpoints
Yu Chen · Zhenming LIU · Bin Ren · Xin Jin
On Leveraging Pretrained GANs for Generation with Limited Data
Miaoyun Zhao · Yulai Cong · Lawrence Carin
Poisson Learning: Graph Based Semi-Supervised Learning At Very Low Label Rates
Jeff Calder · Brendan Cook · Matthew Thorpe · Dejan Slepcev
Simultaneous Inference for Massive Data: Distributed Bootstrap
Yang Yu · Shih-Kang Chao · Guang Cheng
Spectral Frank-Wolfe Algorithm: Strict Complementarity and Linear Convergence
Lijun Ding · Yingjie Fei · Qiantong Xu · Chengrun Yang
Stochastic Hamiltonian Gradient Methods for Smooth Games
Nicolas Loizou · Hugo Berard · Alexia Jolicoeur-Martineau · Pascal Vincent · Simon Lacoste-Julien · Ioannis Mitliagkas
Time-aware Large Kernel Convolutions
Vasileios Lioutas · Yuhong Guo
ACFlow: Flow Models for Arbitrary Conditional Likelihoods
Yang Li · Shoaib Akbar · Junier Oliva
Adaptive Reward-Poisoning Attacks against Reinforcement Learning
Xuezhou Zhang · Yuzhe Ma · Adish Singla · Jerry Zhu
Adversarial Attacks on Copyright Detection Systems
Parsa Saadatpanah · Ali Shafahi · Tom Goldstein
Computational and Statistical Tradeoffs in Inferring Combinatorial Structures of Ising Model
Ying Jin · Zhaoran Wang · Junwei Lu
Data-Dependent Differentially Private Parameter Learning for Directed Graphical Models
Amrita Roy Chowdhury · Theodoros Rekatsinas · Somesh Jha
Deep Molecular Programming: A Natural Implementation of Binary-Weight ReLU Neural Networks
Marko Vasic · Cameron Chalk · Sarfraz Khurshid · David Soloveichik
DRWR: A Differentiable Renderer without Rendering for Unsupervised 3D Structure Learning from Silhouette Images
Zhizhong Han · Chao Chen · Yushen Liu · Matthias Zwicker
Efficient Continuous Pareto Exploration in Multi-Task Learning
Pingchuan Ma · Tao Du · Wojciech Matusik
Fractal Gaussian Networks: A sparse random graph model based on Gaussian Multiplicative Chaos
Subhroshekhar Ghosh · Krishna Balasubramanian · Xiaochuan Yang
Generalization via Derandomization
Jeffrey Negrea · Gintare Karolina Dziugaite · Daniel Roy
Goal-Aware Prediction: Learning to Model What Matters
Suraj Nair · Silvio Savarese · Chelsea Finn
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
Graph Optimal Transport for Cross-Domain Alignment
Liqun Chen · Zhe Gan · Yu Cheng · Linjie Li · Lawrence Carin · Jingjing Liu
Improving generalization by controlling label-noise information in neural network weights
Hrayr Harutyunyan · Kyle Reing · Greg Ver Steeg · Aram Galstyan
Learning Discrete Structured Representations by Adversarially Maximizing Mutual Information
Karl Stratos · Sam Wiseman
Momentum Improves Normalized SGD
Ashok Cutkosky · Harsh Mehta
More Data Can Expand The Generalization Gap Between Adversarially Robust and Standard Models
Lin Chen · Yifei Min · Mingrui Zhang · Amin Karbasi
No-Regret and Incentive-Compatible Online Learning
Rupert Freeman · David Pennock · Chara Podimata · Jennifer Wortman Vaughan
On hyperparameter tuning in general clustering problemsm
Xinjie Fan · Yuguang Yue · Purnamrita Sarkar · Y. X. Rachel Wang
Online Learned Continual Compression with Adaptive Quantization Modules
Lucas Caccia · Eugene Belilovsky · Massimo Caccia · Joelle Pineau
Online Learning with Imperfect Hints
Aditya Bhaskara · Ashok Cutkosky · Ravi Kumar · Manish Purohit
Provable Smoothness Guarantees for Black-Box Variational Inference
Justin Domke
Recht-Re Noncommutative Arithmetic-Geometric Mean Conjecture is False
Zehua Lai · Lek-Heng Lim
Reinforcement Learning in Feature Space: Matrix Bandit, Kernels, and Regret Bound
Lin Yang · Mengdi Wang
The Intrinsic Robustness of Stochastic Bandits to Strategic Manipulation
Zhe Feng · David Parkes · Haifeng Xu
Uniform Convergence of Rank-weighted Learning
Justin Khim · Liu Leqi · Adarsh Prasad · Pradeep Ravikumar
Variance Reduction and Quasi-Newton for Particle-Based Variational Inference
Michael Zhu · Chang Liu · Jun Zhu
Few-shot Domain Adaptation by Causal Mechanism Transfer
Takeshi Teshima · Issei Sato · Masashi Sugiyama
Learning Efficient Multi-agent Communication: An Information Bottleneck Approach
Rundong Wang · Xu He · Runsheng Yu · Wei Qiu · Bo An · Zinovi Rabinovich
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 · Tie-Yan Liu
Self-PU: Self Boosted and Calibrated Positive-Unlabeled Training
Xuxi Chen · Wuyang Chen · Tianlong Chen · Ye Yuan · Chen Gong · Kewei Chen · Zhangyang “Atlas” Wang
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Poster
4:00 AM - 4:45 AM
51 Events in this session
Deep Graph Random Process for Relational-Thinking-Based Speech Recognition
Huang Hengguan · Fuzhao Xue · Hao Wang · Ye Wang
Reducing Sampling Error in Batch Temporal Difference Learning
Brahma Pavse · Ishan Durugkar · Josiah Hanna · Peter Stone
Towards Accurate Post-training Network Quantization via Bit-Split and Stitching
Peisong Wang · Qiang Chen · Xiangyu He · Jian Cheng
Universal Asymptotic Optimality of Polyak Momentum
Damien Scieur · Fabian Pedregosa
Do RNN and LSTM have Long Memory?
Jingyu Zhao · Feiqing Huang · Jia Lv · Yanjie Duan · Zhen Qin · Guodong Li · Guangjian Tian
Improved Sleeping Bandits with Stochastic Action Sets and Adversarial Rewards
Aadirupa Saha · Pierre Gaillard · Michal Valko
Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to-Event Data
Tamara Fernandez · Arthur Gretton · Nicolas Rivera · Wenkai Xu
Learning Optimal Tree Models under Beam Search
Jingwei Zhuo · Ziru Xu · Wei Dai · Han Zhu · HAN LI · Jian Xu · Kun Gai
Lower Complexity Bounds for Finite-Sum Convex-Concave Minimax Optimization Problems
Guangzeng Xie · Luo Luo · yijiang lian · Zhihua Zhang
On Breaking Deep Generative Model-based Defenses and Beyond
Yanzhi Chen · Renjie Xie · Zhanxing Zhu
Performative Prediction
Juan Perdomo · Tijana Zrnic · Celestine Mendler-Dünner · Moritz Hardt
Provably Efficient Model-based Policy Adaptation
Yuda Song · Aditi Mavalankar · Wen Sun · Sicun Gao
Tails of Lipschitz Triangular Flows
Priyank Jaini · Ivan Kobyzev · Yaoliang Yu · Marcus Brubaker
T-GD: Transferable GAN-generated Images Detection Framework
Hyeonseong Jeon · Young Oh Bang · Junyaup Kim · Simon Woo
Towards Understanding the Regularization of Adversarial Robustness on Neural Networks
Yuxin Wen · Shuai Li · Kui Jia
A Nearly-Linear Time Algorithm for Exact Community Recovery in Stochastic Block Model
Peng Wang · Zirui Zhou · Anthony Man-Cho So
Learning Task-Agnostic Embedding of Multiple Black-Box Experts for Multi-Task Model Fusion
Nghia Hoang · Thanh Lam · Bryan Kian Hsiang Low · Patrick Jaillet
Model-Based Reinforcement Learning with Value-Targeted Regression
Alex Ayoub · Zeyu Jia · Csaba Szepesvari · Mengdi Wang · Lin Yang
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
Distribution Augmentation for Generative Modeling
Heewoo Jun · Rewon Child · Mark Chen · John Schulman · Aditya Ramesh · Alec Radford · Ilya Sutskever
On Differentially Private Stochastic Convex Optimization with Heavy-tailed Data
Di Wang · Hanshen Xiao · Srinivas Devadas · Jinhui Xu
An Imitation Learning Approach for Cache Replacement
Evan Liu · Milad Hashemi · Kevin Swersky · Parthasarathy Ranganathan · Junwhan Ahn
Beyond Synthetic Noise: Deep Learning on Controlled Noisy Labels
Lu Jiang · Di Huang · Mason Liu · Weilong Yang
Coresets for Data-efficient Training of Machine Learning Models
Baharan Mirzasoleiman · Jeff Bilmes · Jure Leskovec
Minimax Pareto Fairness: A Multi Objective Perspective
Natalia Martinez Gil · Martin Bertran · Guillermo Sapiro
Non-convex Learning via Replica Exchange Stochastic Gradient MCMC
Wei Deng · Qi Feng · Liyao Gao · Faming Liang · Guang Lin
Principled learning method for Wasserstein distributionally robust optimization with local perturbations
Yongchan Kwon · Wonyoung Kim · Joong-Ho (Johann) Won · Myunghee Cho Paik
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
Visual Grounding of Learned Physical Models
Yunzhu Li · Toru Lin · Kexin Yi · Daniel Bear · Daniel Yamins · Jiajun Wu · Josh Tenenbaum · Antonio Torralba
AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation
Jae Hyun Lim · Aaron Courville · Christopher Pal · Chin-Wei Huang
Certified Data Removal from Machine Learning Models
Chuan Guo · Tom Goldstein · Awni Hannun · Laurens van der Maaten
Collapsed Amortized Variational Inference for Switching Nonlinear Dynamical Systems
Zhe Dong · Bryan Seybold · Kevin Murphy · Hung Bui
Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks
Zhishuai Guo · Mingrui Liu · Zhuoning Yuan · Li Shen · Wei Liu · Tianbao Yang
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
Defense Through Diverse Directions
Christopher Bender · Yang Li · Yifeng Shi · Michael K. Reiter · Junier Oliva
Domain Aggregation Networks for Multi-Source Domain Adaptation
Junfeng Wen · Russell Greiner · Dale Schuurmans
Emergence of Separable Manifolds in Deep Language Representations
Jonathan Mamou · Hang Le · Miguel A del Rio Fernandez · Cory Stephenson · Hanlin Tang · Yoon Kim · SueYeon Chung
Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs
Meng Qu · Tianyu Gao · Louis-Pascal Xhonneux · Jian Tang
Generalization and Representational Limits of Graph Neural Networks
Vikas K Garg · Stefanie Jegelka · Tommi Jaakkola
Generalized and Scalable Optimal Sparse Decision Trees
Jimmy Lin · Chudi Zhong · Diane Hu · Cynthia Rudin · Margo Seltzer
Gradient Temporal-Difference Learning with Regularized Corrections
Sina Ghiassian · Andrew Patterson · Shivam Garg · Dhawal Gupta · Adam White · Martha White
High-dimensional Robust Mean Estimation via Gradient Descent
Yu Cheng · Ilias Diakonikolas · Rong Ge · Mahdi Soltanolkotabi
Learning Human Objectives by Evaluating Hypothetical Behavior
Siddharth Reddy · Anca Dragan · Sergey Levine · Shane Legg · Jan Leike
Learning Mixtures of Graphs from Epidemic Cascades
Jessica Hoffmann · Soumya Basu · Surbhi Goel · Constantine Caramanis
Momentum-Based Policy Gradient Methods
Feihu Huang · Shangqian Gao · Jian Pei · Heng Huang
One-shot Distributed Ridge Regression in High Dimensions
Yue Sheng · Edgar Dobriban
Optimizing Data Usage via Differentiable Rewards
Xinyi Wang · Hieu Pham · Paul Michel · Antonios Anastasopoulos · Jaime Carbonell · Graham Neubig
Progressive Identification of True Labels for Partial-Label Learning
Jiaqi Lv · Miao Xu · LEI FENG · Gang Niu · Xin Geng · Masashi Sugiyama
SGD Learns One-Layer Networks in WGANs
Qi Lei · Jason Lee · Alexandros Dimakis · Constantinos Daskalakis
Sparse Sinkhorn Attention
Yi Tay · Dara Bahri · Liu Yang · Don Metzler · Da-Cheng Juan
The Performance Analysis of Generalized Margin Maximizers on Separable Data
Fariborz Salehi · Ehsan Abbasi · Babak Hassibi
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Poster
5:00 AM - 5:45 AM
39 Events in this session
Better depth-width trade-offs for neural networks through the lens of dynamical systems
Evangelos Chatziafratis · Sai Ganesh Nagarajan · Ioannis Panageas
Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning
Lingxiao Wang · Zhuoran Yang · Zhaoran Wang
Converging to Team-Maxmin Equilibria in Zero-Sum Multiplayer Games
Youzhi Zhang · Bo An
Doubly robust off-policy evaluation with shrinkage
Yi Su · Maria Dimakopoulou · Akshay Krishnamurthy · Miroslav Dudik
Goodness-of-Fit Tests for Inhomogeneous Random Graphs
Soham Dan · Bhaswar B. Bhattacharya
Graph Structure of Neural Networks
Jiaxuan You · Jure Leskovec · Kaiming He · Saining Xie
Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning
Dipendra Kumar Misra · Mikael Henaff · Akshay Krishnamurthy · John Langford
Lorentz Group Equivariant Neural Network for Particle Physics
Alexander Bogatskiy · Brandon Anderson · Jan T Offermann · Marwah Roussi · David Miller · Risi Kondor
Normalized Loss Functions for Deep Learning with Noisy Labels
Xingjun Ma · Hanxun Huang · Yisen Wang · Simone Romano · Sarah Erfani · James Bailey
Optimal Differential Privacy Composition for Exponential Mechanisms
Jinshuo Dong · David Durfee · Ryan Rogers
Perceptual Generative Autoencoders
Zijun Zhang · Ruixiang ZHANG · Zongpeng Li · Yoshua Bengio · Liam Paull
Semi-Supervised Learning with Normalizing Flows
Pavel Izmailov · Polina Kirichenko · Marc Finzi · Andrew Wilson
Semi-Supervised StyleGAN for Disentanglement Learning
Weili Nie · Tero Karras · Animesh Garg · Shoubhik Debnath · Anjul Patney · Ankit Patel · Anima Anandkumar
Understanding and Stabilizing GANs' Training Dynamics Using Control Theory
Kun Xu · Chongxuan Li · Jun Zhu · Bo Zhang
Variational Inference for Sequential Data with Future Likelihood Estimates
Geon-Hyeong Kim · Youngsoo Jang · Hongseok Yang · Kee-Eung Kim
When Demands Evolve Larger and Noisier: Learning and Earning in a Growing Environment
Feng Zhu · Zeyu Zheng
One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control
Wenlong Huang · Igor Mordatch · Deepak Pathak
Provably Convergent Two-Timescale Off-Policy Actor-Critic with Function Approximation
Shangtong Zhang · Bo Liu · Hengshuai Yao · Shimon Whiteson
Model Fusion with Kullback--Leibler Divergence
Sebastian Claici · Mikhail Yurochkin · Soumya Ghosh · Justin Solomon
Parameter-free, Dynamic, and Strongly-Adaptive Online Learning
Ashok Cutkosky
Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions
Michael Chang · Sid Kaushik · S. Matthew Weinberg · Thomas Griffiths · Sergey Levine
On the Global Convergence Rates of Softmax Policy Gradient Methods
Jincheng Mei · Chenjun Xiao · Csaba Szepesvari · Dale Schuurmans
ROMA: Multi-Agent Reinforcement Learning with Emergent Roles
Tonghan Wang · Heng Dong · Victor Lesser · Chongjie Zhang
The Implicit Regularization of Stochastic Gradient Flow for Least Squares
Alnur Ali · Edgar Dobriban · Ryan Tibshirani
Accelerated Message Passing for Entropy-Regularized MAP Inference
Jonathan Lee · Aldo Pacchiano · Peter Bartlett · Michael Jordan
Aligned Cross Entropy for Non-Autoregressive Machine Translation
Marjan Ghazvininejad · Vladimir Karpukhin · Luke Zettlemoyer · Omer Levy
Bio-Inspired Hashing for Unsupervised Similarity Search
Chaitanya Ryali · John Hopfield · Leopold Grinberg · Dmitry Krotov
Concept Bottleneck Models
Pang Wei Koh · Thao Nguyen · Yew Siang Tang · Stephen Mussmann · Emma Pierson · Been Kim · Percy Liang
CURL: Contrastive Unsupervised Representations for Reinforcement Learning
Michael Laskin · Aravind Srinivas · Pieter Abbeel
Decision Trees for Decision-Making under the Predict-then-Optimize Framework
Adam Elmachtoub · Jason Cheuk Nam Liang · Ryan McNellis
Evaluating Machine Accuracy on ImageNet
Vaishaal Shankar · Rebecca Roelofs · Horia Mania · Alex Fang · Benjamin Recht · Ludwig Schmidt
Generalization Error of Generalized Linear Models in High Dimensions
Melikasadat Emami · Mojtaba Sahraee-Ardakan · Parthe Pandit · Sundeep Rangan · Alyson Fletcher
Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection
Mao Ye · Chengyue Gong · Lizhen Nie · Denny Zhou · Adam Klivans · Qiang Liu
Implicit competitive regularization in GANs
Florian Schäfer · Hongkai Zheng · Anima Anandkumar
Improving the Sample and Communication Complexity for Decentralized Non-Convex Optimization: Joint Gradient Estimation and Tracking
Haoran Sun · Songtao Lu · Mingyi Hong
Inferring DQN structure for high-dimensional continuous control
Andrey Sakryukin · Chedy Raissi · Mohan Kankanhalli
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
Video Prediction via Example Guidance
Jingwei Xu · Harry (Huazhe) Xu · Bingbing Ni · Xiaokang Yang · Trevor Darrell
When Does Self-Supervision Help Graph Convolutional Networks?
Yuning You · Tianlong Chen · Zhangyang “Atlas” Wang · Yang Shen
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Poster
8:00 AM - 8:45 AM
22 Events in this session
Bounding the fairness and accuracy of classifiers from population statistics
Sivan Sabato · Elad Yom-Tov
ECLIPSE: An Extreme-Scale Linear Program Solver for Web-Applications
Kinjal Basu · Amol Ghoting · Rahul Mazumder · Yao Pan
Energy-Based Processes for Exchangeable Data
Mengjiao Yang · Bo Dai · Hanjun Dai · Dale Schuurmans
Multiclass Neural Network Minimization via Tropical Newton Polytope Approximation
Georgios Smyrnis · Petros Maragos
Active World Model Learning in Agent-rich Environments with Progress Curiosity
Kuno Kim · Megumi Sano · Julian De Freitas · Nick Haber · Daniel Yamins
A Distributional Framework For Data Valuation
Amirata Ghorbani · Michael Kim · James Zou
Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation
Jian Liang · Dapeng Hu · Jiashi Feng
From PAC to Instance-Optimal Sample Complexity in the Plackett-Luce Model
Aadirupa Saha · Aditya Gopalan
Improved Optimistic Algorithms for Logistic Bandits
Louis Faury · Marc Abeille · Clément Calauzènes · Olivier Fercoq
WaveFlow: A Compact Flow-based Model for Raw Audio
Wei Ping · Kainan Peng · Kexin Zhao · Zhao Song
Inter-domain Deep Gaussian Processes
Tim G. J. Rudner · Dino Sejdinovic · Yarin Gal
Landscape Connectivity and Dropout Stability of SGD Solutions for Over-parameterized Neural Networks
Alexander Shevchenko · Marco Mondelli
Learning Flat Latent Manifolds with VAEs
Nutan Chen · Alexej Klushyn · Francesco Ferroni · Justin Bayer · Patrick van der Smagt
Missing Data Imputation using Optimal Transport
Boris Muzellec · Julie Josse · Claire Boyer · Marco Cuturi
Optimizer Benchmarking Needs to Account for Hyperparameter Tuning
Prabhu Teja Sivaprasad · Florian Mai · Thijs Vogels · Martin Jaggi · François Fleuret
A Flexible Latent Space Model for Multilayer Networks
Xuefei Zhang · Songkai Xue · Ji Zhu
Composable Sketches for Functions of Frequencies: Beyond the Worst Case
Edith Cohen · Ofir Geri · Rasmus Pagh
Naive Exploration is Optimal for Online LQR
Max Simchowitz · Dylan Foster
Online Continual Learning from Imbalanced Data
Aristotelis Chrysakis · Marie-Francine Moens
Online Multi-Kernel Learning with Graph-Structured Feedback
Pouya M Ghari · Yanning Shen
On Thompson Sampling with Langevin Algorithms
Eric Mazumdar · Aldo Pacchiano · Yian Ma · Michael Jordan · Peter Bartlett
Topologically Densified Distributions
Christoph Hofer · Florian Graf · Marc Niethammer · Roland Kwitt
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Poster
9:00 AM - 9:45 AM
18 Events in this session
Symbolic Network: Generalized Neural Policies for Relational MDPs
Sankalp Garg · Aniket Bajpai · Mausam
Unsupervised Discovery of Interpretable Directions in the GAN Latent Space
Andrey Voynov · Artem Babenko
Information Particle Filter Tree: An Online Algorithm for POMDPs with Belief-Based Rewards on Continuous Domains
Johannes Fischer · Ömer Sahin Tas
Soft Threshold Weight Reparameterization for Learnable Sparsity
Aditya Kusupati · Vivek Ramanujan · Raghav Somani · Mitchell Wortsman · Prateek Jain · Sham Kakade · Ali Farhadi
Double-Loop Unadjusted Langevin Algorithm
Paul Rolland · Armin Eftekhari · Ali Kavis · Volkan Cevher
Gradient-free Online Learning in Continuous Games with Delayed Rewards
Amélie Héliou · Panayotis Mertikopoulos · Zhengyuan Zhou
Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules
Sarthak Mittal · Alex Lamb · Anirudh Goyal · Vikram Voleti · Murray Shanahan · Guillaume Lajoie · Michael Mozer · Yoshua Bengio
Low Bias Low Variance Gradient Estimates for Hierarchical Boolean Stochastic Networks
Adeel Pervez · Taco Cohen · Efstratios Gavves
Neural Kernels Without Tangents
Vaishaal Shankar · Alex Fang · Wenshuo Guo · Sara Fridovich-Keil · Jonathan Ragan-Kelley · Ludwig Schmidt · Benjamin Recht
PowerNorm: Rethinking Batch Normalization in Transformers
Sheng Shen · Zhewei Yao · Amir Gholaminejad · Michael Mahoney · Kurt Keutzer
Random extrapolation for primal-dual coordinate descent
Ahmet Alacaoglu · Olivier Fercoq · Volkan Cevher
Randomized Block-Diagonal Preconditioning for Parallel Learning
Celestine Mendler-Dünner · Aurelien Lucchi
Real-Time Optimisation for Online Learning in Auctions
Lorenzo Croissant · Marc Abeille · Clément Calauzènes
Efficient Proximal Mapping of the 1-path-norm of Shallow Networks
Fabian Latorre · Paul Rolland · Shaul Nadav Hallak · Volkan Cevher
Learning to Branch for Multi-Task Learning
Pengsheng Guo · Chen-Yu Lee · Daniel Ulbricht
Robust Learning with the Hilbert-Schmidt Independence Criterion
Daniel Greenfeld · Uri Shalit
The Boomerang Sampler
Joris Bierkens · Sebastiano Grazzi · Kengo Kamatani · Gareth Roberts
Topic Modeling via Full Dependence Mixtures
Dan Fisher · Mark Kozdoba · Shie Mannor
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Poster
10:00 AM - 10:45 AM
32 Events in this session
Graph-based Nearest Neighbor Search: From Practice to Theory
Liudmila Prokhorenkova · Aleksandr Shekhovtsov
Aggregation of Multiple Knockoffs
Tuan-Binh Nguyen · Jerome-Alexis Chevalier · Thirion Bertrand · Sylvain Arlot
Bisection-Based Pricing for Repeated Contextual Auctions against Strategic Buyer
Anton Zhiyanov · Alexey Drutsa
Conditional gradient methods for stochastically constrained convex minimization
Maria-Luiza Vladarean · Ahmet Alacaoglu · Ya-Ping Hsieh · Volkan Cevher
Data-Efficient Image Recognition with Contrastive Predictive Coding
Olivier Henaff
Discriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions
Ahmed Alaa · Mihaela van der Schaar
Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More
Aleksandar Bojchevski · Johannes Gasteiger · Stephan Günnemann
Extra-gradient with player sampling for faster convergence in n-player games
Samy Jelassi · Carles Domingo-Enrich · Damien Scieur · Arthur Mensch · Joan Bruna
Frequency Bias in Neural Networks for Input of Non-Uniform Density
Ronen Basri · Meirav Galun · Amnon Geifman · David Jacobs · Yoni Kasten · Shira Kritchman
Learning to Rank Learning Curves
Martin Wistuba · Tejaswini Pedapati
Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack
Francesco Croce · Matthias Hein
Monte-Carlo Tree Search as Regularized Policy Optimization
Jean-Bastien Grill · Florent Altché · Yunhao Tang · Thomas Hubert · Michal Valko · Ioannis Antonoglou · Remi Munos
Near-optimal Regret Bounds for Stochastic Shortest Path
Aviv Rosenberg · Alon Cohen · Yishay Mansour · Haim Kaplan
Optimistic Bounds for Multi-output Learning
Henry Reeve · Ata Kaban
PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
Jingqing Zhang · Yao Zhao · Mohammad Saleh · Peter Liu
Predictive Coding for Locally-Linear Control
Rui Shu · Tung Nguyen · Yinlam Chow · Tuan Pham · Khoat Than · Mohammad Ghavamzadeh · Stefano Ermon · Hung Bui
Revisiting Training Strategies and Generalization Performance in Deep Metric Learning
Karsten Roth · Timo Milbich · Samrath Sinha · Prateek Gupta · Bjorn Ommer · Joseph Paul Cohen
Supervised Quantile Normalization for Low Rank Matrix Factorization
Marco Cuturi · Olivier Teboul · Jonathan Niles-Weed · Jean-Philippe Vert
The Complexity of Finding Stationary Points with Stochastic Gradient Descent
Yoel Drori · Ohad Shamir
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks
Jakub Swiatkowski · Kevin Roth · Bastiaan Veeling · Linh Tran · Joshua V Dillon · Jasper Snoek · Stephan Mandt · Tim Salimans · Rodolphe Jenatton · Sebastian Nowozin
Stochastic Latent Residual Video Prediction
Jean-Yves Franceschi · Edouard Delasalles · Mickael Chen · Sylvain Lamprier · Patrick Gallinari
A quantile-based approach for hyperparameter transfer learning
David Salinas · Huibin Shen · Valerio Perrone
Graph Filtration Learning
Christoph Hofer · Florian Graf · Bastian Rieck · Marc Niethammer · Roland Kwitt
Neural Topic Modeling with Continual Lifelong Learning
Pankaj Gupta · Yatin Chaudhary · Thomas Runkler · Hinrich Schuetze
Predicting Choice with Set-Dependent Aggregation
Nir Rosenfeld · Kojin Oshiba · Yaron Singer
Spectral Clustering with Graph Neural Networks for Graph Pooling
Filippo Maria Bianchi · Daniele Grattarola · Cesare Alippi
Stochastic Differential Equations with Variational Wishart Diffusions
Martin Jørgensen · Marc Deisenroth · Hugh Salimbeni
Stochastic Subspace Cubic Newton Method
Filip Hanzely · Nikita Doikov · Yurii Nesterov · Peter Richtarik
Anderson Acceleration of Proximal Gradient Methods
Vien Mai · Mikael Johansson
Extrapolation for Large-batch Training in Deep Learning
Tao Lin · Lingjing Kong · Sebastian Stich · Martin Jaggi
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
Scalable Gaussian Process Separation for Kernels with a Non-Stationary Phase
Jan Graßhoff · Alexandra Jankowski · Philipp Rostalski
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Poster
11:00 AM - 11:45 AM
18 Events in this session
Amortised Learning by Wake-Sleep
Li Kevin Wenliang · Theodore Moskovitz · Heishiro Kanagawa · Maneesh Sahani
Convolutional Kernel Networks for Graph-Structured Data
Dexiong Chen · Laurent Jacob · Julien Mairal
Latent Bernoulli Autoencoder
Jiri Fajtl · Vasileios Argyriou · Dorothy Monekosso · Paolo Remagnino
On Efficient Low Distortion Ultrametric Embedding
Vincent Cohen-Addad · Karthik C. S. · Guillaume Lagarde
Preselection Bandits
Viktor Bengs · Eyke Hüllermeier
A Generative Model for Molecular Distance Geometry
Gregor Simm · Jose Miguel Hernandez-Lobato
Influenza Forecasting Framework based on Gaussian Processes
Christoph Zimmer · Reza Yaesoubi
Kernel interpolation with continuous volume sampling
Ayoub Belhadji · Rémi Bardenet · Pierre Chainais
Modulating Surrogates for Bayesian Optimization
Erik Bodin · Markus Kaiser · Ieva Kazlauskaite · Zhenwen Dai · Neill Campbell · Carl Henrik Ek
Projective Preferential Bayesian Optimization
Petrus Mikkola · Milica Todorović · Jari Järvi · Patrick Rinke · Samuel Kaski
Teaching with Limited Information on the Learner's Behaviour
Ferdinando Cicalese · Francisco Sergio de Freitas Filho · Eduardo Laber · Marco Molinaro
The FAST Algorithm for Submodular Maximization
Adam Breuer · Eric Balkanski · Yaron Singer
Towards Adaptive Residual Network Training: A Neural-ODE Perspective
Chengyu Dong · Liyuan Liu · Zichao Li · Jingbo Shang
Towards non-parametric drift detection via Dynamic Adapting Window Independence Drift Detection (DAWIDD)
Fabian Hinder · André Artelt · CITEC Barbara Hammer
Training Binary Neural Networks using the Bayesian Learning Rule
Xiangming Meng · Roman Bachmann · Mohammad Emtiyaz Khan
Optimistic Policy Optimization with Bandit Feedback
Lior Shani · Yonathan Efroni · Aviv Rosenberg · Shie Mannor
Stochastic Optimization for Regularized Wasserstein Estimators
Marin Ballu · Quentin Berthet · Francis Bach
Reliable Fidelity and Diversity Metrics for Generative Models
Muhammad Ferjad Naeem · Seong Joon Oh · Yunjey Choi · Youngjung Uh · Jaejun Yoo
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Workshop
11:00 AM - 5:00 AM
17 Events in this session
Self-supervision in Audio and Speech
Mirco Ravanelli · Dmitriy Serdyuk · R Devon Hjelm · Bhuvana Ramabhadran · Titouan Parcollet
5th ICML Workshop on Human Interpretability in Machine Learning (WHI)
Adrian Weller · Alice Xiang · Amit Dhurandhar · Been Kim · Dennis Wei · Kush Varshney · Umang Bhatt
Law & Machine Learning
Céline Castets-Renard · Sylvain Cussat-Blanc · Laurent Risser
Learning with Missing Values
Julie Josse · Jes Frellsen · Pierre-Alexandre Mattei · Gael Varoquaux
Healthcare Systems, Population Health, and the Role of Health-tech
Creighton Heaukulani · Konstantina Palla · Katherine Heller · Niranjani Prasad · Marzyeh Ghassemi
Challenges in Deploying and Monitoring Machine Learning Systems
Alessandra Tosi · Nathan Korda · Neil Lawrence
Workshop on AI for Autonomous Driving (AIAD)
Wei-Lun (Harry) Chao · Rowan McAllister · Adrien Gaidon · Li Erran Li · Sven Kreiss
MLRetrospectives: A Venue for Self-Reflection in ML Research
Jessica Forde · Jesse Dodge · Mayoore Jaiswal · Rosanne Liu · Ryan Lowe · Rosanne Liu · Joelle Pineau · Yoshua Bengio
ICML 2020 Workshop on Computational Biology
Workshop CompBio · Delasa Aghamirzaie · Alexander Anderson · Elham Azizi · Abdoulaye Baniré Diallo · Cassandra Burdziak · Jill Gallaher · Anshul Kundaje · Dana Pe'er · Sandhya Prabhakaran · Amine Remita · Mark Robertson-Tessi · Wesley Tansey · Julia Vogt · Yubin Xie
Participatory Approaches to Machine Learning
Angela Zhou · David Madras · Deborah Raji · Smitha Milli · Bogdan Kulynych · Richard Zemel
Workshop on Continual Learning
Haytham Fayek · Arslan Chaudhry · David Lopez-Paz · Eugene Belilovsky · Jonathan Schwarz · Marc Pickett · Rahaf Aljundi · Sayna Ebrahimi · Razvan Pascanu · Puneet Dokania
Workshop on eXtreme Classification: Theory and Applications
Anna Choromanska · John Langford · Maryam Majzoubi · Yashoteja Prabhu
Object-Oriented Learning: Perception, Representation, and Reasoning
Sungjin Ahn · Adam Kosiorek · Jessica Hamrick · Sjoerd van Steenkiste · Yoshua Bengio
Theoretical Foundations of Reinforcement Learning
Emma Brunskill · Thodoris Lykouris · Max Simchowitz · Wen Sun · Mengdi Wang
ML Interpretability for Scientific Discovery
Subhashini Venugopalan · Michael Brenner · Scott Linderman · Been Kim
Uncertainty and Robustness in Deep Learning Workshop (UDL)
Sharon Yixuan Li · Balaji Lakshminarayanan · Dan Hendrycks · Thomas Dietterich · Jasper Snoek
Beyond first order methods in machine learning systems
Albert S Berahas · Amir Gholaminejad · Anastasios Kyrillidis · Michael Mahoney · Fred Roosta
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Poster
12:00 PM - 12:45 PM
13 Events in this session
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 · Feilx Hill
The continuous categorical: a novel simplex-valued exponential family
Elliott Gordon-Rodriguez · Gabriel Loaiza-Ganem · John Cunningham
Efficiently sampling functions from Gaussian process posteriors
James Wilson · Viacheslav Borovitskiy · Alexander Terenin · Peter Mostowsky · Marc Deisenroth
Forecasting Sequential Data Using Consistent Koopman Autoencoders
Omri Azencot · N. Benjamin Erichson · Vanessa Lin · Michael Mahoney
From Sets to Multisets: Provable Variational Inference for Probabilistic Integer Submodular Models
Aytunc Sahin · Yatao Bian · Joachim Buhmann · Andreas Krause
Linear bandits with Stochastic Delayed Feedback
Claire Vernade · Alexandra Carpentier · Tor Lattimore · Giovanni Zappella · Beyza Ermis · Michael Brueckner
Multi-Agent Determinantal Q-Learning
Yaodong Yang · Ying Wen · Jun Wang · Liheng Chen · Kun Shao · David Mguni · Weinan Zhang
Parallel Algorithm for Non-Monotone DR-Submodular Maximization
Alina Ene · Huy Nguyen
Regularized Optimal Transport is Ground Cost Adversarial
François-Pierre Paty · Marco Cuturi
Reinforcement Learning for Molecular Design Guided by Quantum Mechanics
Gregor Simm · Robert Pinsler · Jose Miguel Hernandez-Lobato
Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift
Alexander Chan · Ahmed Alaa · Zhaozhi Qian · Mihaela van der Schaar
Variational Autoencoders with Riemannian Brownian Motion Priors
Dimitris Kalatzis · David Eklund · Georgios Arvanitidis · Søren Hauberg
Spectral Subsampling MCMC for Stationary Time Series
Robert Salomone · Matias Quiroz · Robert kohn · Mattias Villani · Minh-Ngoc Tran
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Poster
1:00 PM - 1:45 PM
32 Events in this session
Deep Streaming Label Learning
Zhen Wang · Liu Liu · Dacheng Tao
Curse of Dimensionality on Randomized Smoothing for Certifiable Robustness
Aounon Kumar · Alexander Levine · Tom Goldstein · Soheil Feizi
Learning Factorized Weight Matrix for Joint Filtering
Xiangyu Xu · Yongrui Ma · Wenxiu Sun
Neural Network Control Policy Verification With Persistent Adversarial Perturbation
Yuh-Shyang Wang · Tsui-Wei Weng · Luca Daniel
Off-Policy Actor-Critic with Shared Experience Replay
Simon Schmitt · Matteo Hessel · Karen Simonyan
Randomization matters How to defend against strong adversarial attacks
Rafael Pinot · Raphael Ettedgui · Geovani Rizk · Yann Chevaleyre · Jamal Atif
Projection-free Distributed Online Convex Optimization with $O(\sqrt{T})$ Communication Complexity
Yuanyu Wan · Wei-Wei Tu · Lijun Zhang
Representation Learning via Adversarially-Contrastive Optimal Transport
Anoop Cherian · Shuchin Aeron
A Graph to Graphs Framework for Retrosynthesis Prediction
Chence Shi · Minkai Xu · Hongyu Guo · Ming Zhang · Jian Tang
A Tree-Structured Decoder for Image-to-Markup Generation
Jianshu Zhang · Jun Du · Yongxin Yang · Yi-Zhe Song · Si Wei · Lirong Dai
Cost-effectively Identifying Causal Effects When Only Response Variable is Observable
Tian-Zuo Wang · Xi-Zhu Wu · Sheng-Jun Huang · Zhi-Hua Zhou
Does the Markov Decision Process Fit the Data: Testing for the Markov Property in Sequential Decision Making
Chengchun Shi · Runzhe Wan · Rui Song · Wenbin Lu · Ling Leng
Do We Need Zero Training Loss After Achieving Zero Training Error?
Takashi Ishida · Ikko Yamane · Tomoya Sakai · Gang Niu · Masashi Sugiyama
DropNet: Reducing Neural Network Complexity via Iterative Pruning
Chong Min John Tan · Mehul Motani
Dual-Path Distillation: A Unified Framework to Improve Black-Box Attacks
Yonggang Zhang · Ya Li · Tongliang Liu · Xinmei Tian
Learning Autoencoders with Relational Regularization
Hongteng Xu · Dixin Luo · Ricardo Henao · Svati Shah · Lawrence Carin
Minimax Weight and Q-Function Learning for Off-Policy Evaluation
Masatoshi Uehara · Jiawei Huang · Nan Jiang
More Information Supervised Probabilistic Deep Face Embedding Learning
Ying Huang · Shangfeng Qiu · Wenwei Zhang · Xianghui Luo · Jinzhuo Wang
Multinomial Logit Bandit with Low Switching Cost
Kefan Dong · Yingkai Li · Qin Zhang · Yuan Zhou
Multi-objective Bayesian Optimization using Pareto-frontier Entropy
Shinya Suzuki · Shion Takeno · Tomoyuki Tamura · Kazuki Shitara · Masayuki Karasuyama
Non-autoregressive Machine Translation with Disentangled Context Transformer
Jungo Kasai · James Cross · Marjan Ghazvininejad · Jiatao Gu
On the (In)tractability of Computing Normalizing Constants for the Product of Determinantal Point Processes
Naoto Ohsaka · Tatsuya Matsuoka
On the Power of Compressed Sensing with Generative Models
Akshay Kamath · Eric Price · Sushrut Karmalkar
Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup
Jang-Hyun Kim · Wonho Choo · Hyun Oh Song
Sample Complexity Bounds for 1-bit Compressive Sensing and Binary Stable Embeddings with Generative Priors
Zhaoqiang Liu · Selwyn Gomes · Avtansh Tiwari · Jonathan Scarlett
Self-Attentive Associative Memory
Hung Le · Truyen Tran · Svetha Venkatesh
Sparse Subspace Clustering with Entropy-Norm
Liang Bai · Jiye Liang
Transformer Hawkes Process
Simiao Zuo · Haoming Jiang · Zichong Li · Tuo Zhao · Hongyuan Zha
Few-shot Domain Adaptation by Causal Mechanism Transfer
Takeshi Teshima · Issei Sato · Masashi Sugiyama
Learning Efficient Multi-agent Communication: An Information Bottleneck Approach
Rundong Wang · Xu He · Runsheng Yu · Wei Qiu · Bo An · Zinovi Rabinovich
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 · Tie-Yan Liu
Self-PU: Self Boosted and Calibrated Positive-Unlabeled Training
Xuxi Chen · Wuyang Chen · Tianlong Chen · Ye Yuan · Chen Gong · Kewei Chen · Zhangyang “Atlas” Wang
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