ICML 2021

1,183 papers

Beyond Variance Reduction: Understanding the True Impact of Baselines on Policy Optimization

Wesley Chung, Valentin Thomas, Marlos C. Machado, Nicolas Le Roux

Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing

Filippos Christianos, Georgios Papoudakis, Muhammad A Rahman, Stefano V Albrecht

Modeling Hierarchical Structures with Continuous Recursive Neural Networks

Jishnu Ray Chowdhury, Cornelia Caragea

Label-Only Membership Inference Attacks

Christopher A. Choquette-Choo, Florian Tramer, Nicholas Carlini, Nicolas Papernot

Variational Empowerment as Representation Learning for Goal-Conditioned Reinforcement Learning

Jongwook Choi, Archit Sharma, Honglak Lee, Sergey Levine, Shixiang Shane Gu

Learning from Nested Data with Ornstein Auto-Encoders

Youngwon Choi, Sungdong Lee, Joong-Ho Won

Unifying Vision-and-Language Tasks via Text Generation

Jaemin Cho, Jie Lei, Hao Tan, Mohit Bansal

Robust Learning-Augmented Caching: An Experimental Study

Jakub Chłędowski, Adam Polak, Bartosz Szabucki, Konrad Tomasz Żołna

Quantifying and Reducing Bias in Maximum Likelihood Estimation of Structured Anomalies

Uthsav Chitra, Kimberly Ding, Jasper C.H. Lee, Benjamin J Raphael

Parallelizing Legendre Memory Unit Training

Narsimha Reddy Chilkuri, Chris Eliasmith

Light RUMs

Flavio Chierichetti, Ravi Kumar, Andrew Tomkins

Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates

Steve Chien, Prateek Jain, Walid Krichene, Steffen Rendle, Shuang Song

Understanding and Mitigating Accuracy Disparity in Regression

Jianfeng Chi, Yuan Tian, Geoffrey J. Gordon, Han Zhao

Online Optimization in Games via Control Theory: Connecting Regret, Passivity and Poincaré Recurrence

Yun Kuen Cheung, Georgios Piliouras

Problem Dependent View on Structured Thresholding Bandit Problems

James Cheshire, Pierre Menard, Alexandra Carpentier

Exact Optimization of Conformal Predictors via Incremental and Decremental Learning

Giovanni Cherubin, Konstantinos Chatzikokolakis, Martin Jaggi

Self-supervised and Supervised Joint Training for Resource-rich Machine Translation

Yong Cheng, Wei Wang, Lu Jiang, Wolfgang Macherey

SPADE: A Spectral Method for Black-Box Adversarial Robustness Evaluation

Wuxinlin Cheng, Chenhui Deng, Zhiqiang Zhao, Yaohui Cai, Zhiru Zhang

ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training

Jianfei Chen, Lianmin Zheng, Zhewei Yao, Dequan Wang, Ion Stoica

Accelerating Gossip SGD with Periodic Global Averaging

Yiming Chen, Kun Yuan, Yingya Zhang, PAN PAN, Yinghui Xu

A Receptor Skeleton for Capsule Neural Networks

Jintai Chen, Hongyun Yu, Chengde Qian, Danny Z Chen, Jian Wu

Cyclically Equivariant Neural Decoders for Cyclic Codes

Xiangyu Chen, Min Ye

Overcoming Catastrophic Forgetting by Bayesian Generative Regularization

Pei-Hung Chen, Wei Wei, Cho-jui Hsieh, Bo Dai

Representation Subspace Distance for Domain Adaptation Regression

Xinyang Chen, Sinan Wang, Jianmin Wang, Mingsheng Long

Large-Scale Multi-Agent Deep FBSDEs

Tianrong Chen, Ziyi O Wang, Ioannis Exarchos, Evangelos Theodorou

Analysis of stochastic Lanczos quadrature for spectrum approximation

Tyler Chen, Thomas Trogdon, Shashanka Ubaru

Data-driven Prediction of General Hamiltonian Dynamics via Learning Exactly-Symplectic Maps

Renyi Chen, Molei Tao

Network Inference and Influence Maximization from Samples

Wei Chen, Xiaoming Sun, Jialin Zhang, Zhijie Zhang

A Unified Lottery Ticket Hypothesis for Graph Neural Networks

Tianlong Chen, Yongduo Sui, Xuxi Chen, Aston Zhang, Zhangyang Wang

Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting

Yuzhou Chen, Ignacio Segovia, Yulia R. Gel

Large-Margin Contrastive Learning with Distance Polarization Regularizer

Shuo Chen, Gang Niu, Chen Gong, Jun Li, Jian Yang

SpreadsheetCoder: Formula Prediction from Semi-structured Context

Xinyun Chen, Petros Maniatis, Rishabh Singh, Charles Sutton, Hanjun Dai

Finding the Stochastic Shortest Path with Low Regret: the Adversarial Cost and Unknown Transition Case

Liyu Chen, Haipeng Luo

CARTL: Cooperative Adversarially-Robust Transfer Learning

Dian Chen, Hongxin Hu, Qian Wang, Li Yinli, Cong Wang

Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation

Xiaohui Chen, Xu Han, Jiajing Hu, Francisco Ruiz, Liping Liu

Mandoline: Model Evaluation under Distribution Shift

Mayee Chen, Karan Goel, Nimit S Sohoni, Fait Poms, Kayvon Fatahalian

Learning Self-Modulating Attention in Continuous Time Space with Applications to Sequential Recommendation

Chao Chen, Haoyu Geng, Nianzu Yang, Junchi Yan, Daiyue Xue

Decentralized Riemannian Gradient Descent on the Stiefel Manifold

Shixiang Chen, Alfredo Garcia, Mingyi Hong, Shahin Shahrampour

Neural Feature Matching in Implicit 3D Representations

Yunlu Chen, Basura Fernando, Hakan Bilen, Thomas Mensink, Efstratios Gavves

Scalable Computations of Wasserstein Barycenter via Input Convex Neural Networks

Jiaojiao Fan, Amirhossein Taghvaei, Yongxin Chen

Improved Corruption Robust Algorithms for Episodic Reinforcement Learning

Yifang Chen, Simon Du, Kevin Jamieson

Integer Programming for Causal Structure Learning in the Presence of Latent Variables

Rui Chen, Sanjeeb Dash, Tian Gao

Unsupervised Learning of Visual 3D Keypoints for Control

Boyuan Chen, Pieter Abbeel, Deepak Pathak

Unified Robust Semi-Supervised Variational Autoencoder

Xu Chen

Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills

Yevgen Chebotar, Karol Hausman, Yao Lu, Ted Xiao, Dmitry Kalashnikov

Classification with Rejection Based on Cost-sensitive Classification

Nontawat Charoenphakdee, Zhenghang Cui, Yivan Zhang, Masashi Sugiyama

Solving Challenging Dexterous Manipulation Tasks With Trajectory Optimisation and Reinforcement Learning

Henry J Charlesworth, Giovanni Montana

Differentiable Spatial Planning using Transformers

Devendra Singh Chaplot, Deepak Pathak, Jitendra Malik

DeepWalking Backwards: From Embeddings Back to Graphs

Sudhanshu Chanpuriya, Cameron Musco, Konstantinos Sotiropoulos, Charalampos Tsourakakis

Image-Level or Object-Level? A Tale of Two Resampling Strategies for Long-Tailed Detection

Nadine Chang, Zhiding Yu, Yu-Xiong Wang, Animashree Anandkumar, Sanja Fidler

Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment

Michael Chang, Sid Kaushik, Sergey Levine, Tom Griffiths

Locally Private k-Means in One Round

Alisa Chang, Badih Ghazi, Ravi Kumar, Pasin Manurangsi

Goal-Conditioned Reinforcement Learning with Imagined Subgoals

Elliot Chane-Sane, Cordelia Schmid, Ivan Laptev

HoroPCA: Hyperbolic Dimensionality Reduction via Horospherical Projections

Ines Chami, Albert Gu, Dat P Nguyen, Christopher Re

GRAND: Graph Neural Diffusion

Ben Chamberlain, James Rowbottom, Maria I Gorinova, Michael Bronstein, Stefan Webb

Learning Node Representations Using Stationary Flow Prediction on Large Payment and Cash Transaction Networks

Ciwan Ceylan, Salla Franzén, Florian T. Pokorny

Learning Routines for Effective Off-Policy Reinforcement Learning

Edoardo Cetin, Oya Celiktutan

Revisiting Rainbow: Promoting more insightful and inclusive deep reinforcement learning research

Johan Samir Obando Ceron, Pablo Samuel Castro

Best Model Identification: A Rested Bandit Formulation

Leonardo Cella, Massimiliano Pontil, Claudio Gentile

Fair Classification with Noisy Protected Attributes: A Framework with Provable Guarantees

L. Elisa Celis, Lingxiao Huang, Vijay Keswani, Nisheeth K. Vishnoi

Disentangling syntax and semantics in the brain with deep networks

Charlotte Caucheteux, Alexandre Gramfort, Jean-Remi King

Marginal Contribution Feature Importance - an Axiomatic Approach for Explaining Data

Amnon Catav, Boyang Fu, Yazeed Zoabi, Ahuva Libi Weiss Meilik, Noam Shomron

Multi-Receiver Online Bayesian Persuasion

Matteo Castiglioni, Alberto Marchesi, Andrea Celli, Nicola Gatti

Online Policy Gradient for Model Free Learning of Linear Quadratic Regulators with $\sqrt$T Regret

Asaf B Cassel, Tomer Koren

Optimizing persistent homology based functions

Mathieu Carrière, Frederic Chazal, Marc Glisse, Yuichi Ike, Hariprasad Kannan

Parameter-free Locally Accelerated Conditional Gradients

Alejandro Carderera, Jelena Diakonikolas, Cheuk Yin Lin, Sebastian Pokutta

Learning from Similarity-Confidence Data

Yuzhou Cao, Lei Feng, Yitian Xu, Bo An, Gang Niu

Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Design

Yue Cao, Payel Das, Vijil Chenthamarakshan, Pin-Yu Chen, Igor Melnyk

Asymmetric Heavy Tails and Implicit Bias in Gaussian Noise Injections

Alexander Camuto, Xiaoyu Wang, Lingjiong Zhu, Chris Holmes, Mert Gurbuzbalaban

A Gradient Based Strategy for Hamiltonian Monte Carlo Hyperparameter Optimization

Andrew Campbell, Wenlong Chen, Vincent Stimper, José Miguel Hernández-Lobato, Yichuan Zhang

High-dimensional Experimental Design and Kernel Bandits

Romain Camilleri, Kevin Jamieson, Julian Katz-Samuels

On Lower Bounds for Standard and Robust Gaussian Process Bandit Optimization

Xu Cai, Jonathan Scarlett

GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training

Tianle Cai, Shengjie Luo, Keyulu Xu, Di He, Tie-yan Liu

A Zeroth-Order Block Coordinate Descent Algorithm for Huge-Scale Black-Box Optimization

HanQin Cai, Yuchen Lou, Daniel Mckenzie, Wotao Yin

Lenient Regret and Good-Action Identification in Gaussian Process Bandits

Xu Cai, Selwyn Gomes, Jonathan Scarlett

A Theory of Label Propagation for Subpopulation Shift

Tianle Cai, Ruiqi Gao, Jason Lee, Qi Lei

Finite mixture models do not reliably learn the number of components

Diana Cai, Trevor Campbell, Tamara Broderick

Disambiguation of Weak Supervision leading to Exponential Convergence rates

Vivien A Cabannnes, Francis Bach, Alessandro Rudi

Differentially Private Correlation Clustering

Mark Bun, Marek Elias, Janardhan Kulkarni

Narrow Margins: Classification, Margins and Fat Tails

Francois Buet-Golfouse

Model-Free and Model-Based Policy Evaluation when Causality is Uncertain

David A Bruns-Smith

Value Alignment Verification

Daniel S Brown, Jordan Schneider, Anca Dragan, Scott Niekum

Machine Unlearning for Random Forests

Jonathan Brophy, Daniel Lowd

Reinforcement Learning of Implicit and Explicit Control Flow Instructions

Ethan Brooks, Janarthanan Rajendran, Richard L Lewis, Satinder Singh

Evaluating the Implicit Midpoint Integrator for Riemannian Hamiltonian Monte Carlo

James Brofos, Roy R Lederman

High-Performance Large-Scale Image Recognition Without Normalization

Andy Brock, Soham De, Samuel L Smith, Karen Simonyan

Offline Contextual Bandits with Overparameterized Models

David Brandfonbrener, William Whitney, Rajesh Ranganath, Joan Bruna

The Hintons in your Neural Network: a Quantum Field Theory View of Deep Learning

Roberto Bondesan, Max Welling

Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks

Cristian Bodnar, Fabrizio Frasca, Yuguang Wang, Nina Otter, Guido F Montufar

Black-box density function estimation using recursive partitioning

Erik Bodin, Zhenwen Dai, Neill Campbell, Carl Henrik Ek

One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning

Avrim Blum, Nika Haghtalab, Richard Lanas Phillips, Han Shao

Multiplying Matrices Without Multiplying

Davis Blalock, John Guttag

Low-Precision Reinforcement Learning: Running Soft Actor-Critic in Half Precision

Johan Bjorck, Xiangyu Chen, Christopher De Sa, Carla P Gomes, Kilian Weinberger

Online Learning for Load Balancing of Unknown Monotone Resource Allocation Games

Ilai Bistritz, Nicholas Bambos

Scalable Normalizing Flows for Permutation Invariant Densities

Marin Biloš, Stephan Günnemann

Model Distillation for Revenue Optimization: Interpretable Personalized Pricing

Max Biggs, Wei Sun, Markus Ettl

Neural Symbolic Regression that scales

Luca Biggio, Tommaso Bendinelli, Alexander Neitz, Aurelien Lucchi, Giambattista Parascandolo

Follow-the-Regularized-Leader Routes to Chaos in Routing Games

Jakub Bielawski, Thiparat Chotibut, Fryderyk Falniowski, Grzegorz Kosiorowski, Michał Misiurewicz

TempoRL: Learning When to Act

André Biedenkapp, Raghu Rajan, Frank Hutter, Marius Lindauer

Non-Autoregressive Electron Redistribution Modeling for Reaction Prediction

Hangrui Bi, Hengyi Wang, Chence Shi, Connor Coley, Jian Tang