ICML 2021

1,183 papers

Dynamic Planning and Learning under Recovering Rewards

David Simchi-Levi, Zeyu Zheng, Feng Zhu

Collaborative Bayesian Optimization with Fair Regret

Rachael Hwee Ling Sim, Yehong Zhang, Bryan Kian Hsiang Low, Patrick Jaillet

Directed Graph Embeddings in Pseudo-Riemannian Manifolds

Aaron Sim, Maciej L Wiatrak, Angus Brayne, Paidi Creed, Saee Paliwal

A Precise Performance Analysis of Support Vector Regression

Houssem Sifaou, Abla Kammoun, Mohamed-Slim Alouini

On Characterizing GAN Convergence Through Proximal Duality Gap

Sahil Sidheekh, Aroof Aimen, Narayanan C Krishnan

Testing Group Fairness via Optimal Transport Projections

Nian Si, Karthyek Murthy, Jose Blanchet, Viet Anh Nguyen

Aggregating From Multiple Target-Shifted Sources

Changjian Shui, Zijian Li, Jiaqi Li, Christian Gagné, Charles X Ling

Zoo-Tuning: Adaptive Transfer from A Zoo of Models

Yang Shu, Zhi Kou, ZHANGJIE CAO, Jianmin Wang, Mingsheng Long

AGENT: A Benchmark for Core Psychological Reasoning

Tianmin Shu, Abhishek Bhandwaldar, Chuang Gan, Kevin Smith, Shari Liu

Large-Scale Meta-Learning with Continual Trajectory Shifting

JaeWoong Shin, Hae Beom Lee, Boqing Gong, Sung Ju Hwang

GANMEX: One-vs-One Attributions using GAN-based Model Explainability

Sheng-Min Shih, Pin-Ju Tien, Zohar Karnin

Deeply-Debiased Off-Policy Interval Estimation

Chengchun Shi, Runzhe Wan, Victor Chernozhukov, Rui Song

Segmenting Hybrid Trajectories using Latent ODEs

Ruian Shi, Quaid Morris

Learning Gradient Fields for Molecular Conformation Generation

Chence Shi, Shitong Luo, Minkai Xu, Jian Tang

SparseBERT: Rethinking the Importance Analysis in Self-attention

Han Shi, Jiahui Gao, Xiaozhe Ren, Hang Xu, Xiaodan Liang

State Relevance for Off-Policy Evaluation

Simon P Shen, Yecheng Ma, Omer Gottesman, Finale Doshi-velez

Backdoor Scanning for Deep Neural Networks through K-Arm Optimization

Guangyu Shen, Yingqi Liu, Guanhong Tao, Shengwei An, Qiuling Xu

Sample-Optimal PAC Learning of Halfspaces with Malicious Noise

Jie Shen

On the Power of Localized Perceptron for Label-Optimal Learning of Halfspaces with Adversarial Noise

Jie Shen

Personalized Federated Learning using Hypernetworks

Aviv Shamsian, Aviv Navon, Ethan Fetaya, Gal Chechik

Equivariant Networks for Pixelized Spheres

Mehran Shakerinava, Siamak Ravanbakhsh

RRL: Resnet as representation for Reinforcement Learning

Rutav M Shah, Vikash Kumar

Online Submodular Resource Allocation with Applications to Rebalancing Shared Mobility Systems

Pier Giuseppe Sessa, Ilija Bogunovic, Andreas Krause, Maryam Kamgarpour

State Entropy Maximization with Random Encoders for Efficient Exploration

Younggyo Seo, Lili Chen, Jinwoo Shin, Honglak Lee, Pieter Abbeel

Pure Exploration and Regret Minimization in Matching Bandits

Flore Sentenac, Jialin Yi, Clément Calauzènes, Vianney Perchet, Milan Vojnovic

Top-k eXtreme Contextual Bandits with Arm Hierarchy

Rajat Sen, Alexander Rakhlin, Lexing Ying, Rahul Kidambi, Dean Foster

Learning Intra-Batch Connections for Deep Metric Learning

Jenny Denise Seidenschwarz, Ismail Elezi, Laura Leal-Taixé

Connecting Sphere Manifolds Hierarchically for Regularization

Damien Scieur, Youngsung Kim

Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attacks

Avi Schwarzschild, Micah Goldblum, Arjun Gupta, John P Dickerson, Tom Goldstein

Equivariant message passing for the prediction of tensorial properties and molecular spectra

Kristof Schütt, Oliver Unke, Michael Gastegger

Descending through a Crowded Valley - Benchmarking Deep Learning Optimizers

Robin M Schmidt, Frank Schneider, Philipp Hennig

Linear Transformers Are Secretly Fast Weight Programmers

Imanol Schlag, Kazuki Irie, Jürgen Schmidhuber

Low-Rank Sinkhorn Factorization

meyer scetbon, Marco Cuturi, Gabriel Peyré

A Representation Learning Perspective on the Importance of Train-Validation Splitting in Meta-Learning

Nikunj Saunshi, Arushi Gupta, Wei Hu

E(n) Equivariant Graph Neural Networks

Vı́ctor Garcia Satorras, Emiel Hoogeboom, Max Welling

Towards Understanding Learning in Neural Networks with Linear Teachers

Roei Sarussi, Alon Brutzkus, Amir Globerson

Recomposing the Reinforcement Learning Building Blocks with Hypernetworks

Elad Sarafian, Shai Keynan, Sarit Kraus

Meta-Learning Bidirectional Update Rules

Mark Sandler, Max Vladymyrov, Andrey Zhmoginov, Nolan Miller, Tom Madams

Momentum Residual Neural Networks

Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyré

Asymptotics of Ridge Regression in Convolutional Models

Mojtaba Sahraee-Ardakan, Tung Mai, Anup Rao, Ryan A. Rossi, Sundeep Rangan

Optimal regret algorithm for Pseudo-1d Bandit Convex Optimization

Aadirupa Saha, Nagarajan Natarajan, Praneeth Netrapalli, Prateek Jain

Dueling Convex Optimization

Aadirupa Saha, Tomer Koren, Yishay Mansour

Adversarial Dueling Bandits

Aadirupa Saha, Tomer Koren, Yishay Mansour

Stochastic Sign Descent Methods: New Algorithms and Better Theory

Mher Safaryan, Peter Richtarik

Unsupervised Part Representation by Flow Capsules

Sara Sabour, Andrea Tagliasacchi, Soroosh Yazdani, Geoffrey Hinton, David J Fleet

Training Data Subset Selection for Regression with Controlled Generalization Error

Durga S, Rishabh Iyer, Ganesh Ramakrishnan, Abir De

Model-Based Reinforcement Learning via Latent-Space Collocation

Oleh Rybkin, Chuning Zhu, Anusha Nagabandi, Kostas Daniilidis, Igor Mordatch

Simple and Effective VAE Training with Calibrated Decoders

Oleh Rybkin, Kostas Daniilidis, Sergey Levine

UnICORNN: A recurrent model for learning very long time dependencies

T. Konstantin Rusch, Siddhartha Mishra

Tilting the playing field: Dynamical loss functions for machine learning

Miguel Ruiz-Garcia, Ge Zhang, Samuel S Schoenholz, Andrea J. Liu

On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes

Tim G. J. Rudner, Oscar Key, Yarin Gal, Tom Rainforth

Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding

Yangjun Ruan, Karen Ullrich, Daniel S Severo, James Townsend, Ashish Khisti

An Algorithm for Stochastic and Adversarial Bandits with Switching Costs

Chloé Rouyer, Yevgeny Seldin, Nicolò Cesa-bianchi

PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees

Jonas Rothfuss, Vincent Fortuin, Martin Josifoski, Andreas Krause

Multi-group Agnostic PAC Learnability

Guy N Rothblum, Gal Yona

Karsten Roth, Timo Milbich, Bjorn Ommer, Joseph Paul Cohen, Marzyeh Ghassemi

Benchmarks, Algorithms, and Metrics for Hierarchical Disentanglement

Andrew Ross, Finale Doshi-velez

On the Predictability of Pruning Across Scales

Jonathan S Rosenfeld, Jonathan Frankle, Michael Carbin, Nir Shavit

Discretization Drift in Two-Player Games

Mihaela C Rosca, Yan Wu, Benoit Dherin, David Barrett

TeachMyAgent: a Benchmark for Automatic Curriculum Learning in Deep RL

Clément Romac, Rémy Portelas, Katja Hofmann, Pierre-yves Oudeyer

Representation Matters: Assessing the Importance of Subgroup Allocations in Training Data

Esther Rolf, Theodora T Worledge, Benjamin Recht, Michael Jordan

On Linear Identifiability of Learned Representations

Geoffrey Roeder, Luke Metz, Durk Kingma

Principled Simplicial Neural Networks for Trajectory Prediction

T. Mitchell Roddenberry, Nicholas Glaze, Santiago Segarra

Best Arm Identification in Graphical Bilinear Bandits

Geovani Rizk, Albert Thomas, Igor Colin, Rida Laraki, Yann Chevaleyre

Solving high-dimensional parabolic PDEs using the tensor train format

Lorenz Richter, Leon Sallandt, Nikolas Nüsken

Integrated Defense for Resilient Graph Matching

Jiaxiang Ren, Zijie Zhang, Jiayin Jin, Xin Zhao, Sixing Wu

Interpreting and Disentangling Feature Components of Various Complexity from DNNs

Jie Ren, Mingjie Li, Zexu Liu, Quanshi Zhang

LEGO: Latent Execution-Guided Reasoning for Multi-Hop Question Answering on Knowledge Graphs

Hongyu Ren, Hanjun Dai, Bo Dai, Xinyun Chen, Michihiro Yasunaga

Sharf: Shape-conditioned Radiance Fields from a Single View

Konstantinos Rematas, Ricardo Martin-Brualla, Vittorio Ferrari

Classifying high-dimensional Gaussian mixtures: Where kernel methods fail and neural networks succeed

Maria Refinetti, Sebastian Goldt, Florent Krzakala, Lenka Zdeborová

Align, then memorise: the dynamics of learning with feedback alignment

Maria Refinetti, Stéphane D’Ascoli, Ruben Ohana, Sebastian Goldt

Implicit Regularization in Tensor Factorization

Noam Razin, Asaf Maman, Nadav Cohen

Cross-domain Imitation from Observations

Dripta S. Raychaudhuri, Sujoy Paul, Jeroen Vanbaar, Amit K. Roy-Chowdhury

Disentangling Sampling and Labeling Bias for Learning in Large-output Spaces

Ankit Singh Rawat, Aditya K Menon, Wittawat Jitkrittum, Sadeep Jayasumana, Felix Yu

Enhancing Robustness of Neural Networks through Fourier Stabilization

Netanel Raviv, Aidan Kelley, Minzhe Guo, Yevgeniy Vorobeychik

Generative Particle Variational Inference via Estimation of Functional Gradients

Neale Ratzlaff, Qinxun Bai, Li Fuxin, Wei Xu

Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting

Kashif Rasul, Calvin Seward, Ingmar Schuster, Roland Vollgraf

MSA Transformer

Roshan M Rao, Jason Liu, Robert Verkuil, Joshua Meier, John Canny

End-to-End Learning of Coherent Probabilistic Forecasts for Hierarchical Time Series

Syama Sundar Rangapuram, Lucien D Werner, Konstantinos Benidis, Pedro Mercado, Jan Gasthaus

Zero-Shot Text-to-Image Generation

Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss

Differentially Private Sliced Wasserstein Distance

Alain Rakotomamonjy, Ralaivola Liva

Hierarchical Clustering of Data Streams: Scalable Algorithms and Approximation Guarantees

Anand Rajagopalan, Fabio Vitale, Danny Vainstein, Gui Citovsky, Cecilia M Procopiuc

Decoupling Value and Policy for Generalization in Reinforcement Learning

Roberta Raileanu, Rob Fergus

Towards Open Ad Hoc Teamwork Using Graph-based Policy Learning

Muhammad A Rahman, Niklas Hopner, Filippos Christianos, Stefano V Albrecht

A General Framework For Detecting Anomalous Inputs to DNN Classifiers

Jayaram Raghuram, Varun Chandrasekaran, Somesh Jha, Suman Banerjee

Learning Transferable Visual Models From Natural Language Supervision

Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh

On Reward-Free RL with Kernel and Neural Function Approximations: Single-Agent MDP and Markov Game

Shuang Qiu, Jieping Ye, Zhaoran Wang, Zhuoran Yang

Optimization Planning for 3D ConvNets

Zhaofan Qiu, Ting Yao, Chong-Wah Ngo, Tao Mei

Provably Efficient Fictitious Play Policy Optimization for Zero-Sum Markov Games with Structured Transitions

Shuang Qiu, Xiaohan Wei, Jieping Ye, Zhaoran Wang, Zhuoran Yang

Neural Transformation Learning for Deep Anomaly Detection Beyond Images

Chen Qiu, Timo Pfrommer, Marius Kloft, Stephan Mandt, Maja Rudolph

Budgeted Heterogeneous Treatment Effect Estimation

Tian Qin, Tian-Zuo Wang, Zhi-Hua Zhou

Density Constrained Reinforcement Learning

Zengyi Qin, Yuxiao Chen, Chuchu Fan

Oneshot Differentially Private Top-k Selection

Gang Qiao, Weijie Su, Li Zhang

Efficient Differentiable Simulation of Articulated Bodies

Yi-Ling Qiao, Junbang Liang, Vladlen Koltun, Ming C Lin

Global Prosody Style Transfer Without Text Transcriptions

Kaizhi Qian, Yang Zhang, Shiyu Chang, Jinjun Xiong, Chuang Gan

A Probabilistic Approach to Neural Network Pruning

Xin Qian, Diego Klabjan

BANG: Bridging Autoregressive and Non-autoregressive Generation with Large Scale Pretraining

Weizhen Qi, Yeyun Gong, Jian Jiao, Yu Yan, Weizhu Chen

Dense for the Price of Sparse: Improved Performance of Sparsely Initialized Networks via a Subspace Offset

Ilan Price, Jared Tanner

Bias-Free Scalable Gaussian Processes via Randomized Truncations

Andres Potapczynski, Luhuan Wu, Dan Biderman, Geoff Pleiss, John P Cunningham

Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech

Vadim Popov, Ivan Vovk, Vladimir Gogoryan, Tasnima Sadekova, Mikhail Kudinov