ICML 2022

1,233 papers

Off-Policy Evaluation for Large Action Spaces via Embeddings

Yuta Saito, Thorsten Joachims

Unraveling Attention via Convex Duality: Analysis and Interpretations of Vision Transformers

Arda Sahiner, Tolga Ergen, Batu Ozturkler, John Pauly, Morteza Mardani

Optimal and Efficient Dynamic Regret Algorithms for Non-Stationary Dueling Bandits

Aadirupa Saha, Shubham Gupta

Versatile Dueling Bandits: Best-of-both World Analyses for Learning from Relative Preferences

Aadirupa Saha, Pierre Gaillard

FedNL: Making Newton-Type Methods Applicable to Federated Learning

Mher Safaryan, Rustem Islamov, Xun Qian, Peter Richtarik

Exploiting Independent Instruments: Identification and Distribution Generalization

Sorawit Saengkyongam, Leonard Henckel, Niklas Pfister, Jonas Peters

Hindering Adversarial Attacks with Implicit Neural Representations

Andrei A Rusu, Dan Andrei Calian, Sven Gowal, Raia Hadsell

Graph-Coupled Oscillator Networks

T. Konstantin Rusch, Ben Chamberlain, James Rowbottom, Siddhartha Mishra, Michael Bronstein

Continual Learning via Sequential Function-Space Variational Inference

Tim G. J. Rudner, Freddie Bickford Smith, Qixuan Feng, Yee Whye Teh, Yarin Gal

Constraint-based graph network simulator

Yulia Rubanova, Alvaro Sanchez-Gonzalez, Tobias Pfaff, Peter Battaglia

Direct Behavior Specification via Constrained Reinforcement Learning

Julien Roy, Roger Girgis, Joshua Romoff, Pierre-Luc Bacon, Chris J Pal

Learning to Infer Structures of Network Games

Emanuele Rossi, Federico Monti, Yan Leng, Michael Bronstein, Xiaowen Dong

Efficiently Learning the Topology and Behavior of a Networked Dynamical System Via Active Queries

Daniel J Rosenkrantz, Abhijin Adiga, Madhav Marathe, Zirou Qiu, S S Ravi

A Consistent and Efficient Evaluation Strategy for Attribution Methods

Yao Rong, Tobias Leemann, Vadim Borisov, Gjergji Kasneci, Enkelejda Kasneci

Dual Decomposition of Convex Optimization Layers for Consistent Attention in Medical Images

Tom Ron, Tamir Hazan

Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models

Paul Rolland, Volkan Cevher, Matthäus Kleindessner, Chris Russell, Dominik Janzing

Function-space Inference with Sparse Implicit Processes

Simon Rodrı́guez-Santana, Bryan Zaldivar, Daniel Hernández-lobato

Short-Term Plasticity Neurons Learning to Learn and Forget

Hector Garcia Rodriguez, Qinghai Guo, Timoleon Moraitis

LyaNet: A Lyapunov Framework for Training Neural ODEs

Ivan Dario Jimenez Rodriguez, Aaron Ames, Yisong Yue

Probabilistically Robust Learning: Balancing Average and Worst-case Performance

Alexander Robey, Luiz Chamon, George J. Pappas, Hamed Hassani

Robust SDE-Based Variational Formulations for Solving Linear PDEs via Deep Learning

Lorenz Richter, Julius Berner

3PC: Three Point Compressors for Communication-Efficient Distributed Training and a Better Theory for Lazy Aggregation

Peter Richtarik, Igor Sokolov, Elnur Gasanov, Ilyas Fatkhullin, Zhize Li

A Unified View on PAC-Bayes Bounds for Meta-Learning

Arezou Rezazadeh

Benchmarking and Analyzing Point Cloud Classification under Corruptions

Jiawei Ren, Liang Pan, Ziwei Liu

Towards Theoretical Analysis of Transformation Complexity of ReLU DNNs

Jie Ren, Mingjie Li, Meng Zhou, Shih-Han Chan, Quanshi Zhang

Proximal Exploration for Model-guided Protein Sequence Design

Zhizhou Ren, Jiahan Li, Fan Ding, Yuan Zhou, Jianzhu Ma

The dynamics of representation learning in shallow, non-linear autoencoders

Maria Refinetti, Sebastian Goldt

Universality of Winning Tickets: A Renormalization Group Perspective

William T Redman, Tianlong Chen, Zhangyang Wang, Akshunna S. Dogra

One-Pass Algorithms for MAP Inference of Nonsymmetric Determinantal Point Processes

Aravind Reddy, Ryan A. Rossi, Zhao Song, Anup Rao, Tung Mai

Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Neural Networks

Noam Razin, Asaf Maman, Nadav Cohen

Linear Adversarial Concept Erasure

Shauli Ravfogel, Michael Twiton, Yoav Goldberg, Ryan D Cotterell

A Closer Look at Smoothness in Domain Adversarial Training

Harsh Rangwani, Sumukh K Aithal, Mayank Mishra, Arihant Jain, Venkatesh Babu Radhakrishnan

Fishr: Invariant Gradient Variances for Out-of-Distribution Generalization

Alexandre Rame, Corentin Dancette, Matthieu Cord

DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale

Samyam Rajbhandari, Conglong Li, Zhewei Yao, Minjia Zhang, Reza Yazdani Aminabadi

Convergence of Uncertainty Sampling for Active Learning

Anant Raj, Francis Bach

Winning the Lottery Ahead of Time: Efficient Early Network Pruning

John Rachwan, Daniel Zügner, Bertrand Charpentier, Simon Geisler, Morgane Ayle

Particle Transformer for Jet Tagging

Huilin Qu, Congqiao Li, Sitian Qian

Generalized Federated Learning via Sharpness Aware Minimization

Zhe Qu, Xingyu Li, Rui Duan, Yao Liu, Bo Tang

Fast and Provable Nonconvex Tensor RPCA

Haiquan Qiu, Yao Wang, Shaojie Tang, Deyu Meng, Quanming Yao

Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement Learning

Shuang Qiu, Lingxiao Wang, Chenjia Bai, Zhuoran Yang, Zhaoran Wang

Latent Outlier Exposure for Anomaly Detection with Contaminated Data

Chen Qiu, Aodong Li, Marius Kloft, Maja Rudolph, Stephan Mandt

Large-scale Stochastic Optimization of NDCG Surrogates for Deep Learning with Provable Convergence

Zi-Hao Qiu, Quanqi Hu, YONGJIAN ZHONG, Lijun Zhang, Tianbao Yang

Spectral Representation of Robustness Measures for Optimization Under Input Uncertainty

Jixiang Qing, Tom Dhaene, Ivo Couckuyt

Graph Neural Architecture Search Under Distribution Shifts

Yijian Qin, Xin Wang, Ziwei Zhang, Pengtao Xie, Wenwu Zhu

Generalizing to Evolving Domains with Latent Structure-Aware Sequential Autoencoder

Tiexin Qin, Shiqi Wang, Haoliang Li

Sample-Efficient Reinforcement Learning with loglog(T) Switching Cost

Dan Qiao, Ming Yin, Ming Min, Yu-Xiang Wang

Interventional Contrastive Learning with Meta Semantic Regularizer

Wenwen Qiang, Jiangmeng Li, Changwen Zheng, Bing Su, Hui Xiong

ContentVec: An Improved Self-Supervised Speech Representation by Disentangling Speakers

Kaizhi Qian, Yang Zhang, Heting Gao, Junrui Ni, Cheng-I Lai

The Teaching Dimension of Regularized Kernel Learners

Hong Qian, Xu-Hui Liu, Chen-Xi Su, Aimin Zhou, Yang Yu

Universal Joint Approximation of Manifolds and Densities by Simple Injective Flows

Michael Puthawala, Matti Lassas, Ivan Dokmanić, Maarten de Hoop

Nonlinear Feature Diffusion on Hypergraphs

Konstantin Prokopchik, Austin R Benson, Francesco Tudisco

Hardness and Algorithms for Robust and Sparse Optimization

Eric Price, Sandeep Silwal, Samson Zhou

A Simple Guard for Learned Optimizers

Isabeau Prémont-Schwarz, Jaroslav Vı́tků, Jan Feyereisl

On the Practicality of Deterministic Epistemic Uncertainty

Janis Postels, Mattia Segu, Tao Sun, Luca Daniel Sieber, Luc Van Gool

Adaptive Second Order Coresets for Data-efficient Machine Learning

Omead Pooladzandi, David Davini, Baharan Mirzasoleiman

Debiaser Beware: Pitfalls of Centering Regularized Transport Maps

Aram-Alexandre Pooladian, Marco Cuturi, Jonathan Niles-Weed

Offline Meta-Reinforcement Learning with Online Self-Supervision

Vitchyr H Pong, Ashvin V Nair, Laura M Smith, Catherine Huang, Sergey Levine

Constrained Offline Policy Optimization

Nicholas Polosky, Bruno C. da Silva, Madalina Fiterau, Jithin Jagannath

Geometric Multimodal Contrastive Representation Learning

Petra Poklukar, Miguel Vasco, Hang Yin, Francisco S. Melo, Ana Paiva

Deep Networks on Toroids: Removing Symmetries Reveals the Structure of Flat Regions in the Landscape Geometry

Fabrizio Pittorino, Antonio Ferraro, Gabriele Perugini, Christoph Feinauer, Carlo Baldassi

Federated Learning with Partial Model Personalization

Krishna Pillutla, Kshitiz Malik, Abdel-rahman Mohamed, Mike Rabbat, Maziar Sanjabi

A Differential Entropy Estimator for Training Neural Networks

Georg Pichler, Pierre Jean A. Colombo, Malik Boudiaf, Günther Koliander, Pablo Piantanida

Multi-scale Feature Learning Dynamics: Insights for Double Descent

Mohammad Pezeshki, Amartya Mitra, Yoshua Bengio, Guillaume Lajoie

Differentiable Top-k Classification Learning

Felix Petersen, Hilde Kuehne, Christian Borgelt, Oliver Deussen

Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets

Xingang Peng, Shitong Luo, Jiaqi Guan, Qi Xie, Jian Peng

Branchformer: Parallel MLP-Attention Architectures to Capture Local and Global Context for Speech Recognition and Understanding

Yifan Peng, Siddharth Dalmia, Ian Lane, Shinji Watanabe

Neural Network Pruning Denoises the Features and Makes Local Connectivity Emerge in Visual Tasks

Franco Pellegrini, Giulio Biroli

Learning to Cut by Looking Ahead: Cutting Plane Selection via Imitation Learning

Max B Paulus, Giulia Zarpellon, Andreas Krause, Laurent Charlin, Chris Maddison

POET: Training Neural Networks on Tiny Devices with Integrated Rematerialization and Paging

Shishir G. Patil, Paras Jain, Prabal Dutta, Ion Stoica, Joseph Gonzalez

Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution

Vihang Patil, Markus Hofmarcher, Marius-Constantin Dinu, Matthias Dorfer, Patrick M Blies

Reese Pathak, Cong Ma, Martin Wainwright

Neural Language Models are not Born Equal to Fit Brain Data, but Training Helps

Alexandre Pasquiou, Yair Lakretz, John T Hale, Bertrand Thirion, Christophe Pallier

Evolving Curricula with Regret-Based Environment Design

Jack Parker-Holder, Minqi Jiang, Michael Dennis, Mikayel Samvelyan, Jakob Foerster

Kernel Methods for Radial Transformed Compositional Data with Many Zeros

Junyoung Park, Changwon Yoon, Cheolwoo Park, Jeongyoun Ahn

Exact Optimal Accelerated Complexity for Fixed-Point Iterations

Jisun Park, Ernest K Ryu

Blurs Behave Like Ensembles: Spatial Smoothings to Improve Accuracy, Uncertainty, and Robustness

Namuk Park, Songkuk Kim

Learning Symmetric Embeddings for Equivariant World Models

Jung Yeon Park, Ondrej Biza, Linfeng Zhao, Jan-Willem van de Meent, Robin Walters

The Unsurprising Effectiveness of Pre-Trained Vision Models for Control

Simone Parisi, Aravind Rajeswaran, Senthil Purushwalkam, Abhinav Gupta

Validating Causal Inference Methods

Harsh Parikh, Carlos Varjão, Louise Xu, Eric Tchetgen Tchetgen

A Theoretical Comparison of Graph Neural Network Extensions

Pál András Papp, Roger Wattenhofer

Constrained Discrete Black-Box Optimization using Mixed-Integer Programming

Theodore P Papalexopoulos, Christian Tjandraatmadja, Ross Anderson, Juan Pablo Vielma, David Belanger

Towards Coherent and Consistent Use of Entities in Narrative Generation

Pinelopi Papalampidi, Kris Cao, Tomas Kocisky

Robustness and Accuracy Could Be Reconcilable by (Proper) Definition

Tianyu Pang, Min Lin, Xiao Yang, Jun Zhu, Shuicheng Yan

A Unified Weight Initialization Paradigm for Tensorial Convolutional Neural Networks

Yu Pan, Zeyong Su, Ao Liu, Wang Jingquan, Nannan Li

Plan Better Amid Conservatism: Offline Multi-Agent Reinforcement Learning with Actor Rectification

Ling Pan, Longbo Huang, Tengyu Ma, Huazhe Xu

On Learning Mixture of Linear Regressions in the Non-Realizable Setting

Soumyabrata Pal, Arya Mazumdar, Rajat Sen, Avishek Ghosh

A Study on the Ramanujan Graph Property of Winning Lottery Tickets

Bithika Pal, Arindam Biswas, Sudeshna Kolay, Pabitra Mitra, Biswajit Basu

History Compression via Language Models in Reinforcement Learning

Fabian Paischer, Thomas Adler, Vihang Patil, Angela Bitto-Nemling, Markus Holzleitner

Zero-shot AutoML with Pretrained Models

Ekrem Öztürk, Fabio Ferreira, Hadi Jomaa, Lars Schmidt-Thieme, Josif Grabocka

Scalable Deep Gaussian Markov Random Fields for General Graphs

Joel Oskarsson, Per Sidén, Fredrik Lindsten

Antonio Orvieto, Hans Kersting, Frank Proske, Francis Bach, Aurelien Lucchi

Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering

Lorenzo Orecchia, Konstantinos Ameranis, Charalampos Tsourakakis, Kunal Talwar

Generalized Leverage Scores: Geometric Interpretation and Applications

Bruno Ordozgoiti, Antonis Matakos, Aristides Gionis

Multicoated Supermasks Enhance Hidden Networks

Yasuyuki Okoshi, Ángel López Garcı́a-Arias, Kazutoshi Hirose, Kota Ando, Kazushi Kawamura

Fast Finite Width Neural Tangent Kernel

Roman Novak, Jascha Sohl-dickstein, Samuel S Schoenholz

Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval

Pascal Notin, Mafalda Dias, Jonathan Frazer, Javier Marchena-Hurtado, Aidan N Gomez

Utilizing Expert Features for Contrastive Learning of Time-Series Representations

Manuel T Nonnenmacher, Lukas Oldenburg, Ingo Steinwart, David Reeb

Path-Aware and Structure-Preserving Generation of Synthetically Accessible Molecules

Juhwan Noh, Dae-Woong Jeong, Kiyoung Kim, Sehui Han, Moontae Lee

Generative Trees: Adversarial and Copycat

Richard Nock, Mathieu Guillame-Bert

Efficient Test-Time Model Adaptation without Forgetting

Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang, Yaofo Chen, Shijian Zheng