ICML 2022

1,233 papers

Consistent Polyhedral Surrogates for Top-k Classification and Variants

Anish Thilagar, Rafael Frongillo, Jessica J Finocchiaro, Emma Goodwill

Algorithms for the Communication of Samples

Lucas Theis, Noureldin Y Ahmed

Generalised Policy Improvement with Geometric Policy Composition

Shantanu Thakoor, Mark Rowland, Diana Borsa, Will Dabney, Rémi Munos

Reverse Engineering ℓ_p attacks: A block-sparse optimization approach with recovery guarantees

Darshan Thaker, Paris Giampouras, René Vidal

LCANets: Lateral Competition Improves Robustness Against Corruption and Attack

Michael Teti, Garrett Kenyon, Ben Migliori, Juston Moore

LIDL: Local Intrinsic Dimension Estimation Using Approximate Likelihood

Piotr Tempczyk, Rafał Michaluk, Lukasz Garncarek, Przemysław Spurek, Jacek Tabor

Efficient Distributionally Robust Bayesian Optimization with Worst-case Sensitivity

Sebastian Shenghong Tay, Chuan Sheng Foo, Urano Daisuke, Richalynn Leong, Bryan Kian Hsiang Low

FedNest: Federated Bilevel, Minimax, and Compositional Optimization

Davoud Ataee Tarzanagh, Mingchen Li, CHRISTOS THRAMPOULIDIS, Samet Oymak

Cross-Space Active Learning on Graph Convolutional Networks

Yufei Tao, Hao Wu, Shiyuan Deng

Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning

Zhenheng Tang, Yonggang Zhang, Shaohuai Shi, XIN HE, Bo Han

Deep Safe Incomplete Multi-view Clustering: Theorem and Algorithm

Huayi Tang, Yong Liu

Rethinking Graph Neural Networks for Anomaly Detection

Jianheng Tang, JIAJIN LI, Ziqi Gao, Jia Li

Biased Gradient Estimate with Drastic Variance Reduction for Meta Reinforcement Learning

Yunhao Tang

N-Penetrate: Active Learning of Neural Collision Handler for Complex 3D Mesh Deformations

Qingyang Tan, Zherong Pan, Breannan Smith, Takaaki Shiratori, Dinesh Manocha

A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources

Xiaoqing Tan, Chung-Chou H. Chang, Ling Zhou, Lu Tang

SQ-VAE: Variational Bayes on Discrete Representation with Self-annealed Stochastic Quantization

Yuhta Takida, Takashi Shibuya, Weihsiang Liao, Chieh-Hsin Lai, Junki Ohmura

Sequential and Parallel Constrained Max-value Entropy Search via Information Lower Bound

Shion Takeno, Tomoyuki Tamura, Kazuki Shitara, Masayuki Karasuyama

Distributionally-Aware Kernelized Bandit Problems for Risk Aversion

Sho Takemori

Being Properly Improper

Tyler Sypherd, Richard Nock, Lalitha Sankar

Causal Imitation Learning under Temporally Correlated Noise

Gokul Swamy, Sanjiban Choudhury, Drew Bagnell, Steven Wu

Ananda Theertha Suresh, Ziteng Sun, Jae Ro, Felix Yu

Black-Box Tuning for Language-Model-as-a-Service

Tianxiang Sun, Yunfan Shao, Hong Qian, Xuanjing Huang, Xipeng Qiu

Out-of-Distribution Detection with Deep Nearest Neighbors

Yiyou Sun, Yifei Ming, Xiaojin Zhu, Yixuan Li

MAE-DET: Revisiting Maximum Entropy Principle in Zero-Shot NAS for Efficient Object Detection

Zhenhong Sun, Ming Lin, Xiuyu Sun, Zhiyu Tan, Hao Li

Adaptive Random Walk Gradient Descent for Decentralized Optimization

Tao Sun, Dongsheng Li, Bao Wang

AGNAS: Attention-Guided Micro and Macro-Architecture Search

Zihao Sun, YU HU, Shun Lu, Longxing Yang, Jilin Mei

Cliff Diving: Exploring Reward Surfaces in Reinforcement Learning Environments

Ryan Sullivan, Jordan K Terry, Benjamin Black, John P Dickerson

Intriguing Properties of Input-Dependent Randomized Smoothing

Peter Súkenı́k, Aleksei Kuvshinov, Stephan Günnemann

Do Differentiable Simulators Give Better Policy Gradients?

Hyung Ju Suh, Max Simchowitz, Kaiqing Zhang, Russ Tedrake

Continuous-Time Analysis of Accelerated Gradient Methods via Conservation Laws in Dilated Coordinate Systems

Jaewook J Suh, Gyumin Roh, Ernest K Ryu

Improved StyleGAN-v2 based Inversion for Out-of-Distribution Images

Rakshith Subramanyam, Vivek Narayanaswamy, Mark Naufel, Andreas Spanias, Jayaraman J. Thiagarajan

Influence-Augmented Local Simulators: a Scalable Solution for Fast Deep RL in Large Networked Systems

Miguel Suau, Jinke He, Matthijs T. J. Spaan, Frans Oliehoek

Divergence-Regularized Multi-Agent Actor-Critic

Kefan Su, Zongqing Lu

Scaling-up Diverse Orthogonal Convolutional Networks by a Paraunitary Framework

Jiahao Su, Wonmin Byeon, Furong Huang

Plug & Play Attacks: Towards Robust and Flexible Model Inversion Attacks

Lukas Struppek, Dominik Hintersdorf, Antonio De Almeida Correira, Antonia Adler, Kristian Kersting

EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction

Hannes Stärk, Octavian Ganea, Lagnajit Pattanaik, Dr.Regina Barzilay, Tommi Jaakkola

3D Infomax improves GNNs for Molecular Property Prediction

Hannes Stärk, Dominique Beaini, Gabriele Corso, Prudencio Tossou, Christian Dallago

Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders

Samuel Stanton, Wesley Maddox, Nate Gruver, Phillip Maffettone, Emily Delaney

Lightweight Projective Derivative Codes for Compressed Asynchronous Gradient Descent

Pedro J Soto, Ilia Ilmer, Haibin Guan, Jun Li

Saute RL: Almost Surely Safe Reinforcement Learning Using State Augmentation

Aivar Sootla, Alexander I Cowen-Rivers, Taher Jafferjee, Ziyan Wang, David H Mguni

Fully-Connected Network on Noncompact Symmetric Space and Ridgelet Transform based on Helgason-Fourier Analysis

Sho Sonoda, Isao Ishikawa, Masahiro Ikeda

A General Recipe for Likelihood-free Bayesian Optimization

Jiaming Song, Lantao Yu, Willie Neiswanger, Stefano Ermon

TAM: Topology-Aware Margin Loss for Class-Imbalanced Node Classification

Jaeyun Song, Joonhyung Park, Eunho Yang

Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning

Kyunghwan Son, Junsu Kim, Sungsoo Ahn, Roben D Delos Reyes, Yung Yi

The Multivariate Community Hawkes Model for Dependent Relational Events in Continuous-time Networks

Hadeel Soliman, Lingfei Zhao, Zhipeng Huang, Subhadeep Paul, Kevin S Xu

Communicating via Markov Decision Processes

Samuel Sokota, Christian A Schroeder De Witt, Maximilian Igl, Luisa M Zintgraf, Philip Torr

GenLabel: Mixup Relabeling using Generative Models

Jy-yong Sohn, Liang Shang, Hongxu Chen, Jaekyun Moon, Dimitris Papailiopoulos

Smoothed Adversarial Linear Contextual Bandits with Knapsacks

Vidyashankar Sivakumar, Shiliang Zuo, Arindam Banerjee

Demystifying the Adversarial Robustness of Random Transformation Defenses

Chawin Sitawarin, Zachary J Golan-Strieb, David Wagner

Reverse Engineering the Neural Tangent Kernel

James Benjamin Simon, Sajant Anand, Mike Deweese

Coin Flipping Neural Networks

Yuval Sieradzki, Nitzan Hodos, Gal Yehuda, Assaf Schuster

Faster Algorithms for Learning Convex Functions

Ali Siahkamari, Durmus Alp Emre Acar, Christopher Liao, Kelly L Geyer, Venkatesh Saligrama

Fair Representation Learning through Implicit Path Alignment

Changjian Shui, Qi Chen, Jiaqi Li, Boyu Wang, Christian Gagné

Bit Prioritization in Variational Autoencoders via Progressive Coding

Rui Shu, Stefano Ermon

Scalable Computation of Causal Bounds

Madhumitha Shridharan, Garud Iyengar

Log-Euclidean Signatures for Intrinsic Distances Between Unaligned Datasets

Tal Shnitzer, Mikhail Yurochkin, Kristjan Greenewald, Justin M Solomon

Robust Group Synchronization via Quadratic Programming

Yunpeng Shi, Cole M Wyeth, Gilad Lerman

A Minimax Learning Approach to Off-Policy Evaluation in Confounded Partially Observable Markov Decision Processes

Chengchun Shi, Masatoshi Uehara, Jiawei Huang, Nan Jiang

Visual Attention Emerges from Recurrent Sparse Reconstruction

Baifeng Shi, Yale Song, Neel Joshi, Trevor Darrell, Xin Wang

Adversarial Masking for Self-Supervised Learning

Yuge Shi, N Siddharth, Philip Torr, Adam R Kosiorek

Pessimistic Q-Learning for Offline Reinforcement Learning: Towards Optimal Sample Complexity

Laixi Shi, Gen Li, Yuting Wei, Yuxin Chen, Yuejie Chi

Global Optimization of K-Center Clustering

Mingfei Shi, Kaixun Hua, Jiayang Ren, Yankai Cao

Gradient-Free Method for Heavily Constrained Nonconvex Optimization

Wanli Shi, Hongchang Gao, Bin Gu

Deep Network Approximation in Terms of Intrinsic Parameters

Zuowei Shen, Haizhao Yang, Shijun Zhang

Staged Training for Transformer Language Models

Sheng Shen, Pete Walsh, Kurt Keutzer, Jesse Dodge, Matthew Peters

Constrained Optimization with Dynamic Bound-scaling for Effective NLP Backdoor Defense

Guangyu Shen, Yingqi Liu, Guanhong Tao, Qiuling Xu, Zhuo Zhang

Connect, Not Collapse: Explaining Contrastive Learning for Unsupervised Domain Adaptation

Kendrick Shen, Robbie M Jones, Ananya Kumar, Sang Michael Xie, Jeff Z. HaoChen

PDO-s3DCNNs: Partial Differential Operator Based Steerable 3D CNNs

Zhengyang Shen, Tao Hong, Qi She, Jinwen Ma, Zhouchen Lin

Metric-Fair Active Learning

Jie Shen, Nan Cui, Jing Wang

Data Augmentation as Feature Manipulation

Ruoqi Shen, Sébastien Bubeck, Suriya Gunasekar

Instance Dependent Regret Analysis of Kernelized Bandits

Shubhanshu Shekhar, Tara Javidi

DNS: Determinantal Point Process Based Neural Network Sampler for Ensemble Reinforcement Learning

Hassam Sheikh, Kizza Frisbee, Mariano Phielipp

Federated Minimax Optimization: Improved Convergence Analyses and Algorithms

PRANAY SHARMA, Rohan Panda, Gauri Joshi, Pramod Varshney

Content Addressable Memory Without Catastrophic Forgetting by Heteroassociation with a Fixed Scaffold

Sugandha Sharma, Sarthak Chandra, Ila Fiete

A State-Distribution Matching Approach to Non-Episodic Reinforcement Learning

Archit Sharma, Rehaan Ahmad, Chelsea Finn

Translating Robot Skills: Learning Unsupervised Skill Correspondences Across Robots

Tanmay Shankar, Yixin Lin, Aravind Rajeswaran, Vikash Kumar, Stuart Anderson

Utility Theory for Sequential Decision Making

Mehran Shakerinava, Siamak Ravanbakhsh

Selective Regression under Fairness Criteria

Abhin Shah, Yuheng Bu, Joshua K Lee, Subhro Das, Rameswar Panda

Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation

Pier Giuseppe Sessa, Maryam Kamgarpour, Andreas Krause

Reinforcement Learning with Action-Free Pre-Training from Videos

Younggyo Seo, Kimin Lee, Stephen L James, Pieter Abbeel

Neural Tangent Kernel Beyond the Infinite-Width Limit: Effects of Depth and Initialization

Mariia Seleznova, Gitta Kutyniok

Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations

Nabeel Seedat, Fergus Imrie, Alexis Bellot, Zhaozhi Qian, Mihaela van der Schaar

Data-SUITE: Data-centric identification of in-distribution incongruous examples

Nabeel Seedat, Jonathan Crabbe, Mihaela van der Schaar

Symmetric Machine Theory of Mind

Melanie Sclar, Graham Neubig, Yonatan Bisk

Improving Robustness against Real-World and Worst-Case Distribution Shifts through Decision Region Quantification

Leo Schwinn, Leon Bungert, An Nguyen, René Raab, Falk Pulsmeyer

Structure Preserving Neural Networks: A Case Study in the Entropy Closure of the Boltzmann Equation

Steffen Schotthöfer, Tianbai Xiao, Martin Frank, Cory Hauck

Modeling Irregular Time Series with Continuous Recurrent Units

Mona Schirmer, Mazin Eltayeb, Stefan Lessmann, Maja Rudolph

Streaming Inference for Infinite Feature Models

Rylan Schaeffer, Yilun Du, Gabrielle K Liu, Ila Fiete

Linear-Time Gromov Wasserstein Distances using Low Rank Couplings and Costs

meyer scetbon, Gabriel Peyré, Marco Cuturi

An Asymptotic Test for Conditional Independence using Analytic Kernel Embeddings

meyer scetbon, Laurent Meunier, Yaniv Romano

Convergence Rates of Non-Convex Stochastic Gradient Descent Under a Generic Lojasiewicz Condition and Local Smoothness

Kevin Scaman, Cedric Malherbe, Ludovic DOS SANTOS

The Neural Race Reduction: Dynamics of Abstraction in Gated Networks

Andrew Saxe, Shagun Sodhani, Sam Jay Lewallen

Understanding Contrastive Learning Requires Incorporating Inductive Biases

Nikunj Saunshi, Jordan Ash, Surbhi Goel, Dipendra Misra, Cyril Zhang

Re-evaluating Word Mover’s Distance

Ryoma Sato, Makoto Yamada, Hisashi Kashima

PoF: Post-Training of Feature Extractor for Improving Generalization

Ikuro Sato, Yamada Ryota, Masayuki Tanaka, Nakamasa Inoue, Rei Kawakami

LSB: Local Self-Balancing MCMC in Discrete Spaces

Emanuele Sansone

The Algebraic Path Problem for Graph Metrics

Enrique Fita Sanmartı́n, Sebastian Damrich, Fred Hamprecht

FITNESS: (Fine Tune on New and Similar Samples) to detect anomalies in streams with drift and outliers

Abishek Sankararaman, Balakrishnan Narayanaswamy, Vikramank Y Singh, Zhao Song

A Convergence Theory for SVGD in the Population Limit under Talagrand’s Inequality T1

Adil SALIM, Lukang Sun, Peter Richtarik

Optimal Clipping and Magnitude-aware Differentiation for Improved Quantization-aware Training

Charbel Sakr, Steve Dai, Rangha Venkatesan, Brian Zimmer, William Dally