ICML 2022

1,233 papers

Scalable Deep Reinforcement Learning Algorithms for Mean Field Games

Mathieu Lauriere, Sarah Perrin, Sertan Girgin, Paul Muller, Ayush Jain

Bregman Proximal Langevin Monte Carlo via Bregman-Moreau Envelopes

Tim Tsz-Kit Lau, Han Liu

Marginal Tail-Adaptive Normalizing Flows

Mike Laszkiewicz, Johannes Lederer, Asja Fischer

Goal Misgeneralization in Deep Reinforcement Learning

Lauro Langosco Di Langosco, Jack Koch, Lee D Sharkey, Jacob Pfau, David Krueger

Co-training Improves Prompt-based Learning for Large Language Models

Hunter Lang, Monica N Agrawal, Yoon Kim, David Sontag

PINs: Progressive Implicit Networks for Multi-Scale Neural Representations

Zoe Landgraf, Alexander Sorkine Hornung, Ricardo S Cabral

Cooperative Online Learning in Stochastic and Adversarial MDPs

Tal Lancewicki, Aviv Rosenberg, Yishay Mansour

DSTAGNN: Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic Flow Forecasting

Shiyong Lan, Yitong Ma, Weikang Huang, Wenwu Wang, Hongyu Yang

Generative Cooperative Networks for Natural Language Generation

Sylvain Lamprier, Thomas Scialom, Antoine Chaffin, Vincent Claveau, Ewa Kijak

Tell me why! Explanations support learning relational and causal structure

Andrew K Lampinen, Nicholas Roy, Ishita Dasgupta, Stephanie Cy Chan, Allison Tam

Functional Output Regression with Infimal Convolution: Exploring the Huber and $ε$-insensitive Losses

Alex Lambert, Dimitri Bouche, Zoltan Szabo, Florence d’Alché-Buc

Smoothed Adaptive Weighting for Imbalanced Semi-Supervised Learning: Improve Reliability Against Unknown Distribution Data

Zhengfeng Lai, Chao Wang, Henrry Gunawan, Sen-Ching S Cheung, Chen-Nee Chuah

FedScale: Benchmarking Model and System Performance of Federated Learning at Scale

Fan Lai, Yinwei Dai, Sanjay Singapuram, Jiachen Liu, Xiangfeng Zhu

Large Batch Experience Replay

Thibault Lahire, Matthieu Geist, Emmanuel Rachelson

Coordinated Attacks against Contextual Bandits: Fundamental Limits and Defense Mechanisms

Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor

Equivariant Priors for compressed sensing with unknown orientation

Anna Kuzina, Kumar Pratik, Fabio Valerio Massoli, Arash Behboodi

Showing Your Offline Reinforcement Learning Work: Online Evaluation Budget Matters

Vladislav Kurenkov, Sergey Kolesnikov

Balancing Discriminability and Transferability for Source-Free Domain Adaptation

Jogendra Nath Kundu, Akshay R Kulkarni, Suvaansh Bhambri, Deepesh Mehta, Shreyas Anand Kulkarni

ActiveHedge: Hedge meets Active Learning

Bhuvesh Kumar, Jacob D Abernethy, Venkatesh Saligrama

Calibrated and Sharp Uncertainties in Deep Learning via Density Estimation

Volodymyr Kuleshov, Shachi Deshpande

Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions

Heiner Kremer, Jia-Jie Zhu, Krikamol Muandet, Bernhard Schölkopf

Active Nearest Neighbor Regression Through Delaunay Refinement

Alexander Kravberg, Giovanni Luca Marchetti, Vladislav Polianskii, Anastasiia Varava, Florian T. Pokorny

Probabilistic ODE Solutions in Millions of Dimensions

Nicholas Krämer, Nathanael Bosch, Jonathan Schmidt, Philipp Hennig

Reconstructing Nonlinear Dynamical Systems from Multi-Modal Time Series

Daniel Kramer, Philine L Bommer, Carlo Tombolini, Georgia Koppe, Daniel Durstewitz

Exact Learning of Preference Structure: Single-peaked Preferences and Beyond

Sonja Kraiczy, Edith Elkind

Certified Adversarial Robustness Under the Bounded Support Set

Yiwen Kou, Qinyuan Zheng, Yisen Wang

Batch Greenkhorn Algorithm for Entropic-Regularized Multimarginal Optimal Transport: Linear Rate of Convergence and Iteration Complexity

Vladimir R. Kostic, Saverio Salzo, Massimiliano Pontil

Controlling Conditional Language Models without Catastrophic Forgetting

Tomasz Korbak, Hady Elsahar, Germán Kruszewski, Marc Dymetman

Adaptive Data Analysis with Correlated Observations

Aryeh Kontorovich, Menachem Sadigurschi, Uri Stemmer

Simultaneously Learning Stochastic and Adversarial Bandits with General Graph Feedback

Fang Kong, Yichi Zhou, Shuai Li

Partial disentanglement for domain adaptation

Lingjing Kong, Shaoan Xie, Weiran Yao, Yujia Zheng, Guangyi Chen

Markov Chain Monte Carlo for Continuous-Time Switching Dynamical Systems

Lukas Köhs, Bastian Alt, Heinz Koeppl

Transfer Learning In Differential Privacy’s Hybrid-Model

Refael Kohen, Or Sheffet

Revisiting Contrastive Learning through the Lens of Neighborhood Component Analysis: an Integrated Framework

Ching-Yun Ko, Jeet Mohapatra, Sijia Liu, Pin-Yu Chen, Luca Daniel

Exploiting Redundancy: Separable Group Convolutional Networks on Lie Groups

David M. Knigge, David W Romero, Erik J Bekkers

Curriculum Reinforcement Learning via Constrained Optimal Transport

Pascal Klink, Haoyi Yang, Carlo D’Eramo, Jan Peters, Joni Pajarinen

SoQal: Selective Oracle Questioning for Consistency Based Active Learning of Cardiac Signals

Dani Kiyasseh, Tingting Zhu, David A Clifton

Generalizing to New Physical Systems via Context-Informed Dynamics Model

Matthieu Kirchmeyer, Yuan Yin, Jérémie Donà, Nicolas Baskiotis, Alain Rakotomamonjy

Accelerated Gradient Methods for Geodesically Convex Optimization: Tractable Algorithms and Convergence Analysis

Jungbin Kim, Insoon Yang

Rotting Infinitely Many-Armed Bandits

Jung-hun Kim, Milan Vojnovic, Se-Young Yun

Soft Truncation: A Universal Training Technique of Score-based Diffusion Model for High Precision Score Estimation

Dongjun Kim, Seungjae Shin, Kyungwoo Song, Wanmo Kang, Il-chul Moon

Sanity Simulations for Saliency Methods

Joon Sik Kim, Gregory Plumb, Ameet Talwalkar

ViT-NeT: Interpretable Vision Transformers with Neural Tree Decoder

Sangwon Kim, Jaeyeal Nam, Byoung Chul Ko

Fisher SAM: Information Geometry and Sharpness Aware Minimisation

Minyoung Kim, Da Li, Shell X Hu, Timothy Hospedales

Variational On-the-Fly Personalization

Jangho Kim, Jun-Tae Lee, Simyung Chang, Nojun Kwak

Guided-TTS: A Diffusion Model for Text-to-Speech via Classifier Guidance

Heeseung Kim, Sungwon Kim, Sungroh Yoon

Dataset Condensation via Efficient Synthetic-Data Parameterization

Jang-Hyun Kim, Jinuk Kim, Seong Joon Oh, Sangdoo Yun, Hwanjun Song

Learning fair representation with a parametric integral probability metric

Dongha Kim, Kunwoong Kim, Insung Kong, Ilsang Ohn, Yongdai Kim

Multi-Level Branched Regularization for Federated Learning

Jinkyu Kim, Geeho Kim, Bohyung Han

Federated Reinforcement Learning: Linear Speedup Under Markovian Sampling

Sajad Khodadadian, PRANAY SHARMA, Gauri Joshi, Siva Theja Maguluri

Neural Network Poisson Models for Behavioural and Neural Spike Train Data

Moein Khajehnejad, Forough Habibollahi, Richard Nock, Ehsan Arabzadeh, Peter Dayan

A Convergent and Dimension-Independent Min-Max Optimization Algorithm

Vijay Keswani, Oren Mangoubi, Sushant Sachdeva, Nisheeth K. Vishnoi

Secure Quantized Training for Deep Learning

Marcel Keller, Ke Sun

Generating Distributional Adversarial Examples to Evade Statistical Detectors

Yigitcan Kaya, Muhammad Bilal Zafar, Sergul Aydore, Nathalie Rauschmayr, Krishnaram Kenthapadi

Robustness Implies Generalization via Data-Dependent Generalization Bounds

Kenji Kawaguchi, Zhun Deng, Kyle Luh, Jiaoyang Huang

Training OOD Detectors in their Natural Habitats

Julian Katz-Samuels, Julia B Nakhleh, Robert Nowak, Yixuan Li

FOCUS: Familiar Objects in Common and Uncommon Settings

Priyatham Kattakinda, Soheil Feizi

Meta-Learning Hypothesis Spaces for Sequential Decision-making

Parnian Kassraie, Jonas Rothfuss, Andreas Krause

Composing Partial Differential Equations with Physics-Aware Neural Networks

Matthias Karlbauer, Timothy Praditia, Sebastian Otte, Sergey Oladyshkin, Wolfgang Nowak

Simultaneous Graph Signal Clustering and Graph Learning

Abdullah Karaaslanli, Selin Aviyente

Differentially Private Approximate Quantiles

Haim Kaplan, Shachar Schnapp, Uri Stemmer

Forget-free Continual Learning with Winning Subnetworks

Haeyong Kang, Rusty John Lloyd Mina, Sultan Rizky Hikmawan Madjid, Jaehong Yoon, Mark Hasegawa-Johnson

Lyapunov Density Models: Constraining Distribution Shift in Learning-Based Control

Katie Kang, Paula Gradu, Jason J Choi, Michael Janner, Claire Tomlin

Deduplicating Training Data Mitigates Privacy Risks in Language Models

Nikhil Kandpal, Eric Wallace, Colin Raffel

Matching Learned Causal Effects of Neural Networks with Domain Priors

Sai Srinivas Kancheti, Abbavaram Gowtham Reddy, Vineeth N Balasubramanian, Amit Sharma

Comprehensive Analysis of Negative Sampling in Knowledge Graph Representation Learning

Hidetaka Kamigaito, Katsuhiko Hayashi

Improved Rates for Differentially Private Stochastic Convex Optimization with Heavy-Tailed Data

Gautam Kamath, Xingtu Liu, Huanyu Zhang

Doubly Robust Distributionally Robust Off-Policy Evaluation and Learning

Nathan Kallus, Xiaojie Mao, Kaiwen Wang, Zhengyuan Zhou

Stochastic Deep Networks with Linear Competing Units for Model-Agnostic Meta-Learning

Konstantinos Kalais, Sotirios Chatzis

Learning-based Optimisation of Particle Accelerators Under Partial Observability Without Real-World Training

Jan Kaiser, Oliver Stein, Annika Eichler

Flashlight: Enabling Innovation in Tools for Machine Learning

Jacob D Kahn, Vineel Pratap, Tatiana Likhomanenko, Qiantong Xu, Awni Hannun

Sketching Algorithms and Lower Bounds for Ridge Regression

Praneeth Kacham, David Woodruff

Efficient Approximate Inference for Stationary Kernel on Frequency Domain

Yohan Jung, Kyungwoo Song, Jinkyoo Park

On Measuring Causal Contributions via do-interventions

Yonghan Jung, Shiva Kasiviswanathan, Jin Tian, Dominik Janzing, Patrick Bloebaum

Robust alignment of cross-session recordings of neural population activity by behaviour via unsupervised domain adaptation

Justin Jude, Matthew Perich, Lee Miller, Matthias Hennig

Robust Fine-Tuning of Deep Neural Networks with Hessian-based Generalization Guarantees

Haotian Ju, Dongyue Li, Hongyang R Zhang

Choosing Answers in Epsilon-Best-Answer Identification for Linear Bandits

Marc Jourdan, Rémy Degenne

Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations

Jaehyeong Jo, Seul Lee, Sung Ju Hwang

Input-agnostic Certified Group Fairness via Gaussian Parameter Smoothing

Jiayin Jin, Zeru Zhang, Yang Zhou, Lingfei Wu

Supervised Off-Policy Ranking

Yue Jin, Yue Zhang, Tao Qin, Xudong Zhang, Jian Yuan

Accelerated Federated Learning with Decoupled Adaptive Optimization

Jiayin Jin, Jiaxiang Ren, Yang Zhou, Lingjuan Lyu, Ji Liu

Domain Adaptation for Time Series Forecasting via Attention Sharing

Xiaoyong Jin, Youngsuk Park, Danielle Maddix, Hao Wang, Yuyang Wang

The Power of Exploiter: Provable Multi-Agent RL in Large State Spaces

Chi Jin, Qinghua Liu, Tiancheng Yu

Sharpened Quasi-Newton Methods: Faster Superlinear Rate and Larger Local Convergence Neighborhood

Qiujiang Jin, Alec Koppel, Ketan Rajawat, Aryan Mokhtari

Antibody-Antigen Docking and Design via Hierarchical Structure Refinement

Wengong Jin, Dr.Regina Barzilay, Tommi Jaakkola

Optimal Algorithms for Stochastic Multi-Level Compositional Optimization

Wei Jiang, Bokun Wang, Yibo Wang, Lijun Zhang, Tianbao Yang

Subspace Learning for Effective Meta-Learning

Weisen Jiang, James Kwok, Yu Zhang

The Role of Deconfounding in Meta-learning

Yinjie Jiang, Zhengyu Chen, Kun Kuang, Luotian Yuan, Xinhai Ye

Online Learning and Pricing with Reusable Resources: Linear Bandits with Sub-Exponential Rewards

Huiwen Jia, Cong Shi, Siqian Shen

Translatotron 2: High-quality direct speech-to-speech translation with voice preservation

Ye Jia, Michelle Tadmor Ramanovich, Tal Remez, Roi Pomerantz

Improving Policy Optimization with Generalist-Specialist Learning

Zhiwei Jia, Xuanlin Li, Zhan Ling, Shuang Liu, Yiran Wu

Agnostic Learnability of Halfspaces via Logistic Loss

Ziwei Ji, Kwangjun Ahn, Pranjal Awasthi, Satyen Kale, Stefani Karp

An Exact Symbolic Reduction of Linear Smart Predict+Optimize to Mixed Integer Linear Programming

Jihwan Jeong, Parth Jaggi, Andrew Butler, Scott Sanner

MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay Buffer

Jeewon Jeon, Woojun Kim, Whiyoung Jung, Youngchul Sung

Towards understanding how momentum improves generalization in deep learning

Samy Jelassi, Yuanzhi Li

Mitigating Modality Collapse in Multimodal VAEs via Impartial Optimization

Adrián Javaloy, Maryam Meghdadi, Isabel Valera

HyperImpute: Generalized Iterative Imputation with Automatic Model Selection

Daniel Jarrett, Bogdan C Cebere, Tennison Liu, Alicia Curth, Mihaela van der Schaar

Planning with Diffusion for Flexible Behavior Synthesis

Michael Janner, Yilun Du, Joshua Tenenbaum, Sergey Levine

Surrogate Likelihoods for Variational Annealed Importance Sampling

Martin Jankowiak, Du Phan

Sequential Covariate Shift Detection Using Classifier Two-Sample Tests

Sooyong Jang, Sangdon Park, Insup Lee, Osbert Bastani