ICML 2022

1,233 papers

Learning Domain Adaptive Object Detection with Probabilistic Teacher

Meilin Chen, Weijie Chen, Shicai Yang, Jie Song, Xinchao Wang

Coarsening the Granularity: Towards Structurally Sparse Lottery Tickets

Tianlong Chen, Xuxi Chen, Xiaolong Ma, Yanzhi Wang, Zhangyang Wang

Nyström Kernel Mean Embeddings

Antoine Chatalic, Nicolas Schreuder, Lorenzo Rosasco, Alessandro Rudi

Sample Efficient Learning of Predictors that Complement Humans

Mohammad-Amin Charusaie, Hussein Mozannar, David Sontag, Samira Samadi

Learning Bellman Complete Representations for Offline Policy Evaluation

Jonathan Chang, Kaiwen Wang, Nathan Kallus, Wen Sun

Style Equalization: Unsupervised Learning of Controllable Generative Sequence Models

Jen-Hao Rick Chang, Ashish Shrivastava, Hema Koppula, Xiaoshuai Zhang, Oncel Tuzel

Revisiting Label Smoothing and Knowledge Distillation Compatibility: What was Missing?

Keshigeyan Chandrasegaran, Ngoc-Trung Tran, Yunqing Zhao, Ngai-Man Cheung

UNIREX: A Unified Learning Framework for Language Model Rationale Extraction

Aaron Chan, Maziar Sanjabi, Lambert Mathias, Liang Tan, Shaoliang Nie

Fairness with Adaptive Weights

Junyi Chai, Xiaoqian Wang

Robust Imitation Learning against Variations in Environment Dynamics

Jongseong Chae, Seungyul Han, Whiyoung Jung, Myungsik Cho, Sungho Choi

Accelerated, Optimal and Parallel: Some results on model-based stochastic optimization

Karan Chadha, Gary Cheng, John Duchi

Stabilizing Off-Policy Deep Reinforcement Learning from Pixels

Edoardo Cetin, Philip J Ball, Stephen Roberts, Oya Celiktutan

Online Learning with Knapsacks: the Best of Both Worlds

Matteo Castiglioni, Andrea Celli, Christian Kroer

Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Data

Timothy J Castiglia, Anirban Das, Shiqiang Wang, Stacy Patterson

The Infinite Contextual Graph Markov Model

Daniele Castellana, Federico Errica, Davide Bacciu, Alessio Micheli

YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for Everyone

Edresson Casanova, Julian Weber, Christopher D Shulby, Arnaldo Candido Junior, Eren Gölge

Estimating and Penalizing Induced Preference Shifts in Recommender Systems

Micah D Carroll, Anca Dragan, Stuart Russell, Dylan Hadfield-Menell

RECAPP: Crafting a More Efficient Catalyst for Convex Optimization

Yair Carmon, Arun Jambulapati, Yujia Jin, Aaron Sidford

A Marriage between Adversarial Team Games and 2-player Games: Enabling Abstractions, No-regret Learning, and Subgame Solving

Luca Carminati, Federico Cacciamani, Marco Ciccone, Nicola Gatti

Burst-Dependent Plasticity and Dendritic Amplification Support Target-Based Learning and Hierarchical Imitation Learning

Cristiano Capone, Cosimo Lupo, Paolo Muratore, Pier Stanislao Paolucci

Gaussian Process Uniform Error Bounds with Unknown Hyperparameters for Safety-Critical Applications

Alexandre Capone, Armin Lederer, Sandra Hirche

A Model-Agnostic Randomized Learning Framework based on Random Hypothesis Subspace Sampling

Yiting Cao, Chao Lan

Théophile Cantelobre, Carlo Ciliberto, Benjamin Guedj, Alessandro Rudi

Adaptive Gaussian Process Change Point Detection

Edoardo Caldarelli, Philippe Wenk, Stefan Bauer, Andreas Krause

Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times

Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco

Reinforcement Learning from Partial Observation: Linear Function Approximation with Provable Sample Efficiency

Qi Cai, Zhuoran Yang, Zhaoran Wang

Convergence of Invariant Graph Networks

Chen Cai, Yusu Wang

Near-Optimal Algorithms for Autonomous Exploration and Multi-Goal Stochastic Shortest Path

Haoyuan Cai, Tengyu Ma, Simon Du

Convolutional and Residual Networks Provably Contain Lottery Tickets

Rebekka Burkholz

Interactive Inverse Reinforcement Learning for Cooperative Games

Thomas Kleine Büning, Anne-Marie George, Christos Dimitrakakis

IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages

Emanuele Bugliarello, Fangyu Liu, Jonas Pfeiffer, Siva Reddy, Desmond Elliott

Causal structure-based root cause analysis of outliers

Kailash Budhathoki, Lenon Minorics, Patrick Bloebaum, Dominik Janzing

Efficient Learning of CNNs using Patch Based Features

Alon Brutzkus, Amir Globerson, Eran Malach, Alon Regev Netser, Shai Shalev-Schwartz

Learning to Predict Graphs with Fused Gromov-Wasserstein Barycenters

Luc Brogat-Motte, Rémi Flamary, Céline Brouard, Juho Rousu, Florence d’Alché-Buc

Tractable Dendritic RNNs for Reconstructing Nonlinear Dynamical Systems

Manuel Brenner, Florian Hess, Jonas M Mikhaeil, Leonard F Bereska, Zahra Monfared

An iterative clustering algorithm for the Contextual Stochastic Block Model with optimality guarantees

Guillaume Braun, Hemant Tyagi, Christophe Biernacki

Lie Point Symmetry Data Augmentation for Neural PDE Solvers

Johannes Brandstetter, Max Welling, Daniel E Worrall

Improving Language Models by Retrieving from Trillions of Tokens

Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford

How to Train Your Wide Neural Network Without Backprop: An Input-Weight Alignment Perspective

Akhilan Boopathy, Ila Fiete

Generalized Results for the Existence and Consistency of the MLE in the Bradley-Terry-Luce Model

Heejong Bong, Alessandro Rinaldo

Lagrangian Method for Q-Function Learning (with Applications to Machine Translation)

Huang Bojun

Optimizing Sequential Experimental Design with Deep Reinforcement Learning

Tom Blau, Edwin V. Bonilla, Iadine Chades, Amir Dezfouli

Guy Blanc, Jane Lange, Ali Malik, Li-Yang Tan

A query-optimal algorithm for finding counterfactuals

Guy Blanc, Caleb Koch, Jane Lange, Li-Yang Tan

Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core Quantities

Julian Bitterwolf, Alexander Meinke, Maximilian Augustin, Matthias Hein

Scalable Spike-and-Slab

Niloy Biswas, Lester Mackey, Xiao-Li Meng

Structure-preserving GANs

Jeremiah Birrell, Markos Katsoulakis, Luc Rey-Bellet, Wei Zhu

Non-Vacuous Generalisation Bounds for Shallow Neural Networks

Felix Biggs, Benjamin Guedj

Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning

Alberto Bietti, Chen-Yu Wei, Miroslav Dudík, John Langford, Steven Wu

Nearly Optimal Catoni’s M-estimator for Infinite Variance

Sujay Bhatt, Guanhua Fang, Ping Li, Gennady Samorodnitsky

Minimax M-estimation under Adversarial Contamination

Sujay Bhatt, Guanhua Fang, Ping Li, Gennady Samorodnitsky

Approximate Bayesian Computation with Domain Expert in the Loop

Ayush Bharti, Louis Filstroff, Samuel Kaski

Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification

Peter Bevan, Amir Atapour-Abarghouei

Safe Learning in Tree-Form Sequential Decision Making: Handling Hard and Soft Constraints

Martino Bernasconi, Federico Cacciamani, Matteo Castiglioni, Alberto Marchesi, Nicola Gatti

Gradient Descent on Neurons and its Link to Approximate Second-order Optimization

Frederik Benzing

Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes

Gregory Benton, Wesley Maddox, Andrew Gordon Wilson

Neural Inverse Kinematic

Raphael Bensadoun, Shir Gur, Nitsan Blau, Lior Wolf

Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Models

Viktor Bengs, Aadirupa Saha, Eyke Hüllermeier

Matching Normalizing Flows and Probability Paths on Manifolds

Heli Ben-Hamu, Samuel Cohen, Joey Bose, Brandon Amos, Maximillian Nickel

Imitation Learning by Estimating Expertise of Demonstrators

Mark Beliaev, Andy Shih, Stefano Ermon, Dorsa Sadigh, Ramtin Pedarsani

On the Hidden Biases of Policy Mirror Ascent in Continuous Action Spaces

Amrit Singh Bedi, Souradip Chakraborty, Anjaly Parayil, Brian M Sadler, Pratap Tokekar

Information Discrepancy in Strategic Learning

Yahav Bechavod, Chara Podimata, Steven Wu, Juba Ziani

Fictitious Play and Best-Response Dynamics in Identical Interest and Zero-Sum Stochastic Games

Lucas Baudin, Rida Laraki

Neural Fisher Discriminant Analysis: Optimal Neural Network Embeddings in Polynomial Time

Burak Bartan, Mert Pilanci

Sparse Mixed Linear Regression with Guarantees: Taming an Intractable Problem with Invex Relaxation

Adarsh Barik, Jean Honorio

Representation Topology Divergence: A Method for Comparing Neural Network Representations.

Serguei Barannikov, Ilya Trofimov, Nikita Balabin, Evgeny Burnaev

On the Surrogate Gap between Contrastive and Supervised Losses

Han Bao, Yoshihiro Nagano, Kento Nozawa

Estimating the Optimal Covariance with Imperfect Mean in Diffusion Probabilistic Models

Fan Bao, Chongxuan Li, Jiacheng Sun, Jun Zhu, Bo Zhang

Generative Modeling for Multi-task Visual Learning

Zhipeng Bao, Martial Hebert, Yu-Xiong Wang

Fast Composite Optimization and Statistical Recovery in Federated Learning

Yajie Bao, Michael Crawshaw, Shan Luo, Mingrui Liu

Learning Stable Classifiers by Transferring Unstable Features

Yujia Bao, Shiyu Chang, Dr.Regina Barzilay

Data Scaling Laws in NMT: The Effect of Noise and Architecture

Yamini Bansal, Behrooz Ghorbani, Ankush Garg, Biao Zhang, Colin Cherry

Certified Neural Network Watermarks with Randomized Smoothing

Arpit Bansal, Ping-yeh Chiang, Michael J Curry, Rajiv Jain, Curtis Wigington

Stability Based Generalization Bounds for Exponential Family Langevin Dynamics

Arindam Banerjee, Tiancong Chen, Xinyan Li, Yingxue Zhou

A$^3$T: Alignment-Aware Acoustic and Text Pretraining for Speech Synthesis and Editing

He Bai, Renjie Zheng, Junkun Chen, Mingbo Ma, Xintong Li

Gaussian Mixture Variational Autoencoder with Contrastive Learning for Multi-Label Classification

Junwen Bai, Shufeng Kong, Carla P Gomes

Near-Optimal Learning of Extensive-Form Games with Imperfect Information

Yu Bai, Chi Jin, Song Mei, Tiancheng Yu

A Hierarchical Transitive-Aligned Graph Kernel for Un-attributed Graphs

Lu Bai, Lixin Cui, Hancock Edwin

End-to-End Balancing for Causal Continuous Treatment-Effect Estimation

Taha Bahadori, Eric Tchetgen Tchetgen, David Heckerman

data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language

Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu

From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative Model

HeeSun Bae, Seungjae Shin, Byeonghu Na, JoonHo Jang, Kyungwoo Song

ASAP.SGD: Instance-based Adaptiveness to Staleness in Asynchronous SGD

Karl Bäckström, Marina Papatriantafilou, Philippas Tsigas

How Tempering Fixes Data Augmentation in Bayesian Neural Networks

Gregor Bachmann, Lorenzo Noci, Thomas Hofmann

Near-optimal rate of consistency for linear models with missing values

Alexis Ayme, Claire Boyer, Aymeric Dieuleveut, Erwan Scornet

Proving Theorems using Incremental Learning and Hindsight Experience Replay

Eser Aygün, Ankit Anand, Laurent Orseau, Xavier Glorot, Stephen M Mcaleer

Iterative Hard Thresholding with Adaptive Regularization: Sparser Solutions Without Sacrificing Runtime

Kyriakos Axiotis, Maxim Sviridenko

H-Consistency Bounds for Surrogate Loss Minimizers

Pranjal Awasthi, Anqi Mao, Mehryar Mohri, Yutao Zhong

Do More Negative Samples Necessarily Hurt In Contrastive Learning?

Pranjal Awasthi, Nishanth Dikkala, Pritish Kamath

Congested Bandits: Optimal Routing via Short-term Resets

Pranjal Awasthi, Kush Bhatia, Sreenivas Gollapudi, Kostas Kollias

Asymptotically-Optimal Gaussian Bandits with Side Observations

Alexia Atsidakou, Orestis Papadigenopoulos, Constantine Caramanis, Sujay Sanghavi, Sanjay Shakkottai

Optimal Algorithms for Mean Estimation under Local Differential Privacy

Hilal Asi, Vitaly Feldman, Kunal Talwar

Private optimization in the interpolation regime: faster rates and hardness results

Hilal Asi, Karan Chadha, Gary Cheng, John Duchi

Understanding Gradient Descent on the Edge of Stability in Deep Learning

Sanjeev Arora, Zhiyuan Li, Abhishek Panigrahi

Gradient Based Clustering

Aleksandar Armacki, Dragana Bajovic, Dusan Jakovetic, Soummya Kar

Thresholded Lasso Bandit

Kaito Ariu, Kenshi Abe, Alexandre Proutiere

VariGrow: Variational Architecture Growing for Task-Agnostic Continual Learning based on Bayesian Novelty

Randy Ardywibowo, Zepeng Huo, Zhangyang Wang, Bobak J Mortazavi, Shuai Huang

Online Balanced Experimental Design

David Arbour, Drew Dimmery, Tung Mai, Anup Rao

EAT-C: Environment-Adversarial sub-Task Curriculum for Efficient Reinforcement Learning

Shuang Ao, Tianyi Zhou, Jing Jiang, Guodong Long, Xuan Song

Adapting the Linearised Laplace Model Evidence for Modern Deep Learning

Javier Antoran, David Janz, James U Allingham, Erik Daxberger, Riccardo Rb Barbano

UnderGrad: A Universal Black-Box Optimization Method with Almost Dimension-Free Convergence Rate Guarantees

Kimon Antonakopoulos, Dong Quan Vu, Volkan Cevher, Kfir Levy, Panayotis Mertikopoulos