ICML 2022

1,233 papers

How to Fill the Optimum Set? Population Gradient Descent with Harmless Diversity

Chengyue Gong, Lemeng Wu, Qiang Liu

RankSim: Ranking Similarity Regularization for Deep Imbalanced Regression

Yu Gong, Greg Mori, Fred Tung

It’s Raw! Audio Generation with State-Space Models

Karan Goel, Albert Gu, Chris Donahue, Christopher Re

Neuro-Symbolic Hierarchical Rule Induction

Claire Glanois, Zhaohui Jiang, Xuening Feng, Paul Weng, Matthieu Zimmer

A Joint Exponential Mechanism For Differentially Private Top-k

Jennifer Gillenwater, Matthew Joseph, Andres Munoz, Monica Ribero Diaz

SCHA-VAE: Hierarchical Context Aggregation for Few-Shot Generation

Giorgio Giannone, Ole Winther

Breaking the √T Barrier: Instance-Independent Logarithmic Regret in Stochastic Contextual Linear Bandits

Avishek Ghosh, Abishek Sankararaman

Offline RL Policies Should Be Trained to be Adaptive

Dibya Ghosh, Anurag Ajay, Pulkit Agrawal, Sergey Levine

Plug-In Inversion: Model-Agnostic Inversion for Vision with Data Augmentations

Amin Ghiasi, Hamid Kazemi, Steven Reich, Chen Zhu, Micah Goldblum

Faster Privacy Accounting via Evolving Discretization

Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi

Blocks Assemble! Learning to Assemble with Large-Scale Structured Reinforcement Learning

Seyed Kamyar Seyed Ghasemipour, Satoshi Kataoka, Byron David, Daniel Freeman, Shixiang Shane Gu

A Regret Minimization Approach to Multi-Agent Control

Udaya Ghai, Udari Madhushani, Naomi Leonard, Elad Hazan

Equivariance versus Augmentation for Spherical Images

Jan Gerken, Oscar Carlsson, Hampus Linander, Fredrik Ohlsson, Christoffer Petersson

Online Learning for Min Sum Set Cover and Pandora’s Box

Evangelia Gergatsouli, Christos Tzamos

Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning

Martin Genzel, Ingo Gühring, Jan Macdonald, Maximilian März

Achieving Minimax Rates in Pool-Based Batch Active Learning

Claudio Gentile, Zhilei Wang, Tong Zhang

Inducing Causal Structure for Interpretable Neural Networks

Atticus Geiger, Zhengxuan Wu, Hanson Lu, Josh Rozner, Elisa Kreiss

Variational Inference with Locally Enhanced Bounds for Hierarchical Models

Tomas Geffner, Justin Domke

UniRank: Unimodal Bandit Algorithms for Online Ranking

Camille-Sovanneary Gauthier, Romaric Gaudel, Elisa Fromont

A Functional Information Perspective on Model Interpretation

Itai Gat, Nitay Calderon, Roi Reichart, Tamir Hazan

The power of first-order smooth optimization for black-box non-smooth problems

Alexander Gasnikov, Anton Novitskii, Vasilii Novitskii, Farshed Abdukhakimov, Dmitry Kamzolov

PAGE-PG: A Simple and Loopless Variance-Reduced Policy Gradient Method with Probabilistic Gradient Estimation

Matilde Gargiani, Andrea Zanelli, Andrea Martinelli, Tyler Summers, John Lygeros

Stochastic smoothing of the top-K calibrated hinge loss for deep imbalanced classification

Camille Garcin, Maximilien Servajean, Alexis Joly, Joseph Salmon

Learning to Incorporate Texture Saliency Adaptive Attention to Image Cartoonization

Xiang Gao, Yuqi Zhang, YingJie Tian

Value Function based Difference-of-Convex Algorithm for Bilevel Hyperparameter Selection Problems

Lucy L Gao, Jane Ye, Haian Yin, Shangzhi Zeng, Jin Zhang

Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack

Ruize Gao, Jiongxiao Wang, Kaiwen Zhou, Feng Liu, Binghui Xie

Lazy Estimation of Variable Importance for Large Neural Networks

Yue Gao, Abby Stevens, Garvesh Raskutti, Rebecca Willett

Rethinking Image-Scaling Attacks: The Interplay Between Vulnerabilities in Machine Learning Systems

Yue Gao, Ilia Shumailov, Kassem Fawaz

Generalizing Gaussian Smoothing for Random Search

Katelyn Gao, Ozan Sener

On the Equivalence Between Temporal and Static Equivariant Graph Representations

Jianfei Gao, Bruno Ribeiro

Deep Reference Priors: What is the best way to pretrain a model?

Yansong Gao, Rahul Ramesh, Pratik Chaudhari

On the Convergence of Local Stochastic Compositional Gradient Descent with Momentum

Hongchang Gao, Junyi Li, Heng Huang

Loss Function Learning for Domain Generalization by Implicit Gradient

Boyan Gao, Henry Gouk, Yongxin Yang, Timothy Hospedales

IDYNO: Learning Nonparametric DAGs from Interventional Dynamic Data

Tian Gao, Debarun Bhattacharjya, Elliot Nelson, Miao Liu, Yue Yu

The Complexity of k-Means Clustering when Little is Known

Robert Ganian, Thekla Hamm, Viktoriia Korchemna, Karolina Okrasa, Kirill Simonov

Robin Hood and Matthew Effects: Differential Privacy Has Disparate Impact on Synthetic Data

Georgi Ganev, Bristena Oprisanu, Emiliano De Cristofaro

Why Should I Trust You, Bellman? The Bellman Error is a Poor Replacement for Value Error

Scott Fujimoto, David Meger, Doina Precup, Ofir Nachum, Shixiang Shane Gu

p-Laplacian Based Graph Neural Networks

Guoji Fu, Peilin Zhao, Yatao Bian

Revisiting Some Common Practices in Cooperative Multi-Agent Reinforcement Learning

Wei Fu, Chao Yu, Zelai Xu, Jiaqi Yang, Yi Wu

DepthShrinker: A New Compression Paradigm Towards Boosting Real-Hardware Efficiency of Compact Neural Networks

Yonggan Fu, Haichuan Yang, Jiayi Yuan, Meng Li, Cheng Wan

Greedy when Sure and Conservative when Uncertain about the Opponents

Haobo Fu, Ye Tian, Hongxiang Yu, Weiming Liu, Shuang Wu

Scaling Structured Inference with Randomization

Yao Fu, John Cunningham, Mirella Lapata

(Non-)Convergence Results for Predictive Coding Networks

Simon Frieder, Thomas Lukasiewicz

Bregman Neural Networks

Jordan Frecon, Gilles Gasso, Massimiliano Pontil, Saverio Salzo

Revisiting the Effects of Stochasticity for Hamiltonian Samplers

Giulio Franzese, Dimitrios Milios, Maurizio Filippone, Pietro Michiardi

SPDY: Accurate Pruning with Speedup Guarantees

Elias Frantar, Dan Alistarh

Extracting Latent State Representations with Linear Dynamics from Rich Observations

Abraham Frandsen, Rong Ge, Holden Lee

A Neural Tangent Kernel Perspective of GANs

Jean-Yves Franceschi, Emmanuel de Bézenac, Ibrahim Ayed, Mickaël Chen, Sylvain Lamprier

Label Ranking through Nonparametric Regression

Dimitris Fotakis, Alkis Kalavasis, Eleni Psaroudaki

Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness

Adam Foster, Arpi Vezer, Craig A. Glastonbury, Paidi Creed, Samer Abujudeh

Fast Relative Entropy Coding with A* coding

Gergely Flamich, Stratis Markou, José Miguel Hernández-Lobato

Fast Population-Based Reinforcement Learning on a Single Machine

arthur flajolet, Claire Bizon Monroc, Karim Beguir, Thomas PIERROT

Conformal Prediction Sets with Limited False Positives

Adam Fisch, Tal Schuster, Tommi Jaakkola, Dr.Regina Barzilay

Coordinated Double Machine Learning

Nitai Fingerhut, Matteo Sesia, Yaniv Romano

Model-Value Inconsistency as a Signal for Epistemic Uncertainty

Angelos Filos, Eszter Vértes, Zita Marinho, Gregory Farquhar, Diana Borsa

A Resilient Distributed Boosting Algorithm

Yuval Filmus, Idan Mehalel, Shay Moran

Principled Knowledge Extrapolation with GANs

Ruili Feng, Jie Xiao, Kecheng Zheng, Deli Zhao, Jingren Zhou

An Intriguing Property of Geophysics Inversion

Yinan Feng, Yinpeng Chen, Shihang Feng, Peng Jin, Zicheng Liu

Private frequency estimation via projective geometry

Vitaly Feldman, Jelani Nelson, Huy Nguyen, Kunal Talwar

Cascaded Gaps: Towards Logarithmic Regret for Risk-Sensitive Reinforcement Learning

Yingjie Fei, Ruitu Xu

Matching Structure for Dual Learning

Hao Fei, Shengqiong Wu, Yafeng Ren, Meishan Zhang

Local Linear Convergence of Douglas-Rachford for Linear Programming: a Probabilistic Analysis

Oisin Faust, Hamza Fawzi

Kernelized Multiplicative Weights for 0/1-Polyhedral Games: Bridging the Gap Between Learning in Extensive-Form and Normal-Form Games

Gabriele Farina, Chung-Wei Lee, Haipeng Luo, Christian Kroer

Investigating Generalization by Controlling Normalized Margin

Alexander R Farhang, Jeremy D Bernstein, Kushal Tirumala, Yang Liu, Yisong Yue

An Equivalence Between Data Poisoning and Byzantine Gradient Attacks

Sadegh Farhadkhani, Rachid Guerraoui, Lê Nguyên Hoang, Oscar Villemaud

Byzantine Machine Learning Made Easy By Resilient Averaging of Momentums

Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, John Stephan

Bayesian Continuous-Time Tucker Decomposition

Shikai Fang, Akil Narayan, Robert Kirby, Shandian Zhe

Data Determines Distributional Robustness in Contrastive Language Image Pre-training (CLIP)

Alex Fang, Gabriel Ilharco, Mitchell Wortsman, Yuhao Wan, Vaishaal Shankar

Variational Wasserstein gradient flow

Jiaojiao Fan, Qinsheng Zhang, Amirhossein Taghvaei, Yongxin Chen

Generalized Data Distribution Iteration

Jiajun Fan, Changnan Xiao

DRIBO: Robust Deep Reinforcement Learning via Multi-View Information Bottleneck

Jiameng Fan, Wenchao Li

Training Discrete Deep Generative Models via Gapped Straight-Through Estimator

Ting-Han Fan, Ta-Chung Chi, Alexander I. Rudnicky, Peter J Ramadge

Variational Sparse Coding with Learned Thresholding

Kion Fallah, Christopher J Rozell

Head2Toe: Utilizing Intermediate Representations for Better Transfer Learning

Utku Evci, Vincent Dumoulin, Hugo Larochelle, Michael C Mozer

Understanding Dataset Difficulty with V-Usable Information

Kawin Ethayarajh, Yejin Choi, Swabha Swayamdipta

Towards Scaling Difference Target Propagation by Learning Backprop Targets

Maxence M Ernoult, Fabrice Normandin, Abhinav Moudgil, Sean Spinney, Eugene Belilovsky

Streaming Algorithm for Monotone k-Submodular Maximization with Cardinality Constraints

Alina Ene, Huy Nguyen

For Learning in Symmetric Teams, Local Optima are Global Nash Equilibria

Scott Emmons, Caspar Oesterheld, Andrew Critch, Vincent Conitzer, Stuart Russell

Discrete Tree Flows via Tree-Structured Permutations

Mai Elkady, Jim Lim, David I. Inouye

pathGCN: Learning General Graph Spatial Operators from Paths

Moshe Eliasof, Eldad Haber, Eran Treister

FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning

Anis Elgabli, Chaouki Ben Issaid, Amrit Singh Bedi, Ketan Rajawat, Mehdi Bennis

Sparsity in Partially Controllable Linear Systems

Yonathan Efroni, Sham Kakade, Akshay Krishnamurthy, Cyril Zhang

Provable Reinforcement Learning with a Short-Term Memory

Yonathan Efroni, Chi Jin, Akshay Krishnamurthy, Sobhan Miryoosefi

Inductive Biases and Variable Creation in Self-Attention Mechanisms

Benjamin L Edelman, Surbhi Goel, Sham Kakade, Cyril Zhang

LIMO: Latent Inceptionism for Targeted Molecule Generation

Peter Eckmann, Kunyang Sun, Bo Zhao, Mudong Feng, Michael Gilson

On the Difficulty of Defending Self-Supervised Learning against Model Extraction

Adam Dziedzic, Nikita Dhawan, Muhammad Ahmad Kaleem, Jonas Guan, Nicolas Papernot

Robust Counterfactual Explanations for Tree-Based Ensembles

Sanghamitra Dutta, Jason Long, Saumitra Mishra, Cecilia Tilli, Daniele Magazzeni

Efficient Low Rank Convex Bounds for Pairwise Discrete Graphical Models

Valentin Durante, George Katsirelos, Thomas Schiex

From data to functa: Your data point is a function and you can treat it like one

Emilien Dupont, Hyunjik Kim, S. M. Ali Eslami, Danilo Jimenez Rezende, Dan Rosenbaum

Deletion Robust Submodular Maximization over Matroids

Paul Duetting, Federico Fusco, Silvio Lattanzi, Ashkan Norouzi-Fard, Morteza Zadimoghaddam

Bayesian Deep Embedding Topic Meta-Learner

Zhibin Duan, Yishi Xu, Jianqiao Sun, Bo Chen, wenchao chen

Parametric Visual Program Induction with Function Modularization

Xuguang Duan, Xin Wang, Ziwei Zhang, Wenwu Zhu

Augment with Care: Contrastive Learning for Combinatorial Problems

Haonan Duan, Pashootan Vaezipoor, Max B Paulus, Yangjun Ruan, Chris Maddison

A Context-Integrated Transformer-Based Neural Network for Auction Design

Zhijian Duan, Jingwu Tang, Yutong Yin, Zhe Feng, Xiang Yan

SE(3) Equivariant Graph Neural Networks with Complete Local Frames

weitao Du, He Zhang, Yuanqi Du, Qi Meng, Wei Chen

Learning Iterative Reasoning through Energy Minimization

Yilun Du, Shuang Li, Joshua Tenenbaum, Igor Mordatch

GLaM: Efficient Scaling of Language Models with Mixture-of-Experts

Nan Du, Yanping Huang, Andrew M Dai, Simon Tong, Dmitry Lepikhin

Bayesian Imitation Learning for End-to-End Mobile Manipulation

Yuqing Du, Daniel Ho, Alex Alemi, Eric Jang, Mohi Khansari

Branching Reinforcement Learning

Yihan Du, Wei Chen

TACTiS: Transformer-Attentional Copulas for Time Series

Alexandre Drouin, Étienne Marcotte, Nicolas Chapados