ICML 2022

1,233 papers

Causal Conceptions of Fairness and their Consequences

Hamed Nilforoshan, Johann D Gaebler, Ravi Shroff, Sharad Goel

The Primacy Bias in Deep Reinforcement Learning

Evgenii Nikishin, Max Schwarzer, Pierluca D’Oro, Pierre-Luc Bacon, Aaron Courville

Diffusion Models for Adversarial Purification

Weili Nie, Brandon Guo, Yujia Huang, Chaowei Xiao, Arash Vahdat

GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models

Alexander Quinn Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin

Optimal Estimation of Policy Gradient via Double Fitted Iteration

Chengzhuo Ni, Ruiqi Zhang, Xiang Ji, Xuezhou Zhang, Mengdi Wang

Recurrent Model-Free RL Can Be a Strong Baseline for Many POMDPs

Tianwei Ni, Benjamin Eysenbach, Ruslan Salakhutdinov

Improving Mini-batch Optimal Transport via Partial Transportation

Khai Nguyen, Dang Nguyen, The-Anh Vu-Le, Tung Pham, nhật Hồ

On Transportation of Mini-batches: A Hierarchical Approach

Khai Nguyen, Dang Nguyen, Quoc Dinh Nguyen, Tung Pham, Hung Bui

Improving Transformers with Probabilistic Attention Keys

Tam Minh Nguyen, Tan Minh Nguyen, Dung D. D. Le, Duy Khuong Nguyen, Viet-Anh Tran

Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling

Tung Nguyen, Aditya Grover

A Framework for Learning to Request Rich and Contextually Useful Information from Humans

Khanh X Nguyen, Yonatan Bisk, Hal Daume III

Improved Regret for Differentially Private Exploration in Linear MDP

Dung Daniel T Ngo, Giuseppe Vietri, Steven Wu

Sublinear-Time Clustering Oracle for Signed Graphs

Stefan Neumann, Pan Peng

Discovering Generalizable Spatial Goal Representations via Graph-based Active Reward Learning

Aviv Netanyahu, Tianmin Shu, Joshua Tenenbaum, Pulkit Agrawal

Stable Conformal Prediction Sets

Eugene Ndiaye

Variational Inference for Infinitely Deep Neural Networks

Achille Nazaret, David Blei

Multi-Task Learning as a Bargaining Game

Aviv Navon, Aviv Shamsian, Idan Achituve, Haggai Maron, Kenji Kawaguchi

Fast Aquatic Swimmer Optimization with Differentiable Projective Dynamics and Neural Network Hydrodynamic Models

Elvis Nava, John Z Zhang, Mike Yan Michelis, Tao Du, Pingchuan Ma

Tight and Robust Private Mean Estimation with Few Users

Shyam Narayanan, Vahab Mirrokni, Hossein Esfandiari

Measuring Representational Robustness of Neural Networks Through Shared Invariances

Vedant Nanda, Till Speicher, Camila Kolling, John P Dickerson, Krishna Gummadi

Improving Ensemble Distillation With Weight Averaging and Diversifying Perturbation

Giung Nam, Hyungi Lee, Byeongho Heo, Juho Lee

Strategic Representation

Vineet Nair, Ganesh Ghalme, Inbal Talgam-Cohen, Nir Rosenfeld

Overcoming Oscillations in Quantization-Aware Training

Markus Nagel, Marios Fournarakis, Yelysei Bondarenko, Tijmen Blankevoort

DNNR: Differential Nearest Neighbors Regression

Youssef Nader, Leon Sixt, Tim Landgraf

Implicit Bias of the Step Size in Linear Diagonal Neural Networks

Mor Shpigel Nacson, Kavya Ravichandran, Nathan Srebro, Daniel Soudry

AutoSNN: Towards Energy-Efficient Spiking Neural Networks

Byunggook Na, Jisoo Mok, Seongsik Park, Dongjin Lee, Hyeokjun Choe

PAC-Net: A Model Pruning Approach to Inductive Transfer Learning

Sanghoon Myung, In Huh, Wonik Jang, Jae Myung Choe, Jisu Ryu

The Importance of Non-Markovianity in Maximum State Entropy Exploration

Mirco Mutti, Riccardo De Santi, Marcello Restelli

Universal and data-adaptive algorithms for model selection in linear contextual bandits

Vidya K Muthukumar, Akshay Krishnamurthy

On the Generalization Analysis of Adversarial Learning

Waleed Mustafa, Yunwen Lei, Marius Kloft

Constants Matter: The Performance Gains of Active Learning

Stephen O Mussmann, Sanjoy Dasgupta

Bounding the Width of Neural Networks via Coupled Initialization A Worst Case Analysis

Alexander Munteanu, Simon Omlor, Zhao Song, David Woodruff

Generalized Beliefs for Cooperative AI

Darius Muglich, Luisa M Zintgraf, Christian A Schroeder De Witt, Shimon Whiteson, Jakob Foerster

CtrlFormer: Learning Transferable State Representation for Visual Control via Transformer

Yao Mark Mu, Shoufa Chen, Mingyu Ding, Jianyu Chen, Runjian Chen

SpeqNets: Sparsity-aware permutation-equivariant graph networks

Christopher Morris, Gaurav Rattan, Sandra Kiefer, Siamak Ravanbakhsh

Rethinking Fano’s Inequality in Ensemble Learning

Terufumi Morishita, Gaku Morio, Shota Horiguchi, Hiroaki Ozaki, Nobuo Nukaga

Power-Law Escape Rate of SGD

Takashi Mori, Liu Ziyin, Kangqiao Liu, Masahito Ueda

Feature and Parameter Selection in Stochastic Linear Bandits

Ahmadreza Moradipari, Berkay Turan, Yasin Abbasi-Yadkori, Mahnoosh Alizadeh, Mohammad Ghavamzadeh

EqR: Equivariant Representations for Data-Efficient Reinforcement Learning

Arnab Kumar Mondal, Vineet Jain, Kaleem Siddiqi, Siamak Ravanbakhsh

A Multi-objective / Multi-task Learning Framework Induced by Pareto Stationarity

Michinari Momma, Chaosheng Dong, Jia Liu

Differentially Private Community Detection for Stochastic Block Models

Mohamed S Mohamed, Dung Nguyen, Anil Vullikanti, Ravi Tandon

Invariant Ancestry Search

Phillip B Mogensen, Nikolaj Thams, Jonas Peters

Memory-Based Model Editing at Scale

Eric Mitchell, Charles Lin, Antoine Bosselut, Christopher D Manning, Chelsea Finn

Fast Convex Optimization for Two-Layer ReLU Networks: Equivalent Model Classes and Cone Decompositions

Aaron Mishkin, Arda Sahiner, Mert Pilanci

ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally!

Konstantin Mishchenko, Grigory Malinovsky, Sebastian Stich, Peter Richtarik

Proximal and Federated Random Reshuffling

Konstantin Mishchenko, Ahmed Khaled, Peter Richtarik

Wide Neural Networks Forget Less Catastrophically

Seyed Iman Mirzadeh, Arslan Chaudhry, Dong Yin, Huiyi Hu, Razvan Pascanu

A Simple Reward-free Approach to Constrained Reinforcement Learning

Sobhan Miryoosefi, Chi Jin

POEM: Out-of-Distribution Detection with Posterior Sampling

Yifei Ming, Ying Fan, Yixuan Li

Prioritized Training on Points that are Learnable, Worth Learning, and not yet Learnt

Sören Mindermann, Jan M Brauner, Muhammed T Razzak, Mrinank Sharma, Andreas Kirsch

Learning Stochastic Shortest Path with Linear Function Approximation

Yifei Min, Jiafan He, Tianhao Wang, Quanquan Gu

Universal Hopfield Networks: A General Framework for Single-Shot Associative Memory Models

Beren Millidge, Tommaso Salvatori, Yuhang Song, Thomas Lukasiewicz, Rafal Bogacz

Modeling Structure with Undirected Neural Networks

Tsvetomila Mihaylova, Vlad Niculae, Andre Martins

Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism

Siqi Miao, Mia Liu, Pan Li

Distribution Regression with Sliced Wasserstein Kernels

Dimitri Meunier, Massimiliano Pontil, Carlo Ciliberto

A Dynamical System Perspective for Lipschitz Neural Networks

Laurent Meunier, Blaise J Delattre, Alexandre Araujo, Alexandre Allauzen

Minimizing Control for Credit Assignment with Strong Feedback

Alexander Meulemans, Matilde Tristany Farinha, Maria R. Cervera, João Sacramento, Benjamin F. Grewe

Stochastic Rising Bandits

Alberto Maria Metelli, Francesco Trovò, Matteo Pirola, Marcello Restelli

Equivariant Quantum Graph Circuits

Peter Mernyei, Konstantinos Meichanetzidis, İsmail İlkan Ceylan

In defense of dual-encoders for neural ranking

Aditya Menon, Sadeep Jayasumana, Ankit Singh Rawat, Seungyeon Kim, Sashank Reddi

ButterflyFlow: Building Invertible Layers with Butterfly Matrices

Chenlin Meng, Linqi Zhou, Kristy Choi, Tri Dao, Stefano Ermon

Transformers are Meta-Reinforcement Learners

Luckeciano C Melo

Steerable 3D Spherical Neurons

Pavlo Melnyk, Michael Felsberg, Mårten Wadenbäck

Causal Transformer for Estimating Counterfactual Outcomes

Valentyn Melnychuk, Dennis Frauen, Stefan Feuerriegel

Optimizing Tensor Network Contraction Using Reinforcement Learning

Eli Meirom, Haggai Maron, Shie Mannor, Gal Chechik

Quant-BnB: A Scalable Branch-and-Bound Method for Optimal Decision Trees with Continuous Features

Rahul Mazumder, Xiang Meng, Haoyue Wang

How to Steer Your Adversary: Targeted and Efficient Model Stealing Defenses with Gradient Redirection

Mantas Mazeika, Bo Li, David Forsyth

How to Stay Curious while avoiding Noisy TVs using Aleatoric Uncertainty Estimation

Augustine Mavor-Parker, Kimberly Young, Caswell Barry, Lewis Griffin

Continual Repeated Annealed Flow Transport Monte Carlo

Alex Matthews, Michael Arbel, Danilo Jimenez Rezende, Arnaud Doucet

Modular Conformal Calibration

Charles Marx, Shengjia Zhao, Willie Neiswanger, Stefano Ermon

SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators

Karolis Martinkus, Andreas Loukas, Nathanael Perraudin, Roger Wattenhofer

Closed-Form Diffeomorphic Transformations for Time Series Alignment

Iñigo Martinez, Elisabeth Viles, Igor G. Olaizola

Nested Bandits

Matthieu Martin, Panayotis Mertikopoulos, Thibaud Rahier, Houssam Zenati

Personalized Federated Learning through Local Memorization

Othmane MARFOQ, Giovanni Neglia, Richard Vidal, Laetitia Kameni

On the Effects of Artificial Data Modification

Antonia Marcu, Adam Prugel-Bennett

On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learning

Weichao Mao, Lin Yang, Kaiqing Zhang, Tamer Basar

Refined Convergence Rates for Maximum Likelihood Estimation under Finite Mixture Models

Tudor Manole, nhật Hồ

Differentially Private Coordinate Descent for Composite Empirical Risk Minimization

Paul Mangold, Aurélien Bellet, Joseph Salmon, Marc Tommasi

Decision-Focused Learning: Through the Lens of Learning to Rank

Jayanta Mandi, Vı́ctor Bucarey, Maxime Mulamba Ke Tchomba, Tias Guns

Unaligned Supervision for Automatic Music Transcription in The Wild

Ben Maman, Amit H Bermano

More Efficient Sampling for Tensor Decomposition With Worst-Case Guarantees

Osman Asif Malik

Robustness in Multi-Objective Submodular Optimization: a Quantile Approach

Cedric Malherbe, Kevin Scaman

Architecture Agnostic Federated Learning for Neural Networks

Disha Makhija, Xing Han, nhật Hồ, Joydeep Ghosh

Nonparametric Involutive Markov Chain Monte Carlo

Carol Mak, Fabian Zaiser, Luke Ong

Knowledge-Grounded Self-Rationalization via Extractive and Natural Language Explanations

Bodhisattwa Prasad Majumder, Oana Camburu, Thomas Lukasiewicz, Julian McAuley

Feature selection using e-values

Subhabrata Majumdar, Snigdhansu Chatterjee

Zero-Shot Reward Specification via Grounded Natural Language

Parsa Mahmoudieh, Deepak Pathak, Trevor Darrell

A Tighter Analysis of Spectral Clustering, and Beyond

Peter Macgregor, He Sun

Interpretable Neural Networks with Frank-Wolfe: Sparse Relevance Maps and Relevance Orderings

Jan Macdonald, Mathieu E. Besançon, Sebastian Pokutta

Quantification and Analysis of Layer-wise and Pixel-wise Information Discarding

Haotian Ma, Hao Zhang, Fan Zhou, Yinqing Zhang, Quanshi Zhang

Versatile Offline Imitation from Observations and Examples via Regularized State-Occupancy Matching

Yecheng Ma, Andrew Shen, Dinesh Jayaraman, Osbert Bastani

Pessimism meets VCG: Learning Dynamic Mechanism Design via Offline Reinforcement Learning

Boxiang Lyu, Zhaoran Wang, Mladen Kolar, Zhuoran Yang

On Finite-Sample Identifiability of Contrastive Learning-Based Nonlinear Independent Component Analysis

Qi Lyu, Xiao Fu

Learning Dynamics and Generalization in Deep Reinforcement Learning

Clare Lyle, Mark Rowland, Will Dabney, Marta Kwiatkowska, Yarin Gal

Channel Importance Matters in Few-Shot Image Classification

Xu Luo, Jing Xu, Zenglin Xu

Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferring

Zhengquan Luo, Yunlong Wang, Zilei Wang, Zhenan Sun, Tieniu Tan

BAMDT: Bayesian Additive Semi-Multivariate Decision Trees for Nonparametric Regression

Zhao Tang Luo, Huiyan Sang, Bani Mallick

A Rigorous Study of Integrated Gradients Method and Extensions to Internal Neuron Attributions

Daniel D Lundstrom, Tianjian Huang, Meisam Razaviyayn

Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering

Ekdeep Lubana, Chi Ian Tang, Fahim Kawsar, Robert Dick, Akhil Mathur

Maximum Likelihood Training for Score-based Diffusion ODEs by High Order Denoising Score Matching

Cheng Lu, Kaiwen Zheng, Fan Bao, Jianfei Chen, Chongxuan Li