ICLR 2022

1,094 papers

Improving Federated Learning Face Recognition via Privacy-Agnostic Clusters

Qiang Meng, Feng Zhou, Hainan Ren, Tianshu Feng, Guochao Liu

Probabilistic Implicit Scene Completion

Dongsu Zhang, Changwoon Choi, Inbum Park, Young Min Kim

Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks

Marten Lienen, Stephan Günnemann

Increasing the Cost of Model Extraction with Calibrated Proof of Work

Adam Dziedzic, Muhammad Ahmad Kaleem, Yu Shen Lu, Nicolas Papernot

Hybrid Local SGD for Federated Learning with Heterogeneous Communications

Yuanxiong Guo, Ying Sun, Rui Hu, Yanmin Gong

SGD Can Converge to Local Maxima

Liu Ziyin, Botao Li, James B Simon, Masahito Ueda

Equivariant Transformers for Neural Network based Molecular Potentials

Philipp Thölke, Gianni De Fabritiis

Label Encoding for Regression Networks

Deval Shah, Zi Yu Xue, Tor Aamodt

Understanding the Role of Self Attention for Efficient Speech Recognition

Kyuhong Shim, Jungwook Choi, Wonyong Sung

Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing

Sai Praneeth Karimireddy, Lie He, Martin Jaggi

PAC-Bayes Information Bottleneck

Zifeng Wang, Shao-Lun Huang, Ercan Engin Kuruoglu, Jimeng Sun, Xi Chen

IntSGD: Adaptive Floatless Compression of Stochastic Gradients

Konstantin Mishchenko, Bokun Wang, Dmitry Kovalev, Peter Richtarik

Jiechao Guan, Zhiwu Lu

Towards Deployment-Efficient Reinforcement Learning: Lower Bound and Optimality

Jiawei Huang, Jinglin Chen, Li Zhao, Tao Qin, Nan Jiang

SHINE: SHaring the INverse Estimate from the forward pass for bi-level optimization and implicit models

Zaccharie Ramzi, Florian Mannel, Shaojie Bai, Jean-Luc Starck, Philippe Ciuciu

Superclass-Conditional Gaussian Mixture Model For Learning Fine-Grained Embeddings

Jingchao Ni, Wei Cheng, Zhengzhang Chen, Takayoshi Asakura, Tomoya Soma

Emergent Communication at Scale

Rahma Chaabouni, Florian Strub, Florent Altché, Eugene Tarassov, Corentin Tallec

The Inductive Bias of In-Context Learning: Rethinking Pretraining Example Design

Yoav Levine, Noam Wies, Daniel Jannai, Dan Navon, Yedid Hoshen

Source-Free Adaptation to Measurement Shift via Bottom-Up Feature Restoration

Cian Eastwood, Ian Mason, Chris Williams, Bernhard Schölkopf

Understanding and Preventing Capacity Loss in Reinforcement Learning

Clare Lyle, Mark Rowland, Will Dabney

Imbedding Deep Neural Networks

Andrew Corbett, Dmitry Kangin

Tackling the Generative Learning Trilemma with Denoising Diffusion GANs

Zhisheng Xiao, Karsten Kreis, Arash Vahdat

Variational methods for simulation-based inference

Manuel Glöckler, Michael Deistler, Jakob H. Macke

How Do Vision Transformers Work?

Namuk Park, Songkuk Kim

Equivariant Subgraph Aggregation Networks

Beatrice Bevilacqua, Fabrizio Frasca, Derek Lim, Balasubramaniam Srinivasan, Chen Cai

Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning

Chenjia Bai, Lingxiao Wang, Zhuoran Yang, Zhi-Hong Deng, Animesh Garg

Generative Planning for Temporally Coordinated Exploration in Reinforcement Learning

Haichao Zhang, Wei Xu, Haonan Yu

Assessing Generalization of SGD via Disagreement

YiDing Jiang, Vaishnavh Nagarajan, Christina Baek, J Zico Kolter

A General Analysis of Example-Selection for Stochastic Gradient Descent

Yucheng Lu, Si Yi Meng, Christopher De Sa

Progressive Distillation for Fast Sampling of Diffusion Models

Tim Salimans, Jonathan Ho

Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy

Jiehui Xu, Haixu Wu, Jianmin Wang, Mingsheng Long

Long Expressive Memory for Sequence Modeling

T. Konstantin Rusch, Siddhartha Mishra, N. Benjamin Erichson, Michael W. Mahoney

Tighter Sparse Approximation Bounds for ReLU Neural Networks

Carles Domingo-Enrich, Youssef Mroueh

Online Hyperparameter Meta-Learning with Hypergradient Distillation

Hae Beom Lee, Hayeon Lee, JaeWoong Shin, Eunho Yang, Timothy Hospedales

When Vision Transformers Outperform ResNets without Pre-training or Strong Data Augmentations

Xiangning Chen, Cho-jui Hsieh, Boqing Gong

Programmatic Reinforcement Learning without Oracles

Wenjie Qiu, He Zhu

On the Optimal Memorization Power of ReLU Neural Networks

Gal Vardi, Gilad Yehudai, Ohad Shamir

Implicit Bias of Projected Subgradient Method Gives Provable Robust Recovery of Subspaces of Unknown Codimension

Paris Giampouras, Benjamin David Haeffele, René Vidal

On the relation between statistical learning and perceptual distances

Alexander Hepburn, Valero Laparra, Raul Santos-Rodriguez, Jona Ballé, Jesus Malo

Continuous-Time Meta-Learning with Forward Mode Differentiation

Tristan Deleu, David Kanaa, Leo Feng, Giancarlo Kerg, Yoshua Bengio

Multitask Prompted Training Enables Zero-Shot Task Generalization

Victor Sanh, Albert Webson, Colin Raffel, Stephen Bach, Lintang Sutawika

Learning Optimal Conformal Classifiers

David Stutz, Krishnamurthy Dj Dvijotham, Ali Taylan Cemgil, Arnaud Doucet

Policy improvement by planning with Gumbel

Ivo Danihelka, Arthur Guez, Julian Schrittwieser, David Silver

Spanning Tree-based Graph Generation for Molecules

Sungsoo Ahn, Binghong Chen, Tianzhe Wang, Le Song

D-CODE: Discovering Closed-form ODEs from Observed Trajectories

Zhaozhi Qian, Krzysztof Kacprzyk, Mihaela van der Schaar

EViT: Expediting Vision Transformers via Token Reorganizations

Youwei Liang, Chongjian GE, Zhan Tong, Yibing Song, Jue Wang

Omni-Dimensional Dynamic Convolution

CHAO LI, Aojun Zhou, Anbang Yao

Finding Biological Plausibility for Adversarially Robust Features via Metameric Tasks

Anne Harrington, Arturo Deza

8-bit Optimizers via Block-wise Quantization

Tim Dettmers, Mike Lewis, Sam Shleifer, Luke Zettlemoyer

Pixelated Butterfly: Simple and Efficient Sparse training for Neural Network Models

Beidi Chen, Tri Dao, Kaizhao Liang, Jiaming Yang, Zhao Song

On the approximation properties of recurrent encoder-decoder architectures

Zhong Li, Haotian Jiang, Qianxiao Li

Context-Aware Sparse Deep Coordination Graphs

Tonghan Wang, Liang Zeng, Weijun Dong, Qianlan Yang, Yang Yu

Learning Altruistic Behaviours in Reinforcement Learning without External Rewards

Tim Franzmeyer, Mateusz Malinowski, João F. Henriques

Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions

Bertrand Charpentier, Oliver Borchert, Daniel Zügner, Simon Geisler, Stephan Günnemann

Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutions

Leslie O'Bray, Max Horn, Bastian Rieck, Karsten Borgwardt

DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting

Wei Fan, Shun Zheng, Xiaohan Yi, Wei Cao, Yanjie Fu

Unifying Likelihood-free Inference with Black-box Optimization and Beyond

Dinghuai Zhang, Jie Fu, Yoshua Bengio, Aaron Courville

Generalized Decision Transformer for Offline Hindsight Information Matching

Hiroki Furuta, Yutaka Matsuo, Shixiang Shane Gu

VAE Approximation Error: ELBO and Exponential Families

Alexander Shekhovtsov, Dmitrij Schlesinger, Boris Flach

Constrained Policy Optimization via Bayesian World Models

Yarden As, Ilnura Usmanova, Sebastian Curi, Andreas Krause

Meta Discovery: Learning to Discover Novel Classes given Very Limited Data

Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu

Near-Optimal Reward-Free Exploration for Linear Mixture MDPs with Plug-in Solver

Xiaoyu Chen, Jiachen Hu, Lin Yang, Liwei Wang

Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions

Nicholas Gao, Stephan Günnemann

Self-supervised Learning is More Robust to Dataset Imbalance

Hong Liu, Jeff Z. HaoChen, Adrien Gaidon, Tengyu Ma

On Lottery Tickets and Minimal Task Representations in Deep Reinforcement Learning

Marc Vischer, Robert Tjarko Lange, Henning Sprekeler

Explanations of Black-Box Models based on Directional Feature Interactions

Aria Masoomi, Davin Hill, Zhonghui Xu, Craig P Hersh, Edwin K. Silverman

Compositional Training for End-to-End Deep AUC Maximization

Zhuoning Yuan, Zhishuai Guo, Nitesh Chawla, Tianbao Yang

Learning transferable motor skills with hierarchical latent mixture policies

Dushyant Rao, Fereshteh Sadeghi, Leonard Hasenclever, Markus Wulfmeier, Martina Zambelli

Learning Pruning-Friendly Networks via Frank-Wolfe: One-Shot, Any-Sparsity, And No Retraining

Miao Lu, Xiaolong Luo, Tianlong Chen, Wuyang Chen, Dong Liu

Sample Efficient Deep Reinforcement Learning via Uncertainty Estimation

Vincent Mai, Kaustubh Mani, Liam Paull

Scalable One-Pass Optimisation of High-Dimensional Weight-Update Hyperparameters by Implicit Differentiation

Ross M Clarke, Elre Talea Oldewage, José Miguel Hernández-Lobato

Learning Causal Models from Conditional Moment Restrictions by Importance Weighting

Masahiro Kato, Masaaki Imaizumi, Kenichiro McAlinn, Shota Yasui, Haruo Kakehi

Churn Reduction via Distillation

Heinrich Jiang, Harikrishna Narasimhan, Dara Bahri, Andrew Cotter, Afshin Rostamizadeh

Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design

Wengong Jin, Jeremy Wohlwend, Regina Barzilay, Tommi S. Jaakkola

Amortized Tree Generation for Bottom-up Synthesis Planning and Synthesizable Molecular Design

Wenhao Gao, Rocío Mercado, Connor W. Coley

Scalable Sampling for Nonsymmetric Determinantal Point Processes

Insu Han, Mike Gartrell, Jennifer Gillenwater, Elvis DOHMATOB, Amin Karbasi

Leveraging Automated Unit Tests for Unsupervised Code Translation

Baptiste Roziere, Jie Zhang, Francois Charton, Mark Harman, Gabriel Synnaeve

Contact Points Discovery for Soft-Body Manipulations with Differentiable Physics

Sizhe Li, Zhiao Huang, Tao Du, Hao Su, Joshua B. Tenenbaum

Analyzing and Improving the Optimization Landscape of Noise-Contrastive Estimation

Bingbin Liu, Elan Rosenfeld, Pradeep Kumar Ravikumar, Andrej Risteski

RelaxLoss: Defending Membership Inference Attacks without Losing Utility

Dingfan Chen, Ning Yu, Mario Fritz

How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective

Yimeng Zhang, Yuguang Yao, Jinghan Jia, Jinfeng Yi, Mingyi Hong

Spike-inspired rank coding for fast and accurate recurrent neural networks

Alan Jeffares, Qinghai Guo, Pontus Stenetorp, Timoleon Moraitis

EE-Net: Exploitation-Exploration Neural Networks in Contextual Bandits

Yikun Ban, Yuchen Yan, Arindam Banerjee, Jingrui He

Half-Inverse Gradients for Physical Deep Learning

Patrick Schnell, Philipp Holl, Nils Thuerey

Interpretable Unsupervised Diversity Denoising and Artefact Removal

Mangal Prakash, Mauricio Delbracio, Peyman Milanfar, Florian Jug

Possibility Before Utility: Learning And Using Hierarchical Affordances

Robby Costales, Shariq Iqbal, Fei Sha

Path Auxiliary Proposal for MCMC in Discrete Space

Haoran Sun, Hanjun Dai, Wei Xia, Arun Ramamurthy

Responsible Disclosure of Generative Models Using Scalable Fingerprinting

Ning Yu, Vladislav Skripniuk, Dingfan Chen, Larry S. Davis, Mario Fritz

Scarf: Self-Supervised Contrastive Learning using Random Feature Corruption

Dara Bahri, Heinrich Jiang, Yi Tay, Donald Metzler

SO(2)-Equivariant Reinforcement Learning

Dian Wang, Robin Walters, Robert Platt

Understanding Domain Randomization for Sim-to-real Transfer

Xiaoyu Chen, Jiachen Hu, Chi Jin, Lihong Li, Liwei Wang

Lossless Compression with Probabilistic Circuits

Anji Liu, Stephan Mandt, Guy Van den Broeck

Representation Learning for Online and Offline RL in Low-rank MDPs

Masatoshi Uehara, Xuezhou Zhang, Wen Sun

R5: Rule Discovery with Reinforced and Recurrent Relational Reasoning

Shengyao Lu, Bang Liu, Keith G. Mills, Shangling Jui, Di Niu

Controlling Directions Orthogonal to a Classifier

Yilun Xu, Hao He, Tianxiao Shen, Tommi S. Jaakkola

Score-Based Generative Modeling with Critically-Damped Langevin Diffusion

Tim Dockhorn, Arash Vahdat, Karsten Kreis

Self-Supervision Enhanced Feature Selection with Correlated Gates

Changhee Lee, Fergus Imrie, Mihaela van der Schaar

Towards Understanding the Data Dependency of Mixup-style Training

Muthu Chidambaram, Xiang Wang, Yuzheng Hu, Chenwei Wu, Rong Ge

Finite-Time Convergence and Sample Complexity of Multi-Agent Actor-Critic Reinforcement Learning with Average Reward

FNU Hairi, Jia Liu, Songtao Lu

On the Importance of Firth Bias Reduction in Few-Shot Classification

Saba Ghaffari, Ehsan Saleh, David Forsyth, Yu-Xiong Wang