ICLR 2022

1,094 papers

Autoregressive Quantile Flows for Predictive Uncertainty Estimation

Phillip Si, Allan Bishop, Volodymyr Kuleshov

Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series

Enyan Dai, Jie Chen

What Happens after SGD Reaches Zero Loss? --A Mathematical Framework

Zhiyuan Li, Tianhao Wang, Sanjeev Arora

Learning Long-Term Reward Redistribution via Randomized Return Decomposition

Zhizhou Ren, Ruihan Guo, Yuan Zhou, Jian Peng

Training invariances and the low-rank phenomenon: beyond linear networks

Thien Le, Stefanie Jegelka

Revisiting Over-smoothing in BERT from the Perspective of Graph

Han Shi, Jiahui Gao, Hang Xu, Xiaodan Liang, Zhenguo Li

Properties from mechanisms: an equivariance perspective on identifiable representation learning

Kartik Ahuja, Jason Hartford, Yoshua Bengio

TRGP: Trust Region Gradient Projection for Continual Learning

Sen Lin, Li Yang, Deliang Fan, Junshan Zhang

POETREE: Interpretable Policy Learning with Adaptive Decision Trees

Alizée Pace, Alex Chan, Mihaela van der Schaar

ViTGAN: Training GANs with Vision Transformers

Kwonjoon Lee, Huiwen Chang, Lu Jiang, Han Zhang, Zhuowen Tu

Learning Vision-Guided Quadrupedal Locomotion End-to-End with Cross-Modal Transformers

Ruihan Yang, Minghao Zhang, Nicklas Hansen, Huazhe Xu, Xiaolong Wang

COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation

Jongmin Lee, Cosmin Paduraru, Daniel J Mankowitz, Nicolas Heess, Doina Precup

NASPY: Automated Extraction of Automated Machine Learning Models

Xiaoxuan Lou, Shangwei Guo, Jiwei Li, Yaoxin Wu, Tianwei Zhang

Revisiting Design Choices in Offline Model Based Reinforcement Learning

Cong Lu, Philip Ball, Jack Parker-Holder, Michael Osborne, Stephen J. Roberts

When should agents explore?

Miruna Pislar, David Szepesvari, Georg Ostrovski, Diana L Borsa, Tom Schaul

Learning Multimodal VAEs through Mutual Supervision

Tom Joy, Yuge Shi, Philip Torr, Tom Rainforth, Sebastian M Schmon

Contrastive Fine-grained Class Clustering via Generative Adversarial Networks

Yunji Kim, Jung-Woo Ha

Compositional Attention: Disentangling Search and Retrieval

Sarthak Mittal, Sharath Chandra Raparthy, Irina Rish, Yoshua Bengio, Guillaume Lajoie

Escaping limit cycles: Global convergence for constrained nonconvex-nonconcave minimax problems

Thomas Pethick, Puya Latafat, Panos Patrinos, Olivier Fercoq, Volkan Cevher

EntQA: Entity Linking as Question Answering

Wenzheng Zhang, Wenyue Hua, Karl Stratos

Looking Back on Learned Experiences For Class/task Incremental Learning

Mozhgan PourKeshavarzi, Guoying Zhao, Mohammad Sabokrou

Transition to Linearity of Wide Neural Networks is an Emerging Property of Assembling Weak Models

Chaoyue Liu, Libin Zhu, Misha Belkin

Improved deterministic l2 robustness on CIFAR-10 and CIFAR-100

Sahil Singla, Surbhi Singla, Soheil Feizi

Learnability of convolutional neural networks for infinite dimensional input via mixed and anisotropic smoothness

Sho Okumoto, Taiji Suzuki

NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning

Chun-Hao Chang, Rich Caruana, Anna Goldenberg

Continual Learning with Recursive Gradient Optimization

Hao Liu, Huaping Liu

Continual Learning with Filter Atom Swapping

Zichen Miao, Ze Wang, Wei Chen, Qiang Qiu

GNN-LM: Language Modeling based on Global Contexts via GNN

Yuxian Meng, Shi Zong, Xiaoya Li, Xiaofei Sun, Tianwei Zhang

Towards a Unified View of Parameter-Efficient Transfer Learning

Junxian He, Chunting Zhou, Xuezhe Ma, Taylor Berg-Kirkpatrick, Graham Neubig

Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking

Octavian-Eugen Ganea, Xinyuan Huang, Charlotte Bunne, Yatao Bian, Regina Barzilay

AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning

Biwei Huang, Fan Feng, Chaochao Lu, Sara Magliacane, Kun Zhang

Scaling Laws for Neural Machine Translation

Behrooz Ghorbani, Orhan Firat, Markus Freitag, Ankur Bapna, Maxim Krikun

Universal Approximation Under Constraints is Possible with Transformers

Anastasis Kratsios, Behnoosh Zamanlooy, Tianlin Liu, Ivan Dokmanić

Exploring the Limits of Large Scale Pre-training

Samira Abnar, Mostafa Dehghani, Behnam Neyshabur, Hanie Sedghi

Multi-Stage Episodic Control for Strategic Exploration in Text Games

Jens Tuyls, Shunyu Yao, Sham M. Kakade, Karthik R Narasimhan

Message Passing Neural PDE Solvers

Johannes Brandstetter, Daniel E. Worrall, Max Welling

The MultiBERTs: BERT Reproductions for Robustness Analysis

Thibault Sellam, Steve Yadlowsky, Ian Tenney, Jason Wei, Naomi Saphra

TAMP-S2GCNets: Coupling Time-Aware Multipersistence Knowledge Representation with Spatio-Supra Graph Convolutional Networks for Time-Series Forecasting

Yuzhou Chen, Ignacio Segovia-Dominguez, Baris Coskunuzer, Yulia Gel

Linking Emergent and Natural Languages via Corpus Transfer

Shunyu Yao, Mo Yu, Yang Zhang, Karthik R Narasimhan, Joshua B. Tenenbaum

Reinforcement Learning with Sparse Rewards using Guidance from Offline Demonstration

Desik Rengarajan, Gargi Vaidya, Akshay Sarvesh, Dileep Kalathil, Srinivas Shakkottai

Fairness in Representation for Multilingual NLP: Insights from Controlled Experiments on Conditional Language Modeling

Ada Wan

Value Gradient weighted Model-Based Reinforcement Learning

Claas A Voelcker, Victor Liao, Animesh Garg, Amir-massoud Farahmand

On Bridging Generic and Personalized Federated Learning for Image Classification

Hong-You Chen, Wei-Lun Chao

Optimal Transport for Causal Discovery

Ruibo Tu, Kun Zhang, Hedvig Kjellström, Cheng Zhang

CoBERL: Contrastive BERT for Reinforcement Learning

Andrea Banino, Adrià Puigdomènech Badia, Jacob C Walker, Tim Scholtes, Jovana Mitrovic

Relational Multi-Task Learning: Modeling Relations between Data and Tasks

Kaidi Cao, Jiaxuan You, Jure Leskovec

SOSP: Efficiently Capturing Global Correlations by Second-Order Structured Pruning

Manuel Nonnenmacher, Thomas Pfeil, Ingo Steinwart, David Reeb

Boosting Randomized Smoothing with Variance Reduced Classifiers

Miklós Z. Horváth, Mark Niklas Mueller, Marc Fischer, Martin Vechev

SphereFace2: Binary Classification is All You Need for Deep Face Recognition

Yandong Wen, Weiyang Liu, Adrian Weller, Bhiksha Raj, Rita Singh

Geometric and Physical Quantities improve E(3) Equivariant Message Passing

Johannes Brandstetter, Rob Hesselink, Elise van der Pol, Erik J Bekkers, Max Welling

Does your graph need a confidence boost? Convergent boosted smoothing on graphs with tabular node features

Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis, Soji Adeshina, Yangkun Wang

MT3: Multi-Task Multitrack Music Transcription

Joshua P Gardner, Ian Simon, Ethan Manilow, Curtis Hawthorne, Jesse Engel

DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization

Aviral Kumar, Rishabh Agarwal, Tengyu Ma, Aaron Courville, George Tucker

Perceiver IO: A General Architecture for Structured Inputs & Outputs

Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu

On the Uncomputability of Partition Functions in Energy-Based Sequence Models

Chu-Cheng Lin, Arya D. McCarthy

Learning-Augmented k-means Clustering

Jon C. Ergun, Zhili Feng, Sandeep Silwal, David Woodruff, Samson Zhou

Memorizing Transformers

Yuhuai Wu, Markus Norman Rabe, DeLesley Hutchins, Christian Szegedy

Deconstructing the Inductive Biases of Hamiltonian Neural Networks

Nate Gruver, Marc Anton Finzi, Samuel Don Stanton, Andrew Gordon Wilson

Understanding Latent Correlation-Based Multiview Learning and Self-Supervision: An Identifiability Perspective

Qi Lyu, Xiao Fu, Weiran Wang, Songtao Lu

Latent Variable Sequential Set Transformers for Joint Multi-Agent Motion Prediction

Roger Girgis, Florian Golemo, Felipe Codevilla, Martin Weiss, Jim Aldon D'Souza

Learning meta-features for AutoML

Herilalaina Rakotoarison, Louisot Milijaona, Andry RASOANAIVO, Michele Sebag, Marc Schoenauer

GreaseLM: Graph REASoning Enhanced Language Models

Xikun Zhang, Antoine Bosselut, Michihiro Yasunaga, Hongyu Ren, Percy Liang

Adversarial Support Alignment

Shangyuan Tong, Timur Garipov, Yang Zhang, Shiyu Chang, Tommi S. Jaakkola

Reinforcement Learning under a Multi-agent Predictive State Representation Model: Method and Theory

Zhi Zhang, Zhuoran Yang, Han Liu, Pratap Tokekar, Furong Huang

Learning more skills through optimistic exploration

DJ Strouse, Kate Baumli, David Warde-Farley, Volodymyr Mnih, Steven Stenberg Hansen

Strength of Minibatch Noise in SGD

Liu Ziyin, Kangqiao Liu, Takashi Mori, Masahito Ueda

On the Connection between Local Attention and Dynamic Depth-wise Convolution

Qi Han, Zejia Fan, Qi Dai, Lei Sun, Ming-Ming Cheng

On Predicting Generalization using GANs

Yi Zhang, Arushi Gupta, Nikunj Saunshi, Sanjeev Arora

On Improving Adversarial Transferability of Vision Transformers

Muzammal Naseer, Kanchana Ranasinghe, Salman Khan, Fahad Khan, Fatih Porikli

RotoGrad: Gradient Homogenization in Multitask Learning

Adrián Javaloy, Isabel Valera

Planning in Stochastic Environments with a Learned Model

Ioannis Antonoglou, Julian Schrittwieser, Sherjil Ozair, Thomas K Hubert, David Silver

Sampling with Mirrored Stein Operators

Jiaxin Shi, Chang Liu, Lester Mackey

Learning Hierarchical Structures with Differentiable Nondeterministic Stacks

Brian DuSell, David Chiang

Dynamics-Aware Comparison of Learned Reward Functions

Blake Wulfe, Logan Michael Ellis, Jean Mercat, Rowan Thomas McAllister, Adrien Gaidon

Wiring Up Vision: Minimizing Supervised Synaptic Updates Needed to Produce a Primate Ventral Stream

Franziska Geiger, Martin Schrimpf, Tiago Marques, James J. DiCarlo

Language modeling via stochastic processes

Rose E Wang, Esin Durmus, Noah Goodman, Tatsunori Hashimoto

The Information Geometry of Unsupervised Reinforcement Learning

Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine

RISP: Rendering-Invariant State Predictor with Differentiable Simulation and Rendering for Cross-Domain Parameter Estimation

Pingchuan Ma, Tao Du, Joshua B. Tenenbaum, Wojciech Matusik, Chuang Gan

Analytic-DPM: an Analytic Estimate of the Optimal Reverse Variance in Diffusion Probabilistic Models

Fan Bao, Chongxuan Li, Jun Zhu, Bo Zhang

CycleMLP: A MLP-like Architecture for Dense Prediction

Shoufa Chen, Enze Xie, Chongjian GE, Runjian Chen, Ding Liang

Real-Time Neural Voice Camouflage

Mia Chiquier, Chengzhi Mao, Carl Vondrick

Hyperparameter Tuning with Renyi Differential Privacy

Nicolas Papernot, Thomas Steinke

ProtoRes: Proto-Residual Network for Pose Authoring via Learned Inverse Kinematics

Boris N. Oreshkin, Florent Bocquelet, Felix G. Harvey, Bay Raitt, Dominic Laflamme

Transform2Act: Learning a Transform-and-Control Policy for Efficient Agent Design

Ye Yuan, Yuda Song, Zhengyi Luo, Wen Sun, Kris M. Kitani

F8Net: Fixed-Point 8-bit Only Multiplication for Network Quantization

Qing Jin, Jian Ren, Richard Zhuang, Sumant Hanumante, Zhengang Li

Finetuned Language Models are Zero-Shot Learners

Jason Wei, Maarten Bosma, Vincent Zhao, Kelvin Guu, Adams Wei Yu

Sparse Communication via Mixed Distributions

António Farinhas, Wilker Aziz, Vlad Niculae, Andre Martins

DISCOVERING AND EXPLAINING THE REPRESENTATION BOTTLENECK OF DNNS

Huiqi Deng, Qihan Ren, Hao Zhang, Quanshi Zhang

Meta-Learning with Fewer Tasks through Task Interpolation

Huaxiu Yao, Linjun Zhang, Chelsea Finn

Vision-Based Manipulators Need to Also See from Their Hands

Kyle Hsu, Moo Jin Kim, Rafael Rafailov, Jiajun Wu, Chelsea Finn

Representational Continuity for Unsupervised Continual Learning

Divyam Madaan, Jaehong Yoon, Yuanchun Li, Yunxin Liu, Sung Ju Hwang

Resolving Training Biases via Influence-based Data Relabeling

Shuming Kong, Yanyan Shen, Linpeng Huang

Diffusion-Based Voice Conversion with Fast Maximum Likelihood Sampling Scheme

Vadim Popov, Ivan Vovk, Vladimir Gogoryan, Tasnima Sadekova, Mikhail Sergeevich Kudinov

Understanding over-squashing and bottlenecks on graphs via curvature

Jake Topping, Francesco Di Giovanni, Benjamin Paul Chamberlain, Xiaowen Dong, Michael M. Bronstein

Learning Strides in Convolutional Neural Networks

Rachid Riad, Olivier Teboul, David Grangier, Neil Zeghidour

Open-Set Recognition: A Good Closed-Set Classifier is All You Need

Sagar Vaze, Kai Han, Andrea Vedaldi, Andrew Zisserman

A Fine-Grained Analysis on Distribution Shift

Olivia Wiles, Sven Gowal, Florian Stimberg, Sylvestre-Alvise Rebuffi, Ira Ktena

Einops: Clear and Reliable Tensor Manipulations with Einstein-like Notation

Alex Rogozhnikov

Frame Averaging for Invariant and Equivariant Network Design

Omri Puny, Matan Atzmon, Edward J. Smith, Ishan Misra, Aditya Grover

GeoDiff: A Geometric Diffusion Model for Molecular Conformation Generation

Minkai Xu, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon