ICLR 2021

859 papers

Scaling Symbolic Methods using Gradients for Neural Model Explanation

Subham Sekhar Sahoo, Subhashini Venugopalan, Li Li, Rishabh Singh, Patrick Riley

The Risks of Invariant Risk Minimization

Elan Rosenfeld, Pradeep Kumar Ravikumar, Andrej Risteski

Incorporating Symmetry into Deep Dynamics Models for Improved Generalization

Rui Wang, Robin Walters, Rose Yu

Learning from Demonstration with Weakly Supervised Disentanglement

Yordan Hristov, Subramanian Ramamoorthy

Linear Convergent Decentralized Optimization with Compression

Xiaorui Liu, Yao Li, Rongrong Wang, Jiliang Tang, Ming Yan

Revisiting Few-sample BERT Fine-tuning

Tianyi Zhang, Felix Wu, Arzoo Katiyar, Kilian Q Weinberger, Yoav Artzi

Incremental few-shot learning via vector quantization in deep embedded space

Kuilin Chen, Chi-Guhn Lee

My Body is a Cage: the Role of Morphology in Graph-Based Incompatible Control

Vitaly Kurin, Maximilian Igl, Tim Rocktäschel, Wendelin Boehmer, Shimon Whiteson

Directed Acyclic Graph Neural Networks

Veronika Thost, Jie Chen

For self-supervised learning, Rationality implies generalization, provably

Yamini Bansal, Gal Kaplun, Boaz Barak

Explaining by Imitating: Understanding Decisions by Interpretable Policy Learning

Alihan Hüyük, Daniel Jarrett, Cem Tekin, Mihaela van der Schaar

A Critique of Self-Expressive Deep Subspace Clustering

Benjamin David Haeffele, Chong You, René Vidal

WaveGrad: Estimating Gradients for Waveform Generation

Nanxin Chen, Yu Zhang, Heiga Zen, Ron J Weiss, Mohammad Norouzi

On the Critical Role of Conventions in Adaptive Human-AI Collaboration

Andy Shih, Arjun Sawhney, Jovana Kondic, Stefano Ermon, Dorsa Sadigh

Multi-resolution modeling of a discrete stochastic process identifies causes of cancer

Adam Uri Yaari, Maxwell Sherman, Oliver Clarke Priebe, Po-Ru Loh, Boris Katz

Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers

Benjamin Eysenbach, Shreyas Chaudhari, Swapnil Asawa, Sergey Levine, Ruslan Salakhutdinov

C-Learning: Learning to Achieve Goals via Recursive Classification

Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine

Generating Adversarial Computer Programs using Optimized Obfuscations

Shashank Srikant, Sijia Liu, Tamara Mitrovska, Shiyu Chang, Quanfu Fan

Fast convergence of stochastic subgradient method under interpolation

Huang Fang, Zhenan Fan, Michael Friedlander

Deep Neural Tangent Kernel and Laplace Kernel Have the Same RKHS

Lin Chen, Sheng Xu

Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning

Haibo Yang, Minghong Fang, Jia Liu

Anchor & Transform: Learning Sparse Embeddings for Large Vocabularies

Paul Pu Liang, Manzil Zaheer, Yuan Wang, Amr Ahmed

On InstaHide, Phase Retrieval, and Sparse Matrix Factorization

Sitan Chen, Xiaoxiao Li, Zhao Song, Danyang Zhuo

Effective Distributed Learning with Random Features: Improved Bounds and Algorithms

Yong Liu, Jiankun Liu, Shuqiang Wang

Loss Function Discovery for Object Detection via Convergence-Simulation Driven Search

Peidong Liu, Gengwei Zhang, Bochao Wang, Hang Xu, Xiaodan Liang

Deep Equals Shallow for ReLU Networks in Kernel Regimes

Alberto Bietti, Francis Bach

Task-Agnostic Morphology Evolution

Donald Joseph Hejna III, Pieter Abbeel, Lerrel Pinto

Discrete Graph Structure Learning for Forecasting Multiple Time Series

Chao Shang, Jie Chen, Jinbo Bi

X2T: Training an X-to-Text Typing Interface with Online Learning from User Feedback

Jensen Gao, Siddharth Reddy, Glen Berseth, Nicholas Hardy, Nikhilesh Natraj

PAC Confidence Predictions for Deep Neural Network Classifiers

Sangdon Park, Shuo Li, Insup Lee, Osbert Bastani

A Design Space Study for LISTA and Beyond

Tianjian Meng, Xiaohan Chen, Yifan Jiang, Zhangyang Wang

Robust early-learning: Hindering the memorization of noisy labels

Xiaobo Xia, Tongliang Liu, Bo Han, Chen Gong, Nannan Wang

Direction Matters: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate

Jingfeng Wu, Difan Zou, Vladimir braverman, Quanquan Gu

ANOCE: Analysis of Causal Effects with Multiple Mediators via Constrained Structural Learning

Hengrui Cai, Rui Song, Wenbin Lu

CcGAN: Continuous Conditional Generative Adversarial Networks for Image Generation

Xin Ding, Yongwei Wang, Zuheng Xu, William J Welch, Z. Jane Wang

Learning What To Do by Simulating the Past

David Lindner, Rohin Shah, Pieter Abbeel, Anca Dragan

AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models

Ke Sun, Zhanxing Zhu, Zhouchen Lin

CoCo: Controllable Counterfactuals for Evaluating Dialogue State Trackers

Shiyang Li, Semih Yavuz, Kazuma Hashimoto, Jia Li, Tong Niu

Combining Ensembles and Data Augmentation Can Harm Your Calibration

Yeming Wen, Ghassen Jerfel, Rafael Müller, Michael W Dusenberry, Jasper Snoek

Evaluating the Disentanglement of Deep Generative Models through Manifold Topology

Sharon Zhou, Eric Zelikman, Fred Lu, Andrew Y. Ng, Gunnar E. Carlsson

Latent Skill Planning for Exploration and Transfer

Kevin Xie, Homanga Bharadhwaj, Danijar Hafner, Animesh Garg, Florian Shkurti

ALFWorld: Aligning Text and Embodied Environments for Interactive Learning

Mohit Shridhar, Xingdi Yuan, Marc-Alexandre Côté, Yonatan Bisk, Adam Trischler

MODALS: Modality-agnostic Automated Data Augmentation in the Latent Space

Tsz-Him Cheung, Dit-Yan Yeung

Spatio-Temporal Graph Scattering Transform

Chao Pan, Siheng Chen, Antonio Ortega

On Dyadic Fairness: Exploring and Mitigating Bias in Graph Connections

Peizhao Li, Yifei Wang, Han Zhao, Pengyu Hong, Hongfu Liu

Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval

Wenhan Xiong, Xiang Li, Srini Iyer, Jingfei Du, Patrick Lewis

What Makes Instance Discrimination Good for Transfer Learning?

Nanxuan Zhao, Zhirong Wu, Rynson W. H. Lau, Stephen Lin

A Geometric Analysis of Deep Generative Image Models and Its Applications

Binxu Wang, Carlos R Ponce

Meta-learning Symmetries by Reparameterization

Allan Zhou, Tom Knowles, Chelsea Finn

When Optimizing f-Divergence is Robust with Label Noise

Jiaheng Wei, Yang Liu

An Unsupervised Deep Learning Approach for Real-World Image Denoising

Dihan Zheng, Sia Huat Tan, Xiaowen Zhang, Zuoqiang Shi, Kaisheng Ma

FairBatch: Batch Selection for Model Fairness

Yuji Roh, Kangwook Lee, Steven Euijong Whang, Changho Suh

Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval

Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu

Empirical or Invariant Risk Minimization? A Sample Complexity Perspective

Kartik Ahuja, Jun Wang, Amit Dhurandhar, Karthikeyan Shanmugam, Kush R. Varshney

Uncertainty Estimation and Calibration with Finite-State Probabilistic RNNs

Cheng Wang, Carolin Lawrence, Mathias Niepert

The Importance of Pessimism in Fixed-Dataset Policy Optimization

Jacob Buckman, Carles Gelada, Marc G Bellemare

Local Convergence Analysis of Gradient Descent Ascent with Finite Timescale Separation

Tanner Fiez, Lillian J Ratliff

Representation Balancing Offline Model-based Reinforcement Learning

Byung-Jun Lee, Jongmin Lee, Kee-eung Kim

MetaNorm: Learning to Normalize Few-Shot Batches Across Domains

Yingjun Du, Xiantong Zhen, Ling Shao, Cees G. M. Snoek

Large-width functional asymptotics for deep Gaussian neural networks

Daniele Bracale, Stefano Favaro, Sandra Fortini, Stefano Peluchetti

Reducing the Computational Cost of Deep Generative Models with Binary Neural Networks

Thomas Bird, Friso Kingma, David Barber

Efficient Conformal Prediction via Cascaded Inference with Expanded Admission

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

Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization

Tatsuya Matsushima, Hiroki Furuta, Yutaka Matsuo, Ofir Nachum, Shixiang Gu

Group Equivariant Conditional Neural Processes

Makoto Kawano, Wataru Kumagai, Akiyoshi Sannai, Yusuke Iwasawa, Yutaka Matsuo

Neural gradients are near-lognormal: improved quantized and sparse training

Brian Chmiel, Liad Ben-Uri, moran shkolnik, Elad Hoffer, Ron Banner

MultiModalQA: complex question answering over text, tables and images

Alon Talmor, Ori Yoran, Amnon Catav, Dan Lahav, Yizhong Wang

BOIL: Towards Representation Change for Few-shot Learning

Jaehoon Oh, Hyungjun Yoo, ChangHwan Kim, Se-Young Yun

Drop-Bottleneck: Learning Discrete Compressed Representation for Noise-Robust Exploration

Jaekyeom Kim, Minjung Kim, Dongyeon Woo, Gunhee Kim

Prediction and generalisation over directed actions by grid cells

Changmin Yu, Timothy Behrens, Neil Burgess

Primal Wasserstein Imitation Learning

Robert Dadashi, Leonard Hussenot, Matthieu Geist, Olivier Pietquin

Understanding the effects of data parallelism and sparsity on neural network training

Namhoon Lee, Thalaiyasingam Ajanthan, Philip Torr, Martin Jaggi

Adapting to Reward Progressivity via Spectral Reinforcement Learning

Michael Dann, John Thangarajah

Identifying Physical Law of Hamiltonian Systems via Meta-Learning

Seungjun Lee, Haesang Yang, Woojae Seong

Estimating informativeness of samples with Smooth Unique Information

Hrayr Harutyunyan, Alessandro Achille, Giovanni Paolini, Orchid Majumder, Avinash Ravichandran

In Search of Lost Domain Generalization

Ishaan Gulrajani, David Lopez-paz

Predicting Classification Accuracy When Adding New Unobserved Classes

Yuli Slavutsky, Yuval Benjamini

Generalized Energy Based Models

Michael Arbel, Liang Zhou, Arthur Gretton

Prototypical Representation Learning for Relation Extraction

Ning Ding, Xiaobin Wang, Yao Fu, Guangwei Xu, Rui Wang

Categorical Normalizing Flows via Continuous Transformations

Phillip Lippe, Efstratios Gavves

Relating by Contrasting: A Data-efficient Framework for Multimodal Generative Models

Yuge Shi, Brooks Paige, Philip Torr, Siddharth N

Neurally Augmented ALISTA

Freya Behrens, Jonathan Sauder, Peter Jung

Acting in Delayed Environments with Non-Stationary Markov Policies

Esther Derman, Gal Dalal, Shie Mannor

Long Range Arena : A Benchmark for Efficient Transformers

Yi Tay, Mostafa Dehghani, Samira Abnar, Yikang Shen, Dara Bahri

HyperGrid Transformers: Towards A Single Model for Multiple Tasks

Yi Tay, Zhe Zhao, Dara Bahri, Donald Metzler, Da-Cheng Juan

Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks

Yige Li, Xixiang Lyu, Nodens Koren, Lingjuan Lyu, Bo Li

PC2WF: 3D Wireframe Reconstruction from Raw Point Clouds

Yujia Liu, Stefano D'Aronco, Konrad Schindler, Jan Dirk Wegner

Meta-Learning of Structured Task Distributions in Humans and Machines

Sreejan Kumar, Ishita Dasgupta, Jonathan Cohen, nathaniel daw, Thomas Griffiths

QPLEX: Duplex Dueling Multi-Agent Q-Learning

Jianhao Wang, Zhizhou Ren, Terry Liu, Yang Yu, Chongjie Zhang

Lifelong Learning of Compositional Structures

Jorge A Mendez, Eric Eaton

Randomized Ensembled Double Q-Learning: Learning Fast Without a Model

Xinyue Chen, Che Wang, Zijian Zhou, Keith W. Ross

Statistical inference for individual fairness

Subha Maity, Songkai Xue, Mikhail Yurochkin, Yuekai Sun

Learning Deep Features in Instrumental Variable Regression

Liyuan Xu, Yutian Chen, Siddarth Srinivasan, Nando de Freitas, Arnaud Doucet

Interpretable Models for Granger Causality Using Self-explaining Neural Networks

Ričards Marcinkevičs, Julia E Vogt

FastSpeech 2: Fast and High-Quality End-to-End Text to Speech

Yi Ren, Chenxu Hu, Xu Tan, Tao Qin, Sheng Zhao

Adversarial score matching and improved sampling for image generation

Alexia Jolicoeur-Martineau, Rémi Piché-Taillefer, Ioannis Mitliagkas, Remi Tachet des Combes

Uncertainty in Gradient Boosting via Ensembles

Andrey Malinin, Liudmila Prokhorenkova, Aleksei Ustimenko

Taming GANs with Lookahead-Minmax

Tatjana Chavdarova, Matteo Pagliardini, Sebastian U Stich, Francois Fleuret, Martin Jaggi

Ask Your Humans: Using Human Instructions to Improve Generalization in Reinforcement Learning

Valerie Chen, Abhinav Gupta, Kenneth Marino

Learning the Pareto Front with Hypernetworks

Aviv Navon, Aviv Shamsian, Ethan Fetaya, Gal Chechik

Learning Neural Generative Dynamics for Molecular Conformation Generation

Minkai Xu, Shitong Luo, Yoshua Bengio, Jian Peng, Jian Tang