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ORCID9th ICLR 2021: Virtual Event, Austria
- 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net 2021
Oral Presentations
- Marcin Andrychowicz, Anton Raichuk, Piotr Stanczyk, Manu Orsini, Sertan Girgin, Raphaël Marinier, Léonard Hussenot, Matthieu Geist, Olivier Pietquin, Marcin Michalski, Sylvain Gelly, Olivier Bachem:
What Matters for On-Policy Deep Actor-Critic Methods? A Large-Scale Study. - Colin Wei, Kendrick Shen, Yining Chen, Tengyu Ma:
Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data. - Dibya Ghosh, Abhishek Gupta, Ashwin Reddy, Justin Fu, Coline Manon Devin, Benjamin Eysenbach, Sergey Levine:
Learning to Reach Goals via Iterated Supervised Learning. - Brenden K. Petersen, Mikel Landajuela, T. Nathan Mundhenk, Cláudio Prata Santiago, Sookyung Kim, Joanne Taery Kim:
Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients. - Atsushi Nitanda, Taiji Suzuki:
Optimal Rates for Averaged Stochastic Gradient Descent under Neural Tangent Kernel Regime. - Shuo Yang, Lu Liu, Min Xu:
Free Lunch for Few-shot Learning: Distribution Calibration. - Mike Gartrell, Insu Han, Elvis Dohmatob, Jennifer Gillenwater, Victor-Emmanuel Brunel:
Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes. - Deniz Oktay, Nick McGreivy, Joshua Aduol, Alex Beatson, Ryan P. Adams:
Randomized Automatic Differentiation. - Luca Weihs, Aniruddha Kembhavi, Kiana Ehsani, Sarah M. Pratt, Winson Han, Alvaro Herrasti, Eric Kolve, Dustin Schwenk, Roozbeh Mottaghi, Ali Farhadi:
Learning Generalizable Visual Representations via Interactive Gameplay. - Huy Tuan Pham, Phan-Minh Nguyen:
Global Convergence of Three-layer Neural Networks in the Mean Field Regime. - Max B. Paulus, Chris J. Maddison, Andreas Krause:
Rao-Blackwellizing the Straight-Through Gumbel-Softmax Gradient Estimator. - Krzysztof Marcin Choromanski, Valerii Likhosherstov, David Dohan, Xingyou Song, Andreea Gane, Tamás Sarlós, Peter Hawkins, Jared Quincy Davis, Afroz Mohiuddin, Lukasz Kaiser, David Benjamin Belanger, Lucy J. Colwell, Adrian Weller:
Rethinking Attention with Performers. - Javier Antorán, Umang Bhatt, Tameem Adel, Adrian Weller, José Miguel Hernández-Lobato:
Getting a CLUE: A Method for Explaining Uncertainty Estimates. - Xiaoxia Wu, Ethan Dyer, Behnam Neyshabur:
When Do Curricula Work? - Durmus Alp Emre Acar, Yue Zhao
, Ramon Matas Navarro, Matthew Mattina, Paul N. Whatmough, Venkatesh Saligrama:
Federated Learning Based on Dynamic Regularization. - Jingfeng Zhang, Jianing Zhu, Gang Niu, Bo Han, Masashi Sugiyama, Mohan S. Kankanhalli:
Geometry-aware Instance-reweighted Adversarial Training. - Jang-Hyun Kim, Wonho Choo, Hosan Jeong, Hyun Oh Song:
Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity. - Mikhail Yurochkin, Yuekai Sun:
SenSeI: Sensitive Set Invariance for Enforcing Individual Fairness. - Jeff Donahue, Sander Dieleman, Mikolaj Binkowski, Erich Elsen, Karen Simonyan:
End-to-end Adversarial Text-to-Speech. - Bo Zhao, Konda Reddy Mopuri, Hakan Bilen:
Dataset Condensation with Gradient Matching. - Ruochen Wang, Minhao Cheng, Xiangning Chen, Xiaocheng Tang, Cho-Jui Hsieh:
Rethinking Architecture Selection in Differentiable NAS. - Muhammad Khalifa, Hady Elsahar, Marc Dymetman:
A Distributional Approach to Controlled Text Generation. - Qiang Zhang, Tete Xiao, Alexei A. Efros, Lerrel Pinto, Xiaolong Wang:
Learning Cross-Domain Correspondence for Control with Dynamics Cycle-Consistency. - Jonathan Gray, Adam Lerer, Anton Bakhtin, Noam Brown:
Human-Level Performance in No-Press Diplomacy via Equilibrium Search. - Avi Singh, Huihan Liu, Gaoyue Zhou, Albert Yu, Nicholas Rhinehart, Sergey Levine:
Parrot: Data-Driven Behavioral Priors for Reinforcement Learning. - Amy Zhang, Rowan Thomas McAllister, Roberto Calandra, Yarin Gal, Sergey Levine:
Learning Invariant Representations for Reinforcement Learning without Reconstruction. - Xingang Pan, Bo Dai, Ziwei Liu, Chen Change Loy, Ping Luo:
Do 2D GANs Know 3D Shape? Unsupervised 3D Shape Reconstruction from 2D Image GANs. - Lizhen Nie, Mao Ye, Qiang Liu, Dan Nicolae:
VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatments. - Suraj Srinivas, François Fleuret:
Rethinking the Role of Gradient-based Attribution Methods for Model Interpretability. - Alexander Richard, Dejan Markovic, Israel D. Gebru, Steven Krenn, Gladstone Alexander Butler, Fernando De la Torre, Yaser Sheikh:
Neural Synthesis of Binaural Speech From Mono Audio. - Zhifeng Kong, Wei Ping, Jiaji Huang, Kexin Zhao, Bryan Catanzaro:
DiffWave: A Versatile Diffusion Model for Audio Synthesis. - Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby:
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. - Matthew Smart, Anton Zilman:
On the mapping between Hopfield networks and Restricted Boltzmann Machines. - Glen Berseth, Daniel Geng, Coline Manon Devin, Nicholas Rhinehart, Chelsea Finn, Dinesh Jayaraman, Sergey Levine:
SMiRL: Surprise Minimizing Reinforcement Learning in Unstable Environments. - John D. Co-Reyes, Yingjie Miao, Daiyi Peng, Esteban Real, Quoc V. Le, Sergey Levine, Honglak Lee, Aleksandra Faust:
Evolving Reinforcement Learning Algorithms. - Xin Yuan, Pedro Henrique Pamplona Savarese, Michael Maire:
Growing Efficient Deep Networks by Structured Continuous Sparsification. - Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, Jifeng Dai:
Deformable DETR: Deformable Transformers for End-to-End Object Detection. - Ian Gemp, Brian McWilliams, Claire Vernade, Thore Graepel:
EigenGame: PCA as a Nash Equilibrium. - Yuan Yin, Vincent Le Guen, Jérémie Donà, Emmanuel de Bézenac, Ibrahim Ayed, Nicolas Thome, Patrick Gallinari:
Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting. - Erik Arakelyan, Daniel Daza, Pasquale Minervini, Michael Cochez:
Complex Query Answering with Neural Link Predictors. - David A. Klindt, Lukas Schott, Yash Sharma, Ivan Ustyuzhaninov, Wieland Brendel, Matthias Bethge, Dylan M. Paiton:
Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding. - Cheng Perng Phoo, Bharath Hariharan:
Self-training For Few-shot Transfer Across Extreme Task Differences. - Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole:
Score-Based Generative Modeling through Stochastic Differential Equations. - Biao Zhang, Ankur Bapna, Rico Sennrich, Orhan Firat:
Share or Not? Learning to Schedule Language-Specific Capacity for Multilingual Translation. - Yuxuan Zhang, Wenzheng Chen, Huan Ling, Jun Gao, Yinan Zhang, Antonio Torralba, Sanja Fidler:
Image GANs meet Differentiable Rendering for Inverse Graphics and Interpretable 3D Neural Rendering. - Keyulu Xu, Mozhi Zhang, Jingling Li, Simon Shaolei Du, Ken-ichi Kawarabayashi, Stefanie Jegelka:
How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks. - Zhengxian Lin, Kin-Ho Lam, Alan Fern:
Contrastive Explanations for Reinforcement Learning via Embedded Self Predictions. - Chenlin Meng, Jiaming Song, Yang Song, Shengjia Zhao, Stefano Ermon:
Improved Autoregressive Modeling with Distribution Smoothing. - Beidi Chen, Zichang Liu, Binghui Peng, Zhaozhuo Xu, Jonathan Lingjie Li, Tri Dao, Zhao Song, Anshumali Shrivastava, Christopher Ré:
MONGOOSE: A Learnable LSH Framework for Efficient Neural Network Training. - Gobinda Saha, Isha Garg, Kaushik Roy:
Gradient Projection Memory for Continual Learning. - Zhiyuan Li, Yi Zhang, Sanjeev Arora:
Why Are Convolutional Nets More Sample-Efficient than Fully-Connected Nets? - Ankit Vani, Max Schwarzer, Yuchen Lu, Eeshan Dhekane, Aaron C. Courville:
Iterated learning for emergent systematicity in VQA. - T. Konstantin Rusch, Siddhartha Mishra:
Coupled Oscillatory Recurrent Neural Network (coRNN): An accurate and (gradient) stable architecture for learning long time dependencies.
Spotlight Presentations
- Zhenyu Liao, Romain Couillet, Michael W. Mahoney:
Sparse Quantized Spectral Clustering. - Binh Tang, David S. Matteson:
Graph-Based Continual Learning. - Marisa Kirisame, Steven Lyubomirsky, Altan Haan, Jennifer Brennan, Mike He, Jared Roesch, Tianqi Chen, Zachary Tatlock:
Dynamic Tensor Rematerialization. - Zirui Wang, Yulia Tsvetkov, Orhan Firat, Yuan Cao:
Gradient Vaccine: Investigating and Improving Multi-task Optimization in Massively Multilingual Models. - Yonggan Fu, Han Guo, Meng Li, Xin Yang, Yining Ding, Vikas Chandra, Yingyan Lin:
CPT: Efficient Deep Neural Network Training via Cyclic Precision. - Ainesh Bakshi, Chiranjib Bhattacharyya, Ravi Kannan, David P. Woodruff, Samson Zhou:
Learning a Latent Simplex in Input Sparsity Time. - Waïss Azizian, Marc Lelarge:
Expressive Power of Invariant and Equivariant Graph Neural Networks. - Tom Zahavy, André Barreto, Daniel J. Mankowitz, Shaobo Hou, Brendan O'Donoghue, Iurii Kemaev, Satinder Singh:
Discovering a set of policies for the worst case reward. - Stephen Tian, Suraj Nair, Frederik Ebert, Sudeep Dasari, Benjamin Eysenbach, Chelsea Finn, Sergey Levine:
Model-Based Visual Planning with Self-Supervised Functional Distances. - Pengfei Chen, Guangyong Chen, Junjie Ye, Jingwei Zhao, Pheng-Ann Heng:
Noise against noise: stochastic label noise helps combat inherent label noise. - Rishabh Agarwal, Marlos C. Machado, Pablo Samuel Castro, Marc G. Bellemare:
Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning. - Zhisheng Xiao, Karsten Kreis, Jan Kautz, Arash Vahdat:
VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models. - Liam Li, Mikhail Khodak, Nina Balcan, Ameet Talwalkar:
Geometry-Aware Gradient Algorithms for Neural Architecture Search. - Talya Eden, Piotr Indyk, Shyam Narayanan, Ronitt Rubinfeld, Sandeep Silwal, Tal Wagner:
Learning-based Support Estimation in Sublinear Time. - Malik Tiomoko, Hafiz Tiomoko Ali, Romain Couillet:
Deciphering and Optimizing Multi-Task Learning: a Random Matrix Approach. - Nicola De Cao, Gautier Izacard, Sebastian Riedel, Fabio Petroni:
Autoregressive Entity Retrieval. - Faruk Ahmed, Yoshua Bengio, Harm van Seijen, Aaron C. Courville:
Systematic generalisation with group invariant predictions. - Max Olan Smith, Thomas Anthony, Michael P. Wellman:
Iterative Empirical Game Solving via Single Policy Best Response. - Da Xu, Yuting Ye, Chuanwei Ruan:
Understanding the role of importance weighting for deep learning. - Aditya Krishna Menon, Sadeep Jayasumana, Ankit Singh Rawat, Himanshu Jain, Andreas Veit, Sanjiv Kumar:
Long-tail learning via logit adjustment. - Guan-Horng Liu, Tianrong Chen, Evangelos A. Theodorou:
DDPNOpt: Differential Dynamic Programming Neural Optimizer. - Yikai Zhang, Songzhu Zheng, Pengxiang Wu, Mayank Goswami, Chao Chen:
Learning with Feature-Dependent Label Noise: A Progressive Approach. - Xinran Wang, Yu Xiang, Jun Gao, Jie Ding:
Information Laundering for Model Privacy. - Rui Zhao, Yang Gao, Pieter Abbeel, Volker Tresp, Wei Xu:
Mutual Information State Intrinsic Control. - Taiji Suzuki, Shunta Akiyama:
Benefit of deep learning with non-convex noisy gradient descent: Provable excess risk bound and superiority to kernel methods. - Linjun Zhang, Zhun Deng, Kenji Kawaguchi, Amirata Ghorbani, James Zou:
How Does Mixup Help With Robustness and Generalization? - Pratyush Maini, Mohammad Yaghini, Nicolas Papernot:
Dataset Inference: Ownership Resolution in Machine Learning. - Alexander Vargo, Fan Zhang, Mikhail Yurochkin, Yuekai Sun:
Individually Fair Gradient Boosting. - Shengyu Zhao, Jonathan Cui, Yilun Sheng, Yue Dong, Xiao Liang, Eric I-Chao Chang, Yan Xu:
Large Scale Image Completion via Co-Modulated Generative Adversarial Networks. - Nicklas Hansen, Rishabh Jangir, Yu Sun, Guillem Alenyà, Pieter Abbeel, Alexei A. Efros, Lerrel Pinto, Xiaolong Wang:
Self-Supervised Policy Adaptation during Deployment. - Pierre Foret, Ariel Kleiner, Hossein Mobahi, Behnam Neyshabur:
Sharpness-aware Minimization for Efficiently Improving Generalization. - Yoav Levine, Barak Lenz, Opher Lieber, Omri Abend, Kevin Leyton-Brown, Moshe Tennenholtz, Yoav Shoham:
PMI-Masking: Principled masking of correlated spans. - Rewon Child:
Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images. - Max Schwarzer, Ankesh Anand, Rishab Goel, R. Devon Hjelm, Aaron C. Courville, Philip Bachman:
Data-Efficient Reinforcement Learning with Self-Predictive Representations. - Xavier Puig, Tianmin Shu, Shuang Li, Zilin Wang, Yuan-Hong Liao, Joshua B. Tenenbaum, Sanja Fidler, Antonio Torralba:
Watch-And-Help: A Challenge for Social Perception and Human-AI Collaboration. - Yu Tian, Jian Ren, Menglei Chai, Kyle Olszewski, Xi Peng, Dimitris N. Metaxas, Sergey Tulyakov:
A Good Image Generator Is What You Need for High-Resolution Video Synthesis. - Siyi Hu, Fengda Zhu, Xiaojun Chang, Xiaodan Liang:
UPDeT: Universal Multi-agent RL via Policy Decoupling with Transformers. - Augustus Odena, Kensen Shi, David Bieber, Rishabh Singh, Charles Sutton, Hanjun Dai:
BUSTLE: Bottom-Up Program Synthesis Through Learning-Guided Exploration. - Yang Bai, Yuyuan Zeng, Yong Jiang, Shu-Tao Xia, Xingjun Ma, Yisen Wang:
Improving Adversarial Robustness via Channel-wise Activation Suppressing. - Ruosong Wang, Dean P. Foster, Sham M. Kakade:
What are the Statistical Limits of Offline RL with Linear Function Approximation? - Hanxun Huang, Xingjun Ma, Sarah Monazam Erfani, James Bailey, Yisen Wang:
Unlearnable Examples: Making Personal Data Unexploitable. - Tobias Pfaff, Meire Fortunato, Alvaro Sanchez-Gonzalez, Peter W. Battaglia:
Learning Mesh-Based Simulation with Graph Networks. - Xiu Su, Shan You, Tao Huang, Fei Wang, Chen Qian, Changshui Zhang, Chang Xu:
Locally Free Weight Sharing for Network Width Search. - Xiuyuan Cheng, Zichen Miao, Qiang Qiu:
Graph Convolution with Low-rank Learnable Local Filters. - Wonseok Jeon, Chen-Yang Su, Paul Barde, Thang Doan, Derek Nowrouzezahrai, Joelle Pineau:
Regularized Inverse Reinforcement Learning. - Michael Sejr Schlichtkrull, Nicola De Cao, Ivan Titov:
Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking. - Nils Lukas, Yuxuan Zhang, Florian Kerschbaum:
Deep Neural Network Fingerprinting by Conferrable Adversarial Examples. - Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno A. Olshausen, Trevor Darrell:
Tent: Fully Test-Time Adaptation by Entropy Minimization. - Nurit Spingarn, Ron Banner, Tomer Michaeli:
GAN "Steerability" without optimization. - Omer Yair, Tomer Michaeli:
Contrastive Divergence Learning is a Time Reversal Adversarial Game. - Xiaoling Hu, Yusu Wang, Fuxin Li, Dimitris Samaras, Chao Chen:
Topology-Aware Segmentation Using Discrete Morse Theory. - Zhen Qin, Le Yan, Honglei Zhuang, Yi Tay, Rama Kumar Pasumarthi, Xuanhui Wang, Michael Bendersky, Marc Najork:
Are Neural Rankers still Outperformed by Gradient Boosted Decision Trees? - Yoshua Bengio, Prateek Gupta, Tegan Maharaj, Nasim Rahaman, Martin Weiss, Tristan Deleu, Eilif Benjamin Müller, Meng Qu, Victor Schmidt, Pierre-Luc St-Charles, Hannah Alsdurf, Olexa Bilaniuk, David L. Buckeridge, Gaétan Marceau-Caron, Pierre Luc Carrier, Joumana Ghosn, Satya Ortiz-Gagne, Christopher J. Pal, Irina Rish, Bernhard Schölkopf, Abhinav Sharma, Jian Tang, Andrew Williams:
Predicting Infectiousness for Proactive Contact Tracing. - Zhuang Liu, Xuanlin Li, Bingyi Kang, Trevor Darrell:
Regularization Matters in Policy Optimization - An Empirical Study on Continuous Control. - Sejun Park, Chulhee Yun, Jaeho Lee, Jinwoo Shin:
Minimum Width for Universal Approximation. - Xinshuai Dong, Anh Tuan Luu, Rongrong Ji, Hong Liu:
Towards Robustness Against Natural Language Word Substitutions. - Kenji Kawaguchi:
On the Theory of Implicit Deep Learning: Global Convergence with Implicit Layers. - Giovanni Paolini, Ben Athiwaratkun, Jason Krone, Jie Ma, Alessandro Achille, Rishita Anubhai, Cícero Nogueira dos Santos, Bing Xiang, Stefano Soatto:
Structured Prediction as Translation between Augmented Natural Languages. - Amartya Sanyal, Puneet K. Dokania, Varun Kanade, Philip H. S. Torr:
How Benign is Benign Overfitting ? - Sanjeevan Ahilan, Peter Dayan:
Correcting experience replay for multi-agent communication. - Taylor Whittington Webb, Ishan Sinha, Jonathan D. Cohen:
Emergent Symbols through Binding in External Memory. - Naoyuki Terashita, Hiroki Ohashi, Yuichi Nonaka, Takashi Kanemaru:
Influence Estimation for Generative Adversarial Networks. - Zhiao Huang, Yuanming Hu, Tao Du, Siyuan Zhou, Hao Su, Joshua B. Tenenbaum, Chuang Gan:
PlasticineLab: A Soft-Body Manipulation Benchmark with Differentiable Physics. - Cheng Lu, Jianfei Chen, Chongxuan Li, Qiuhao Wang, Jun Zhu:
Implicit Normalizing Flows. - Mandela Patrick, Po-Yao Huang, Yuki Markus Asano, Florian Metze, Alexander G. Hauptmann, João F. Henriques, Andrea Vedaldi:
Support-set bottlenecks for video-text representation learning. - Deunsol Yoon, Sunghoon Hong, Byung-Jun Lee, Kee-Eung Kim:
Winning the L2RPN Challenge: Power Grid Management via Semi-Markov Afterstate Actor-Critic. - Nils Wandel, Michael Weinmann, Reinhard Klein:
Learning Incompressible Fluid Dynamics from Scratch - Towards Fast, Differentiable Fluid Models that Generalize. - Elliot Meyerson, Risto Miikkulainen:
The Traveling Observer Model: Multi-task Learning Through Spatial Variable Embeddings. - Felix Hill, Olivier Tieleman, Tamara von Glehn, Nathaniel Wong, Hamza Merzic, Stephen Clark:
Grounded Language Learning Fast and Slow. - Xudong Wang, Long Lian, Zhongqi Miao, Ziwei Liu, Stella X. Yu:
Long-tailed Recognition by Routing Diverse Distribution-Aware Experts. - Florian Tramèr, Dan Boneh:
Differentially Private Learning Needs Better Features (or Much More Data). - Anand Gopalakrishnan, Sjoerd van Steenkiste, Jürgen Schmidhuber:
Unsupervised Object Keypoint Learning using Local Spatial Predictability. - Sebastian Farquhar, Yarin Gal, Tom Rainforth:
On Statistical Bias In Active Learning: How and When to Fix It. - Tolga Ergen, Mert Pilanci:
Implicit Convex Regularizers of CNN Architectures: Convex Optimization of Two- and Three-Layer Networks in Polynomial Time.