ICLRW 2021

119 papers

A Coding Theorem for the Rate-Distortion-Perception Function Lucas Theis, Aaron B. Wagner

A Geometry-Aware Algorithm to Learn Hierarchical Embeddings in Hyperbolic Space Zhangyu Wang, Lantian Xu, Zhifeng Kong, Weilong Wang, Xuyu Peng, Enyang Zheng

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Accelerating Online Reinforcement Learning via Model-Based Meta-Learning John D Co-Reyes, Sarah Feng, Glen Berseth, Jie Qui, Sergey Levine

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An Energy-Based View of Graph Neural Networks John Young Shin, Prathamesh Dharangutte

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Attend to Connect: End-to-End Brain Functional Connectivity Estimation Usman Mahmood, Zening Fu, Vince Calhoun, Sergey Plis

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Augmented World Models Facilitate Zero-Shot Dynamics Generalization from a Single Offline Environment Philip Ball, Cong Lu, Jack Parker-Holder, S Roberts

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AutoGL: A Library for Automated Graph Learning Chaoyu Guan, Ziwei Zhang, Haoyang Li, Heng Chang, Zeyang Zhang, Yijian Qin, Jiyan Jiang, Xin Wang, Wenwu Zhu

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Bayesian Compressed Deep Learning for State Estimation of Unobservable Power Systems Rafael Lima

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Bermuda Triangles: GNNs Fail to Detect Simple Topological Structures Arseny Tolmachev, Akira Sakai, Masaru Todoriki, Koji Maruhashi

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Bootstrapped Representation Learning on Graphs Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, Remi Munos, Petar Veličković, Michal Valko

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Causal Inference Q-Network: Toward Resilient Reinforcement Learning Chao-Han Huck Yang, Danny I-Te Hung, Yi Ouyang, Pin-Yu Chen

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Chemistry-Informed Macromolecule Graph Representation for Similarity Computation and Supervised Learning Somesh Mohapatra, Joyce An, Rafael Gomez-Bombarelli

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COIN: COmpression with Implicit Neural Representations Emilien Dupont, Adam Golinski, Milad Alizadeh, Yee Whye Teh, Arnaud Doucet

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COMBO: Conservative Offline Model-Based Policy Optimization Anonymous

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Compositionality as Learning Bias in Generative RNNs Solves the Omniglot Challenge Sarah Fabi, Sebastian Otte, Martin V. Butz

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Conditional Coding for Flexible Learned Video Compression Théo Ladune, Pierrick Philippe, Wassim Hamidouche, Lu Zhang, Olivier Déforges

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Conjugate Energy-Based Models Hao Wu, Babak Esmaeili, Michael L Wick, Jean-Baptiste Tristan, Jan-Willem van de Meent

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Connecting Convex Energy-Based Inference and Optimal Transport for Domain Adaptation Arip Asadulaev

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Consistent Assignment for Representation Learning Thalles Santos Silva, Adín Ramírez Rivera

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Curating Publications as Artefacts — Exploring Machine Learning Research in an Interactive Virtual Museum Beatrice Gobbo, Mennatallah El-Assady

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Demonstration-Guided Reinforcement Learning with Learned Skills Anonymous

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Diagrammatic Summaries for Neural Architectures Guy Clarke Marshall, Caroline Jay, André Freitas

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Directional Graph Networks Dominique Beaini, Saro Passaro, Vincent Létourneau, William L. Hamilton, Gabriele Corso, Pietro Liò

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Diversity Based Edge Pruning of Neural Networks Using Determinantal Point Processes Rupam Acharyya, Boyu Zhang, Ankani Chattoraj, Shouman Das, Daniel Stefankovic

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Don't Stack Layers in Graph Neural Networks, Wire Them Randomly Diego Valsesia, Giulia Fracastoro, Enrico Magli

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EBMs Trained with Maximum Likelihood Are Generator Models Trained with a Self-Adverserial Loss Zhisheng Xiao, Qing Yan, Yali Amit

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Energy-Based Anomaly Detection and Localization Ergin U Genc, Nilesh Ahuja, Ibrahima J Ndiour, Omesh Tickoo

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Energy-Based Models for Continual Learning Shuang Li, Yilun Du, Gido Martijn van de Ven, Igor Mordatch

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Energy-Based Models for Earth Observation Applications Javiera Castillo Navarro, Bertrand Le Saux, Alexandre Boulch, Sébastien Lefèvre

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Evaluating Representations by the Complexity of Learning Low-Loss Predictors William F Whitney, Min Jae Song, David Brandfonbrener, Jaan Altosaar, Kyunghyun Cho

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Exhibit: Converting My PhD Thesis to HTML Damien Desfontaines

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Exploring Epithelial-Cell Calcium Signaling with Geometric and Topological Data Analysis Feng Gao, Jessica Moore, Bastian Rieck, Valentina Greco, Smita Krishnaswamy

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Exploring the Similarity of Representations in Model-Agnostic Meta-Learning Thomas Goerttler, Klaus Obermayer

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Fairness and Friends Falaah Arif Khan, Eleni Manis, Julia Stoyanovich

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Fast Graph Learning with Unique Optimal Solutions Sami Abu-El-Haija, Valentino Crespi, Greg Ver Steeg, Aram Galstyan

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Few-Shotlearning with Weak Supervision Ali Ghadirzadeh, Petra Poklukar, Xi Chen, Huaxiu Yao, Hossein Azizpour, Mårten Björkman, Chelsea Finn, Danica Kragic

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Geometry Encoding for Numerical Simulations Amir Maleki, Jan Heyse, Rishikesh Ranade, Haiyang He, Priya Kasimbeg, Jay Pathak

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GLAM: Graph Learning by Modeling Affinity to Labeled Nodes for Graph Neural Networks Vijay Lingam, Arun Iyer, Rahul Ragesh

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Graph Autoencoder for Graph Compression and Representation Learning Yunhao Ge, Yunkui Pang, Linwei Li, Laurent Itti

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Graph Energy-Based Model for Molecular Graph Generation Ryuichiro Hataya, Hideki Nakayama, Kazuki Yoshizoe

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GraphEBM: Molecular Graph Generation with Energy-Based Models Meng Liu, Keqiang Yan, Bora Oztekin, Shuiwang Ji

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Grassmann Graph Embedding Bingxin Zhou, Xuebin Zheng, Yu Guang Wang, Ming Li, Junbin Gao

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Hierarchical Image Compression Framework Yunying Ge, Jing Wang, Yibo Shi, Shangyin Gao

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How Sensitive Are Meta-Learners to Dataset Imbalance? Mateusz Ochal, Massimiliano Patacchiola, Jose Manuel Vazquez Diosdado, Amos Storkey, Sen Wang

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Hybrid Mutual Information Lower-Bound Estimators for Representation Learning Abhishek Sinha, Jiaming Song, Stefano Ermon

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Importance Weighted Compression Lucas Theis, Jonathan Ho

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Improved Contrastive Divergence Training of Energy Based Models Yilun Du, Shuang Li, Joshua B. Tenenbaum, Igor Mordatch

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Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding Yangjun Ruan, Karen Ullrich, Daniel Severo, James Townsend, Ashish J Khisti, Arnaud Doucet, Alireza Makhzani, Chris J. Maddison

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In Defense of the Paper Owen Lockwood

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LDLE: Low Distortion Local Eigenmaps Dhruv Kohli, Alex Cloninger, Gal Mishne

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Learned Transform Compression with Optimized Entropy Encoding Magda Gregorova, Marc Desaules, Alexandros Kalousis

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Learning Generalizable Robotic Reward Functions from "In-the-Wild" Human Videos Anonymous

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Learning One Representation to Optimize All Rewards Anonymous

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Learning State Representations via Temporal Cycle-Consistency Constraint in Model-Based Reinforcement Learning Anonymous

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Learning Task Informed Abstractions Anonymous

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Learning to Explore a Class of Multiple Reward-Free Environments Anonymous

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Learning to Infer Unseen Contexts in Causal Contextual Reinforcement Learning Anonymous

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Learning Where to Learn Dominic Zhao, Nicolas Zucchet, Joao Sacramento, Johannes von Oswald

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Less Suboptimal Learning and Control in Variational POMDPs Anonymous

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LIDL: Local Intrinsic Dimension Estimation Using Approximate Likelihood Piotr Tempczyk, Adam Golinski, Przemysław Spurek, Jacek Tabor

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LOCO: Adaptive Exploration in Reinforcement Learning via Local Estimation of Contraction Coefficients Anonymous

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Lossless Compression and Generalization in Overparameterized Models: The Case of Boosting Nikolaos Nikolaou

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Lossless Compression Using Continuously-Indexed Normalizing Flows Adam Golinski, Anthony L. Caterini

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Lossless Compression with State Space Models Using Bits Back Coding James Townsend, Iain Murray

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Lossy Compression for Lossless Prediction Yann Dubois, Benjamin Bloem-Reddy, Karen Ullrich, Chris J. Maddison

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Lossy Image Compression with Normalizing Flows Leonhard Helminger, Abdelaziz Djelouah, Markus Gross, Christopher Schroers

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Low-Rank Projections of GCNs Laplacian Nathan Grinsztajn, Philippe Preux, Edouard Oyallon

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Lower Bounding Rate-Distortion from Samples Yibo Yang, Stephan Mandt

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Meta Learning for Multi-Agent Communication Abhinav Gupta, Angeliki Lazaridou, Marc Lanctot

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Meta-Learning for Planning: Automatic Synthesis of Sample Based Planners Lucas Paul Saldyt, Heni Amor

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Meta-Learning Using Privileged Information for Dynamics Ben Day, Alexander Luke Ian Norcliffe, Jacob Moss, Pietro Liò

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Minimum Description Length Skills for Accelerated Reinforcement Learning Anonymous

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Model-Invariant State Abstractions for Model-Based Reinforcement Learning Anonymous

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Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment Michael Chang, Sidhant Kaushik, Thomas L. Griffiths, Sergey Levine

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ModulOM: Disseminating Deep Learning Research with Modular Output Mathematics Maxime Istasse, Kim Mens, Christophe De Vleeschouwer

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Neural Data Compression for Physics Plasma Simulation Jong Choi, Michael Churchill, Qian Gong, Seung-Hoe Ku, Jaemoon Lee, Anand Rangarajan, Sanjay Ranka, Dave Pugmire, Cs Chang, Scott Klasky

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No Conditional Models for Me: Training Joint EBMs on Mixed Continuous and Discrete Data Jacob Kelly, Will Sussman Grathwohl

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Offline Meta Learning of Exploration Ron Dorfman, Idan Shenfeld, Aviv Tamar

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Offline Reinforcement Learning with Pseudometric Learning Anonymous

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On Augmenting the References Section with a Citation Network Visualization Putra Manggala, Tigran Atoyan, Gracia Samosir, Jan Varsava, Johannes Ruf

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On Feature Diversity in Energy-Based Models Firas Laakom, Jenni Raitoharju, Alexandros Iosifidis, Moncef Gabbouj

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On Linear Interpolation in the Latent Space of Deep Generative Models Mike Yan Michelis, Quentin Becker

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On the Advantages of Stochastic Encoders Lucas Theis, Eirikur Agustsson

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On the Stability of Graph Convolutional Neural Networks Under Edge Rewiring Henry Kenlay, Dorina Thanou, Xiaowen Dong

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Online High-Dimensional Change-Point Detection Using Topological Data Analysis Xiaojun Zheng, Simon Mak, Yao Xie

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Optimism Is All You Need: Model-Based Imitation Learning from Observation Alone Anonymous

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Out-of-Distribution Generalization of Internal Models Is Correlated with Reward Anonymous

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PAC-Bayes and Information Complexity Pradeep Kr. Banerjee, Guido Montufar

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Persistent Message Passing Heiko Strathmann, Mohammadamin Barekatain, Charles Blundell, Petar Veličković

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Pretraining Reward-Free Representations for Data-Efficient Reinforcement Learning Anonymous

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PsiPhi-Learning: Reinforcement Learning with Demonstrations Using Successor Features and Inverse Temporal Difference Learning Anonymous

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Reconstruction of Pairwise Interactions Using Energy-Based Models Carlo Lucibello, Christoph Feinauer

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Recovering Barabási-ALBERT Parameters of Graphs Through Disentanglement Cristina Guzmán, Daphna Keidar, Tristan Meynier, Andreas Opedal, Niklas Stoehr

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Redundant Information Neural Estimation Michael Kleinman, Alessandro Achille, Stefano Soatto, Jonathan Kao

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Reinforcement Learning with Prototypical Representations Denis Yarats, Rob Fergus, Alessandro Lazaric, Lerrel Pinto

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Relevant Action Matters : Motivating Agent with Action Usefulness Anonymous

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Representation Matters: Offline Pretraining for Sequential Decision Making Anonymous

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Resolving Causal Confusion in Reinforcement Learning via Robust Exploration Anonymous

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Rissanen Data Analysis: Examining Dataset Characteristics via Description Length Ethan Perez, Douwe Kiela, Kyunghyun Cho

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RRL: ResNet as Representation for Reinforcement Learning Anonymous

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Sanity Check for Persistence Diagrams Chen Cai

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Scale Space Flow with Autoregressive Priors Ruihan Yang, Yibo Yang, Joseph Marino, Stephan Mandt

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Scaling up Graph Homomorphism Features with Efficient Data Structures Paul Beaujean, Florian Sikora, Florian Yger

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Self-Supervised Exploration via Latent Bayesian Surprise Anonymous

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Self-Supervised Representation Learning on Manifolds Eric O Korman

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Should EBMs Model the Energy or the Score? Tim Salimans, Jonathan Ho

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Simplicial Regularization Jose Gallego-Posada, Patrick Forré

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Single Layers of Attention Suffice to Predict Protein Contacts Nick Bhattacharya, Neil Thomas, Roshan Rao, Justas Dauparas, Peter K Koo, David Baker, Yun S. Song, Sergey Ovchinnikov

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Solipsistic Reinforcement Learning Anonymous

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Spices: Survey Papers as Interactive Cheatsheet Embeddings Vinay Uday Prabhu, Matthew McAteer, Ryan Teehan

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State Entropy Maximization with Random Encoders for Efficient Exploration Anonymous

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The Emergence of Abstract and Episodic Neurons in Episodic Meta-RL Badr AlKhamissi, Muhammad ElNokrashy, Michael Spranger

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Training and Generating Neural Networks in Compressed Weight Space Kazuki Irie, Jürgen Schmidhuber

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Understanding Diversity Based Neural Network Pruning in Teacher Student Setup Rupam Acharyya, Ankani Chattoraj, Boyu Zhang, Shouman Das, Daniel Stefankovic

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Universal Rate-Distortion-Perception Representations for Lossy Compression George Zhang, Jun Chen, Ashish J Khisti

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Unsupervised Feature Learning for Manipulation with Contrastive Domain Randomization Anonymous

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Unsupervised Geometric Disentanglement via CFAN-VAE Norman Joseph Tatro, Stefan C Schonsheck, Rongjie Lai

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Variational Model-Based Imitation Learning in High-Dimensional Observation Spaces Anonymous

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Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks Cristian Bodnar, Fabrizio Frasca, Yu Guang Wang, Nina Otter, Guido Montufar, Pietro Liò, Michael M. Bronstein

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