ICMLW 2022
261 papers
A Study of Causal Confusion in Preference-Based Reward Learning
Jeremy Tien, Jerry Zhi-Yang He, Zackory Erickson, Anca Dragan, Daniel S. Brown Adaptive Interest for Emphatic Reinforcement Learning
Martin Klissarov, Rasool Fakoor, Jonas Mueller, Kavosh Asadi, Taesup Kim, Alex Smola Adversarial Cheap Talk
Chris Lu, Timon Willi, Alistair Letcher, Jakob Nicolaus Foerster An Investigation into the Open World Survival Game Crafter
Aleksandar Stanić, Yujin Tang, David Ha, Jürgen Schmidhuber BARACK: Partially Supervised Group Robustness with Guarantees
Nimit Sharad Sohoni, Maziar Sanjabi, Nicolas Ballas, Aditya Grover, Shaoliang Nie, Hamed Firooz, Christopher Re Bridging the Training-Inference Gap for Dense Phrase Retrieval
Gyuwan Kim, Jinhyuk Lee, Barlas Oguz, Wenhan Xiong, Yizhe Zhang, Yashar Mehdad, William Yang Wang Building a Subspace of Policies for Scalable Continual Learning
Jean-Baptiste Gaya, Thang Doan, Lucas Caccia, Laure Soulier, Ludovic Denoyer, Roberta Raileanu Causal Balancing for Domain Generalization
Xinyi Wang, Michael Saxon, Jiachen Li, Hongyang Zhang, Kun Zhang, William Yang Wang CCC: Continuously Changing Corruptions
Ori Press, Steffen Schneider, Matthias Kuemmerer, Matthias Bethge Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations
Cong Lu, Philip J. Ball, Tim G. J. Rudner, Jack Parker-Holder, Michael A Osborne, Yee Whye Teh Characterizing Datapoints via Second-Split Forgetting
Pratyush Maini, Saurabh Garg, Zachary Chase Lipton, J Zico Kolter Classifiers Should Do Well Even on Their Worst Classes
Julian Bitterwolf, Alexander Meinke, Valentyn Boreiko, Matthias Hein Curvature-Informed Multi-Task Learning for Graph Networks
Alexander New, Michael J Pekala, Nam Q Le, Janna Domenico, Christine D. Piatko, Christopher D Stiles DAFT: Distilling Adversarially Fine-Tuned Teachers for OOD Robustness
Anshul Nasery, Sravanti Addepalli, Praneeth Netrapalli, Prateek Jain Dialog Inpainting: Turning Documents into Dialogs
Zhuyun Dai, Arun Tejasvi Chaganty, Vincent Y Zhao, Aida Amini, Mike Green, Qazi Mamunur Rashid, Kelvin Guu Differentiable Agent-Based Epidemiological Modeling for End-to-End Learning
Ayush Chopra, Alexander Rodríguez, Jayakumar Subramanian, Balaji Krishnamurthy, B. Aditya Prakash, Ramesh Raskar Directed Exploration via Uncertainty-Aware Critics
Amarildo Likmeta, Matteo Sacco, Alberto Maria Metelli, Marcello Restelli Discovered Policy Optimisation
Chris Lu, Jakub Grudzien Kuba, Alistair Letcher, Luke Metz, Christian Schroeder de Witt, Jakob Nicolaus Foerster Distributionally Adaptive Meta Reinforcement Learning
Anurag Ajay, Dibya Ghosh, Sergey Levine, Pulkit Agrawal, Abhishek Gupta Domain Adaptation Under Open Set Label Shift
Saurabh Garg, Sivaraman Balakrishnan, Zachary Chase Lipton Doubly Right Object Recognition
Revant Teotia, Chengzhi Mao, Carl Vondrick Efficient Continuous Spatio-Temporal Simulation with Graph Spline Networks
Chuanbo Hua, Federico Berto, Michael Poli, Stefano Massaroli, Jinkyoo Park Enhancing Multi-Hop Connectivity for Graph Convolutional Networks
Songtao Liu, Shixiong Jing, Tong Zhao, Zengfeng Huang, Dinghao Wu Enhancing Unit-Tests for Invariance Discovery
Piersilvio De Bartolomeis, Antonio Orvieto, Giambattista Parascandolo Evaluating Self-Supervised Learned Molecular Graphs
Hanchen Wang, Shengchao Liu, Jean Kaddour, Qi Liu, Jian Tang, Matt Kusner, Joan Lasenby Evaluating Self-Supervised Learned Molecular Graphs
Hanchen Wang, Shengchao Liu, Jean Kaddour, Qi Liu, Jian Tang, Matt Kusner, Joan Lasenby Evaluating Systemic Error Detection Methods Using Synthetic Images
Gregory Plumb, Nari Johnson, Ángel Cabrera, Marco Tulio Ribeiro, Ameet Talwalkar Exploration in Reward Machines with Low Regret
Hippolyte Bourel, Anders Jonsson, Odalric-Ambrym Maillard, Mohammad Sadegh Talebi Exploring Long-Horizon Reasoning with Deep RL in Combinatorially Hard Tasks
Andrew C Li, Pashootan Vaezipoor, Rodrigo Toro Icarte, Sheila A. McIlraith Fast Convergence for Unstable Reinforcement Learning Problems by Logarithmic Mapping
Wang Zhang, Lam M. Nguyen, Subhro Das, Alexandre Megretski, Luca Daniel, Tsui-Wei Weng GAUCHE: A Library for Gaussian Processes in Chemistry
Ryan-Rhys Griffiths, Leo Klarner, Henry Moss, Aditya Ravuri, Sang T. Truong, Bojana Rankovic, Yuanqi Du, Arian Rokkum Jamasb, Julius Schwartz, Austin Tripp, Gregory Kell, Anthony Bourached, Alex Chan, Jacob Moss, Chengzhi Guo, Alpha Lee, Philippe Schwaller, Jian Tang Generative Self-Training Improves Pre-Training for Visual Dialog
Gi-Cheon Kang, Sungdong Kim, Jin-Hwa Kim, Donghyun Kwak, Byoung-Tak Zhang Giving Feedback on Interactive Student Programs with Meta-Exploration
Evan Zheran Liu, Moritz Pascal Stephan, Allen Nie, Christopher J Piech, Emma Brunskill, Chelsea Finn Goal-Conditioned Generators of Deep Policies
Francesco Faccio, Vincent Herrmann, Aditya Ramesh, Louis Kirsch, Jürgen Schmidhuber Graphein - A Python Library for Geometric Deep Learning and Network Analysis on Biomolecular Structures and Interaction Networks
Arian Rokkum Jamasb, Ramon Viñas Torné, Eric J Ma, Yuanqi Du, Charles Harris, Kexin Huang, Dominic Hall, Pietro Lio, Tom Leon Blundell Growing ObjectNet: Adding Speech, VQA, Occlusion, and Measuring Dataset Difficulty
David Mayo, David Lu, Chris Zhang, Jesse Cummings, Xinyu Lin, Boris Katz, James R. Glass, Andrei Barbu How Much Data Is Augmentation Worth?
Jonas Geiping, Gowthami Somepalli, Ravid Shwartz-Ziv, Andrew Gordon Wilson, Tom Goldstein, Micah Goldblum How Robust Are Pre-Trained Models to Distribution Shift?
Yuge Shi, Imant Daunhawer, Julia E Vogt, Philip Torr, Amartya Sanyal How Robust Are Pre-Trained Models to Distribution Shift? Yuge Shi, Imant Daunhawer, Julia E Vogt, Philip Torr, Amartya Sanyal How Well Do Contrastively Trained Models Transfer?
M. Moein Shariatnia, Rahim Entezari, Mitchell Wortsman, Olga Saukh, Ludwig Schmidt Huge Frozen Language Models as Readers for Open-Domain Question Answering
Yoav Levine, Ori Ram, Daniel Jannai, Barak Lenz, Shai Shalev-Shwartz, Amnon Shashua, Kevin Leyton-Brown, Yoav Shoham Hyper-Representation for Pre-Training and Transfer Learning
Konstantin Schürholt, Boris Knyazev, Xavier Giró-i-Nieto, Damian Borth HyperInvariances: Amortizing Invariance Learning
Ruchika Chavhan, Henry Gouk, Jan Stuehmer, Timothy Hospedales ImageNet-D: A New Challenging Robustness Dataset Inspired by Domain Adaptation
Evgenia Rusak, Steffen Schneider, Peter Vincent Gehler, Oliver Bringmann, Wieland Brendel, Matthias Bethge ImageNet-Patch: A Dataset for Benchmarking Machine Learning Robustness Against Adversarial Patches
Maura Pintor, Daniele Angioni, Angelo Sotgiu, Luca Demetrio, Ambra Demontis, Battista Biggio, Fabio Roli Improving Group-Based Robustness and Calibration via Ordered Risk and Confidence Regularization
Seungjae Shin, Byeonghu Na, HeeSun Bae, JoonHo Jang, Hyemi Kim, Kyungwoo Song, Youngjae Cho, Il-chul Moon Inferring Relationship Using Theory of Mind in Press Diplomacy
Hyeonchang Jeon, Songmi Oh, Wonsang You, Hoyoun Jung, Kyung-Joong Kim Invariance Discovery for Systematic Generalization in Reinforcement Learning
Mirco Mutti, Riccardo De Santi, Emanuele Rossi, Juan Felipe Calderon, Michael M. Bronstein, Marcello Restelli Invariance Principle Meets Out-of-Distribution Generalization on Graphs
Yongqiang Chen, Yonggang Zhang, Yatao Bian, Han Yang, Ma Kaili, Binghui Xie, Tongliang Liu, Bo Han, James Cheng Knowledge Distillation for Efficient Sequences of Training Runs
Xingyu Liu, Alexander Leonardi, Lu Yu, Christopher Gilmer-Hill, Matthew L Leavitt, Jonathan Frankle Large Language Models Are Zero-Shot Reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, Yusuke Iwasawa LAST: Latent Space Assisted Adaptive Sampling for Protein Trajectories
Hao Tian, Xi Jiang, Sian Xiao, Hunter La Force, Eric Larson, Peng Tao Latent Variable Models for Bayesian Causal Discovery
Jithendaraa Subramanian, Yashas Annadani, Ivaxi Sheth, Stefan Bauer, Derek Nowrouzezahrai, Samira Ebrahimi Kahou Learning Debiased Classifier with Biased Committee
Nayeong Kim, Sehyun Hwang, Sungsoo Ahn, Jaesik Park, Suha Kwak Learning to Induce Causal Structure
Nan Rosemary Ke, Silvia Chiappa, Jane X Wang, Jorg Bornschein, Anirudh Goyal, Melanie Rey, Matthew Botvinick, Theophane Weber, Michael Curtis Mozer, Danilo Jimenez Rezende Linear Connectivity Reveals Generalization Strategies
Jeevesh Juneja, Rachit Bansal, Kyunghyun Cho, João Sedoc, Naomi Saphra MAgNet: Mesh Agnostic Neural PDE Solver
Oussama Boussif, Dan Assouline, Loubna Benabbou, Yoshua Bengio Memorization in NLP Fine-Tuning Methods
Fatemehsadat Mireshghallah, Archit Uniyal, Tianhao Wang, David Evans, Taylor Berg-Kirkpatrick MoCoDA: Model-Based Counterfactual Data Augmentation
Silviu Pitis, Elliot Creager, Ajay Mandlekar, Animesh Garg Model-Based Meta Automatic Curriculum Learning
Zifan Xu, Yulin Zhang, Shahaf S. Shperberg, Reuth Mirsky, Yuqian Jiang, Bo Liu, Peter Stone Model-Based Reinforcement Learning with SINDy
Rushiv Arora, Eliot Moss, Bruno Castro da Silva Monitoring Shortcut Learning Using Mutual Information
Mohammed Adnan, Yani Ioannou, Kenyon Tsai, Angus Galloway, Hamid Tizhoosh, Graham W. Taylor Multimodal Masked Autoencoders Learn Transferable Representations
Xinyang Geng, Hao Liu, Lisa Lee, Dale Schuurmans, Sergey Levine, Pieter Abbeel MultiScale MeshGraphNets
Meire Fortunato, Tobias Pfaff, Peter Wirnsberger, Alexander Pritzel, Peter Battaglia Multiscale Neural Operator: Learning Fast and Grid-Independent PDE Solvers
Björn Lütjens, Catherine H. Crawford, Campbell D Watson, Christopher Hill, Dava Newman Neuro-Symbolic Language Modeling with Automaton-Augmented Retrieval
Uri Alon, Frank F. Xu, Junxian He, Sudipta Sengupta, Dan Roth, Graham Neubig On Combining Global and Localized Self-Supervised Models of Speech
Sri Harsha Dumpala, Chandramouli Shama Sastry, Rudolf Uher, Sageev Oore On the Generalization and Adaption Performance of Causal Models
Nino Scherrer, Anirudh Goyal, Stefan Bauer, Yoshua Bengio, Nan Rosemary Ke On the Importance of Hyperparameters and Data Augmentation for Self-Supervised Learning
Diane Wagner, Fabio Ferreira, Danny Stoll, Robin Tibor Schirrmeister, Samuel Müller, Frank Hutter On the Subspace Structure of Gradient-Based Meta-Learning
Gustaf Tegnér, Alfredo Reichlin, Hang Yin, Mårten Björkman, Danica Kragic Jensfelt One-Shot Transfer Learning of Physics-Informed Neural Networks
Shaan Desai, Marios Mattheakis, Hayden Joy, Pavlos Protopapas, Stephen J. Roberts OOD-CV: A Benchmark for Robustness to Individual Nuisances in Real-World Out-of-Distribution Shifts
Bingchen Zhao, Shaozuo Yu, Wufei Ma, Mingxin Yu, Shenxiao Mei, Angtian Wang, Ju He, Alan Yuille, Adam Kortylewski Optimizing Maintenance by Learning Individual Treatment Effects
Toon Vanderschueren, Robert Boute, Tim Verdonck, Bart Baesens, Wouter Verbeke Path Integral Stochastic Optimal Control for Sampling Transition Paths
Lars Holdijk, Yuanqi Du, Priyank Jaini, Ferry Hooft, Bernd Ensing, Max Welling PGT: A Prompt Based Generative Transformer for the Patent Domain
Dimitrios Christofidellis, Antonio Berrios Torres, Ashish Dave, Manuel Roveri, Kristin Schmidt, Sarath Swaminathan, Hans Vandierendonck, Dmitry Zubarev, Matteo Manica Plex: Towards Reliability Using Pretrained Large Model Extensions
Dustin Tran, Jeremiah Zhe Liu, Michael W Dusenberry, Du Phan, Mark Collier, Jie Ren, Kehang Han, Zi Wang, Zelda E Mariet, Huiyi Hu, Neil Band, Tim G. J. Rudner, Karan Singhal, Zachary Nado, Joost van Amersfoort, Andreas Kirsch, Rodolphe Jenatton, Nithum Thain, Honglin Yuan, E. Kelly Buchanan, Kevin Patrick Murphy, D. Sculley, Yarin Gal, Zoubin Ghahramani, Jasper Snoek, Balaji Lakshminarayanan Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Prior
Ravid Shwartz-Ziv, Micah Goldblum, Hossein Souri, Sanyam Kapoor, Chen Zhu, Yann LeCun, Andrew Gordon Wilson Pre-Trained Image Encoder for Generalizable Visual Reinforcement Learning
Zhecheng Yuan, Zhengrong Xue, Bo Yuan, Xueqian Wang, Yi Wu, Yang Gao, Huazhe Xu Pre-Training on a Data Diet: Identifying Sufficient Examples for Early Training
Mansheej Paul, Brett W Larsen, Surya Ganguli, Jonathan Frankle, Gintare Karolina Dziugaite Predicting Human Similarity Judgments Using Large Language Models
Raja Marjieh, Ilia Sucholutsky, Theodore Sumers, Nori Jacoby, Thomas L. Griffiths Protein Representation Learning by Geometric Structure Pretraining
Zuobai Zhang, Minghao Xu, Arian Rokkum Jamasb, Vijil Chenthamarakshan, Aurelie Lozano, Payel Das, Jian Tang PSP-HDRI$+$: A Synthetic Dataset Generator for Pre-Training of Human-Centric Computer Vision Models
Salehe Erfanian Ebadi, Saurav Dhakad, Sanjay Vishwakarma, Chunpu Wang, You-Cyuan Jhang, Maciek Chociej, Adam Crespi, Alex Thaman, Sujoy Ganguly Pushing the Limits of Self-Supervised ResNets: Can We Outperform Supervised Learning Without Labels on ImageNet?
Nenad Tomasev, Ioana Bica, Brian McWilliams, Lars Holger Buesing, Razvan Pascanu, Charles Blundell, Jovana Mitrovic SCONER: Scoring Negative Candidates\\Before Training Neural Re-Ranker for Question Answering
Man Luo, Mihir Parmar, Jayasurya Sevalur Mahendran, Sahit Jain, Samarth Rawal, Chitta Baral SelecMix: Debiased Learning by Mixing up Contradicting Pairs
Inwoo Hwang, Sangjun Lee, Yunhyeok Kwak, Seong Joon Oh, Damien Teney, Jin-Hwa Kim, Byoung-Tak Zhang Self-Referential Meta Learning
Louis Kirsch, Jürgen Schmidhuber SI-Score: An Image Dataset for Fine-Grained Analysis of Robustness to Object Location, Rotation and Size
Jessica Yung, Rob Romijnders, Alexander Kolesnikov, Lucas Beyer, Josip Djolonga, Neil Houlsby, Sylvain Gelly, Mario Lucic, Xiaohua Zhai Task Factorization in Curriculum Learning
Reuth Mirsky, Shahaf S. Shperberg, Yulin Zhang, Zifan Xu, Yuqian Jiang, Jiaxun Cui, Peter Stone The Importance of Background Information for Out of Distribution Generalization
Jupinder Parmar, Khaled Kamal Saab, Brian Pogatchnik, Daniel Rubin, Christopher Ré The Semantic Shift Benchmark
Sagar Vaze, Kai Han, Andrea Vedaldi, Andrew Zisserman Towards Better Understanding of Self-Supervised Representations
Neha Mukund Kalibhat, Kanika Narang, Hamed Firooz, Maziar Sanjabi, Soheil Feizi Towards Domain Adversarial Methods to Mitigate Texture Bias
Dhruva Kashyap, Sumukh K Aithal, Rakshith C, Natarajan Subramanyam Towards Group Robustness in the Presence of Partial Group Labels
Vishnu Suresh Lokhande, Kihyuk Sohn, Jinsung Yoon, Madeleine Udell, Chen-Yu Lee, Tomas Pfister Towards Learning Self-Organized Criticality of Rydberg Atoms Using Graph Neural Networks
Simon Ohler, Daniel Steven Brady, Winfried Lötzsch, Michael Fleischhauer, Johannes Otterbach Towards Multi-Level Fairness and Robustness on Federated Learning
Fengda Zhang, Kun Kuang, Yuxuan Liu, Long Chen, Jiaxun Lu, Yunfeng Shao, Fei Wu, Chao Wu, Jun Xiao Transform Once: Efficient Operator Learning in Frequency Domain
Michael Poli, Stefano Massaroli, Federico Berto, Jinkyoo Park, Tri Dao, Christopher Re, Stefano Ermon Unsupervised Causal Generative Understanding of Images
Titas Anciukevičius, Patrick Fox-Roberts, Edward Rosten, Paul Henderson Unsupervised Learning Under Latent Label Shift
Pranav Mani, Manley Roberts, Saurabh Garg, Zachary Chase Lipton Unsupervised Model-Based Pre-Training for Data-Efficient Reinforcement Learning from Pixels
Sai Rajeswar, Pietro Mazzaglia, Tim Verbelen, Alexandre Piché, Bart Dhoedt, Aaron Courville, Alexandre Lacoste Variational Inference for Soil Biogeochemical Models
Debora Sujono, Hua Wally Xie, Steven Allison, Erik B. Sudderth Weakly Supervised Inversion of Multi-Physics Data for Geophysical Properties
Shihang Feng, Peng Jin, Yinpeng Chen, Xitong Zhang, Zicheng Liu, David Alumbaugh, Michael Commer, Youzuo Lin What Do We Maximize in Self-Supervised Learning?
Ravid Shwartz-Ziv, Randall Balestriero, Yann LeCun When Does Dough Become a bagel?Analyzing the Remaining Mistakes on ImageNet
Vijay Vasudevan, Benjamin Caine, Raphael Gontijo-Lopes, Sara Fridovich-Keil, Rebecca Roelofs Wild-Time: A Benchmark of In-the-Wild Distribution Shift over Time
Huaxiu Yao, Caroline Choi, Yoonho Lee, Pang Wei Koh, Chelsea Finn