NeurIPSW 2021
596 papers
$\textit{Ab Initio}$ Discovery of Biological Knowledge from scRNA-Seq Data Using Machine Learning
Najeebullah Shah, Jiaqi Li, Fanhong Li, Wenchang Chen, Haoxiang Gao, Sijie Chen, Kui Hua, Xuegong Zhang 3D Infomax Improves GNNs for Molecular Property Prediction
Hannes Stärk, Dominique Beaini, Gabriele Corso, Prudencio Tossou, Christian Dallago, Stephan Günnemann, Pietro Lio A Benchmark with Decomposed Distribution Shifts for 360 Monocular Depth Estimation
Georgios Nikolaos Albanis, Nikolaos Zioulis, Petros Drakoulis, Federico Alvarez, Dimitrios Zarpalas, Petros Daras A Binded VAE for Inorganic Material Generation
Fouad Oubari, Antoine de Mathelin, Rodrigue Décatoire, Mathilde Mougeot A Closer Look at Distribution Shifts and Out-of-Distribution Generalization on Graphs
Mucong Ding, Kezhi Kong, Jiuhai Chen, John Kirchenbauer, Micah Goldblum, David Wipf, Furong Huang, Tom Goldstein A Fine-Grained Analysis of Robustness to Distribution Shifts
Olivia Wiles, Sven Gowal, Florian Stimberg, Sylvestre-Alvise Rebuffi, Ira Ktena, Krishnamurthy Dj Dvijotham, Ali Taylan Cemgil A Framework for Efficient Robotic Manipulation
Albert Zhan, Ruihan Zhao, Lerrel Pinto, Pieter Abbeel, Michael Laskin A Fresh Look at De Novo Molecular Design Benchmarks
Austin Tripp, Gregor N. C. Simm, José Miguel Hernández-Lobato A Generalized and Distributable Generative Model for Private Representation Learning
Sheikh Shams Azam, Taejin Kim, Seyyedali Hosseinalipour, Carlee Joe-Wong, Saurabh Bagchi, Christopher Brinton A Joint Exponential Mechanism for Differentially Private Top-K Set
Andres Munoz Medina, Matthew Joseph, Jennifer Gillenwater, Mónica Ribero A Nested Bi-Level Optimization Framework for Robust Few Shot Learning
Krishnateja Killamsetty, Changbin Li, Chen Zhao, Feng Chen, Rishabh K Iyer A Novel Self-Distillation Architecture to Defeat Membership Inference Attacks
Xinyu Tang, Saeed Mahloujifar, Liwei Song, Virat Shejwalkar, Milad Nasr, Amir Houmansadr, Prateek Mittal A Search Engine for Discovery of Scientific Challenges and Directions
Dan Lahav, Jon Saad-Falcon, Bailey Kuehl, Sophie Johnson, Sravanthi Parasa, Noam Shomron, Duen Horng Chau, Diyi Yang, Eric Horvitz, Daniel S Weld, Tom Hope Accurate Imputation and Efficient Data Acquisitionwith Transformer-Based VAEs
Sarah Lewis, Tatiana Matejovicova, Yingzhen Li, Angus Lamb, Yordan Zaykov, Miltiadis Allamanis, Cheng Zhang Adaptive Pseudo-Labeling for Quantum Calculations
Kexin Huang, Vishnu Sresht, Brajesh Rai, Mykola Bordyuh Addressing Bias in Active Learning with Depth Uncertainty Networks... or Not
Chelsea Murray, James Urquhart Allingham, Javier Antoran, José Miguel Hernández-Lobato An Automatic Differentiation System for the Age of Differential Privacy
Dmitrii Usynin, Alexander Ziller, Moritz Knolle, Daniel Rueckert, Georgios Kaissis An Empirical Study of Pre-Trained Vision Models on Out-of-Distribution Generalization
Yaodong Yu, Heinrich Jiang, Dara Bahri, Hossein Mobahi, Seungyeon Kim, Ankit Singh Rawat, Andreas Veit, Yi Ma Are Convolutional Networks Inherently Foveated?
Bilal Alsallakh, Vivek Miglani, Narine Kokhlikyan, David Adkins, Orion Reblitz-Richardson Are Transformers All That Karel Needs?
Abhay Garg, Anand Sriraman, Kunal Pagarey, Shirish Karande Automatic Curricula via Expert Demonstrations
Siyu Dai, Andreas Hofmann, Brian C. Williams Avoiding Spurious Correlations: Bridging Theory and Practice
Thao Nguyen, Vaishnavh Nagarajan, Hanie Sedghi, Behnam Neyshabur Bayesian Exploration for Lifelong Reinforcement Learning
Haotian Fu, Shangqun Yu, Michael Littman, George Konidaris Bayesian Optimal Experimental Design for Simulator Models of Cognition
Simon Valentin, Steven Kleinegesse, Neil R. Bramley, Michael U. Gutmann, Christopher G. Lucas BEDS-Bench: Behavior of EHR-Models Under Distributional Shift - A Benchmark
Anand Avati, Martin Seneviratne, Yuan Xue, Zhen Xu, Balaji Lakshminarayanan, Andrew M. Dai Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks
Neil Band, Tim G. J. Rudner, Qixuan Feng, Angelos Filos, Zachary Nado, Michael W Dusenberry, Ghassen Jerfel, Dustin Tran, Yarin Gal Benchmarking Robustness to Natural Distribution Shifts for Facial Analysis
Jessica Deuschel, Andreas Foltyn, Leonie Anna Adams, Jan Maximilian Vieregge, Ute Schmid Beyond Target Networks: Improving Deep $q$-Learning with Functional Regularization
Alexandre Piché, Joseph Marino, Gian Maria Marconi, Valentin Thomas, Christopher Pal, Mohammad Emtiyaz Khan BLAST: Latent Dynamics Models from Bootstrapping
Keiran Paster, Lev E McKinney, Sheila A. McIlraith, Jimmy Ba Block Contextual MDPs for Continual Learning
Shagun Sodhani, Franziska Meier, Joelle Pineau, Amy Zhang Bootstrapped Meta-Learning
Sebastian Flennerhag, Yannick Schroecker, Tom Zahavy, Hado van Hasselt, David Silver, Satinder Singh Boxhead: A Dataset for Learning Hierarchical Representations
Yukun Chen, Andrea Dittadi, Frederik Träuble, Stefan Bauer, Bernhard Schölkopf Bringing Atomistic Deep Learning to Prime Time
Nathan C. Frey, Siddharth Samsi, Bharath Ramsundar, Connor W. Coley, Vijay Gadepally Bursting Scientific Filter Bubbles: Boosting Innovation via Novel Author Discovery
Jason Portenoy, Marissa Radensky, Jevin West, Eric Horvitz, Daniel S Weld, Tom Hope C-Planning: An Automatic Curriculum for Learning Goal-Reaching Tasks
Tianjun Zhang, Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine, Joseph E. Gonzalez Certifiably Robust Variational Autoencoders
Ben Barrett, Alexander Camuto, Matthew Willetts, Tom Rainforth Challenges of Adversarial Image Augmentations
Arno Blaas, Xavier Suau, Jason Ramapuram, Nicholas Apostoloff, Luca Zappella Characterizing and Improving MPC-Based Private Inference for Transformer-Based Models
Yongqin Wang, Edward Suh, Wenjie Xiong, Brian Knott, Benjamin Lefaudeux, Murali Annavaram, Hsien-Hsin Lee CIC: Contrastive Intrinsic Control for Unsupervised Skill Discovery Michael Laskin, Hao Liu, Xue Bin Peng, Denis Yarats, Aravind Rajeswaran, Pieter Abbeel Combining Public and Private Data
Cecilia Ferrando, Jennifer Gillenwater, Alex Kulesza Compressing (Multidimensional) Learned Bloom Filters
Angjela Davitkova, Damjan Gjurovski, Sebastian Michel CoMPS: Continual Meta Policy Search
Glen Berseth, Zhiwei Zhang, Grace Zhang, Chelsea Finn, Sergey Levine Concept Generalization in Visual Representation Learning
Mert Bülent Sarıyıldız, Yannis Kalantidis, Diane Larlus, Karteek Alahari Continual Density Ratio Estimation
Yu Chen, Song Liu, Tom Diethe, Peter Flach Continual Learning with Memory Cascades
David Kappel, Francesco Negri, Christian Tetzlaff Continuous Control with Action Quantization from Demonstrations
Robert Dadashi, Leonard Hussenot, Damien Vincent, Sertan Girgin, Anton Raichuk, Matthieu Geist, Olivier Pietquin Continuous Control with Ensemble Deep Deterministic Policy Gradients
Piotr Januszewski, Mateusz Olko, Michał Królikowski, Jakub Swiatkowski, Marcin Andrychowicz, Łukasz Kuciński, Piotr Miłoś Contrastive Learning Through Time
Felix Schneider, Xia Xu, Markus R. Ernst, Zhengyang Yu, Jochen Triesch Controllable Network Data Balancing with GANs
Fares Meghdouri, Thomas Schmied, Thomas Gärtner, Tanja Zseby Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning
Guy Tennenholtz, Assaf Hallak, Gal Dalal, Shie Mannor, Gal Chechik, Uri Shalit Cross-Domain Imitation Learning via Optimal Transport
Arnaud Fickinger, Samuel Cohen, Stuart Russell, Brandon Amos Curriculum Meta-Learning for Few-Shot Classification
Emmanouil Stergiadis, Priyanka Agrawal, Oliver Squire Cyclic Orthogonal Convolutions for Long-Range Integration of Features
Federica Freddi, Jezabel R Garcia, Michael Bromberg, Sepehr Jalali, Da-shan Shiu, Alvin Chua, Alberto Bernacchia DARTS for Inverse Problems: A Study on Stability
Jonas Geiping, Jovita Lukasik, Margret Keuper, Michael Moeller Data Sharing Without Rewards in Multi-Task Offline Reinforcement Learning
Tianhe Yu, Aviral Kumar, Yevgen Chebotar, Chelsea Finn, Sergey Levine, Karol Hausman Data-Driven Taylor-Galerkin Finite-Element Scheme for Convection Problems
Luciano Drozda, Pavanakumar Mohanamuraly, Yuval Realpe, Corentin Lapeyre, Amir Adler, Guillaume Daviller, Thierry Poinsot Debugging the Internals of Convolutional Networks
Bilal Alsallakh, Narine Kokhlikyan, Vivek Miglani, Shubham Muttepawar, Edward Wang, Sara Zhang, David Adkins, Orion Reblitz-Richardson DeDUCE: Generating Counterfactual Explanations at Scale
Benedikt Höltgen, Lisa Schut, Jan M. Brauner, Yarin Gal Deep Reinforcement Learning Explanation via Model Transforms
Mira Finkelstein, Nitsan Levy Schlot, Lucy Liu, Yoav Kolumbus, Jeffrey Rosenschein, David C. Parkes, Sarah Keren Deep RePReL--Combining Planning and Deep RL for Acting in Relational Domains
Harsha Kokel, Arjun Manoharan, Sriraam Natarajan, Balaraman Ravindran, Prasad Tadepalli Deep Subspace Learning for Efficient Reconstruction of Spatiotemporal Imaging Data
Christopher Michael Sandino, Frank Ong, Siddharth Srinivasan Iyer, Adam Bush, Shreyas Vasanawala Differential Privacy via Group Shuffling
Amir Mohammad Abouei, Clement Louis Canonne Distributed Deep Learning for Persistent Monitoring of Agricultural Fields
Yasaman Esfandiari, Koushik Nagasubramanian, Fateme Fotouhi, Patrick S. Schnable, Baskar Ganapathysubramanian, Soumik Sarkar Distributionally Robust Group Backwards Compatibility
Martin Andres Bertran, Natalia Martinez, Alex Oesterling, Guillermo Sapiro Do ImageNet Classifiers Generalize to ImageNet?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, Vaishaal Shankar DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization
Aviral Kumar, Rishabh Agarwal, Tengyu Ma, Aaron Courville, George Tucker, Sergey Levine Drug Repositioning via Text Augmented Knowledge Graph Embeddings
Mian Zhong, Tiancheng Hu, Ying Jiao, Shehzaad Zuzar Dhuliawala, Bipin Singh Dynamic Mirror Descent Based Model Predictive Control for Accelerating Robot Learning
Utkarsh Aashu Mishra, Soumya Rani Samineni, Prakhar Goel, Chandravaran Venkatasai Kunjeti, Himanshu Lodha, Aman Singh, Aditya Verma Sagi, Shalabh Bhatnagar, N Y Shishir Effect of Diversity in Meta-Learning
Ramnath Kumar, Tristan Deleu, Yoshua Bengio Efficient Automated Online Experimentation with Multi-Fidelity
Steven Kleinegesse, Zhenwen Dai, Andreas Damianou, Kamil Ciosek, Federico Tomasi ElegantRL-Podracer: Scalable and Elastic Library for Cloud-Native Deep Reinforcement Learning
Xiao-Yang Liu, Zechu Li, Zhuoran Yang, Jiahao Zheng, Zhaoran Wang, Anwar Walid, Jian Guo, Michael Jordan Ensembles and Cocktails: Robust Finetuning for Natural Language Generation
John Hewitt, Xiang Lisa Li, Sang Michael Xie, Benjamin Newman, Percy Liang Evaluating Machine Accuracy on ImageNet
Vaishaal Shankar, Rebecca Roelofs, Horia Mania, Alex Fang, Benjamin Recht, Ludwig Schmidt Exploiting 3D Shape Bias Towards Robust Vision
Yutaro Yamada, Yuval Kluger, Sahand Negahban, Ilker Yildirim Exploring the Structure of Human Adjective Representations
Karan Grewal, Joshua Peterson, Bill D Thompson, Thomas L. Griffiths Exploring Through Random Curiosity with General Value Functions
Aditya Ramesh, Louis Kirsch, Sjoerd van Steenkiste, Jürgen Schmidhuber Exploring XAI for the Arts: Explaining Latent Space in Generative Music
Nick Bryan-Kinns, Berker Banar, Corey Ford, Courtney N. Reed, Yixiao Zhang, Simon Colton, Jack Armitage Expressive Power of Randomized Signature
Christa Cuchiero, Lukas Gonon, Lyudmila Grigoryeva, Juan-Pablo Ortega, Josef Teichmann Extending the WILDS Benchmark for Unsupervised Adaptation
Shiori Sagawa, Pang Wei Koh, Tony Lee, Irena Gao, Sang Michael Xie, Kendrick Shen, Ananya Kumar, Weihua Hu, Michihiro Yasunaga, Henrik Marklund, Sara Beery, Etienne David, Ian Stavness, Wei Guo, Jure Leskovec, Kate Saenko, Tatsunori Hashimoto, Sergey Levine, Chelsea Finn, Percy Liang Fast Inference and Transfer of Compositional Task for Few-Shot Task Generalization
Sungryull Sohn, Hyunjae Woo, Jongwook Choi, Lyubing Qiang, Izzeddin Gur, Aleksandra Faust, Honglak Lee Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks
Meng Liu, Cong Fu, Xuan Zhang, Limei Wang, Yaochen Xie, Hao Yuan, Youzhi Luo, Zhao Xu, Shenglong Xu, Shuiwang Ji Few Shot Image Generation via Implicit Autoencoding of Support Sets
Andy Huang, Kuan-Chieh Wang, Guillaume Rabusseau, Alireza Makhzani Finding Maximally Informative Patches in Images
Howard Zhong, Guha Balakrishnan, Richard Strong Bowen, Ramin Zabih, William T. Freeman Gotta Go Fast with Score-Based Generative Models
Alexia Jolicoeur-Martineau, Ke Li, Rémi Piché-Taillefer, Tal Kachman, Ioannis Mitliagkas Gradient-Matching Coresets for Continual Learning
Lukas Balles, Giovanni Zappella, Cedric Archambeau GradML: A Gradient-Based Loss for Deep Metric Learning
Bhavya Vasudeva, Puneesh Deora, Saumik Bhattacharya, Umapada Pal, Sukalpa Chanda GRAND: Graph Neural Diffusion
Benjamin Paul Chamberlain, James Rowbottom, Maria I. Gorinova, Stefan D Webb, Emanuele Rossi, Michael M. Bronstein Graph Backup: Data Efficient Backup Exploiting Markovian Data
Zhengyao Jiang, Tianjun Zhang, Robert Kirk, Tim Rocktäschel, Edward Grefenstette GraphGT: Machine Learning Datasets for Graph Generation and Transformation
Yuanqi Du, Shiyu Wang, Xiaojie Guo, Hengning Cao, Shujie Hu, Junji Jiang, Aishwarya Varala, Abhinav Angirekula, Liang Zhao GrASP: Gradient-Based Affordance Selection for Planning
Vivek Veeriah, Zeyu Zheng, Richard Lewis, Satinder Singh Greedy Learning for Large-Scale Neural MRI Reconstruction
Batu Ozturkler, Arda Sahiner, Tolga Ergen, Arjun D Desai, John M. Pauly, Shreyas Vasanawala, Morteza Mardani, Mert Pilanci Grounding Aleatoric Uncertainty in Unsupervised Environment Design
Minqi Jiang, Michael D Dennis, Jack Parker-Holder, Andrei Lupu, Heinrich Kuttler, Edward Grefenstette, Tim Rocktäschel, Jakob Nicolaus Foerster Handling Distribution Shift in Tire Design
Antoine de Mathelin, François Deheeger, Mathilde Mougeot, Nicolas Vayatis Hierarchical Few-Shot Imitation with Skill Transition Models
Kourosh Hakhamaneshi, Ruihan Zhao, Albert Zhan, Pieter Abbeel, Michael Laskin How Does Contrastive Pre-Training Connect Disparate Domains?
Kendrick Shen, Robbie Matthew Jones, Ananya Kumar, Sang Michael Xie, Percy Liang How to Distribute Data Across Tasks for Meta-Learning?
Alexandru Cioba, Michael Bromberg, Qian Wang, Ritwik Niyogi, Georgios Batzolis, Jezabel R Garcia, Da-shan Shiu, Alberto Bernacchia How to Reward Your Drug Agent?
Andrea Karlova, Wim Dehaen, Andrei Penciu How Well Does Self-Supervised Pre-Training Perform with Streaming ImageNet?
Dapeng Hu, Shipeng Yan, Qizhengqiu Lu, Lanqing Hong, Hailin Hu, Yifan Zhang, Zhenguo Li, Xinchao Wang, Jiashi Feng Human-in-the-Loop for a Disconnection Aware Retrosynthesis
Andrea Byekwaso, Philippe Schwaller, Alain C. Vaucher, Alessandra Toniato, Teodoro Laino Hybrid Imitative Planning with Geometric and Predictive Costs in Offroad Environments
Nitish Dashora, Daniel Shin, Dhruv Shah, Henry Leopold, David Fan, Ali Agha, Nicholas Rhinehart, Sergey Levine Identification of Enzymatic Active Sites with Unsupervised Language Modeling
Loïc Kwate Dassi, Matteo Manica, Daniel Probst, Philippe Schwaller, Yves Gaetan Nana Teukam, Teodoro Laino Igeood: An Information Geometry Approach to Out-of-Distribution Detection
Eduardo Dadalto Camara Gomes, Florence Alberge, Pierre Duhamel, Pablo Piantanida IIRC: Incremental Implicitly-Refined Classification
Mohamed Abdelsalam, Mojtaba Faramarzi, Shagun Sodhani, Sarath Chandar ImageNet Suffers from Dichotomous Data Difficulty
Kristof Meding, Luca M. Schulze Buschoff, Robert Geirhos, Felix A. Wichmann Imitation Learning from Pixel Observations for Continuous Control
Samuel Cohen, Brandon Amos, Marc Peter Deisenroth, Mikael Henaff, Eugene Vinitsky, Denis Yarats Implicit Behavioral Cloning
Pete Florence, Corey Lynch, Andy Zeng, Oscar A Ramirez, Ayzaan Wahid, Laura Downs, Adrian Wong, Johnny Lee, Igor Mordatch, Jonathan Tompson Implicitly Regularized RL with Implicit Q-Values
Nino Vieillard, Marcin Andrychowicz, Anton Raichuk, Olivier Pietquin, Matthieu Geist Improving Baselines in the Wild
Kazuki Irie, Imanol Schlag, Róbert Csordás, Jürgen Schmidhuber Improving Robustness in Motor Imagery Brain-Computer Interfaces
Mahta Mousavi, Eric Lybrand, Shuangquan Feng, Shuai Tang, Rayan Saab, Virginia R. de Sa Introducing Symmetries to Black Box Meta Reinforcement Learning
Louis Kirsch, Sebastian Flennerhag, Hado van Hasselt, Abram L. Friesen, Junhyuk Oh, Yutian Chen Introducing Symmetries to Black Box Meta Reinforcement Learning
Louis Kirsch, Sebastian Flennerhag, Hado van Hasselt, Abram L. Friesen, Junhyuk Oh, Yutian Chen Invertible Learned Primal-Dual
Jevgenija Rudzusika, Buda Bajic, Ozan Öktem, Carola-Bibiane Schönlieb, Christian Etmann Is Importance Weighting Incompatible with Interpolating Classifiers?
Ke Alexander Wang, Niladri Shekhar Chatterji, Saminul Haque, Tatsunori Hashimoto Is the Number of Trainable Parameters All That Actually Matters?
Amélie Chatelain, Amine Djeghri, Daniel Hesslow, Julien Launay, Iacopo Poli Just Mix Once: Mixing Samples with Implicit Group Distribution
Giorgio Giannone, Serhii Havrylov, Jordan Massiah, Emine Yilmaz, Yunlong Jiao Language Models as Recommender Systems: Evaluations and Limitations
Yuhui Zhang, Hao Ding, Zeren Shui, Yifei Ma, James Zou, Anoop Deoras, Hao Wang Latent Space Refinement for Deep Generative Models
Ramon Winterhalder, Marco Bellagente, Benjamin Nachman Layer-Parallel Training of Residual Networks with Auxiliary Variables
Qi Sun, Hexin Dong, Zewei Chen, Weizhen Dian, Jiacheng Sun, Yitong Sun, Zhenguo Li, Bin Dong Learning Compositional Tasks from Language Instructions
Lajanugen Logeswaran, Wilka Torrico Carvalho, Honglak Lee Learning Efficient Multi-Agent Cooperative Visual Exploration
Chao Yu, Xinyi Yang, Jiaxuan Gao, Huazhong Yang, Yu Wang, Yi Wu Learning Invariant Representations with Missing Data
Mark Goldstein, Joern-Henrik Jacobsen, Olina Chau, Adriel Saporta, Aahlad Manas Puli, Rajesh Ranganath, Andrew Miller Learning Robust Dynamics Through Variational Sparse Gating
Arnav Kumar Jain, Shiva Kanth Sujit, Shruti Joshi, Vincent Michalski, Danijar Hafner, Samira Ebrahimi Kahou Learning Transferable Motor Skills with Hierarchical Latent Mixture Policies
Dushyant Rao, Fereshteh Sadeghi, Leonard Hasenclever, Markus Wulfmeier, Martina Zambelli, Giulia Vezzani, Dhruva Tirumala, Yusuf Aytar, Josh Merel, Nicolas Heess, Raia Hadsell Leveraging Unlabeled Data to Predict Out-of-Distribution Performance
Saurabh Garg, Sivaraman Balakrishnan, Zachary Chase Lipton, Behnam Neyshabur, Hanie Sedghi Likelihood-Free Inference in State-Space Models with Unknown Dynamics
Alexander Aushev, Thong Anh Tran, Henri Pesonen, Andrew Howes, Samuel Kaski Matching Plug-and-Play Algorithms to the Denoiser
Saurav K Shastri, Rizwan Ahmad, Christopher Metzler, Philip Schniter Measuring Robustness to Natural Distribution Shifts in Image Classification
Rohan Taori, Achal Dave, Vaishaal Shankar, Nicholas Carlini, Benjamin Recht, Ludwig Schmidt Membership Inference Attacks Against NLP Classification Models
Virat Shejwalkar, Huseyin A Inan, Amir Houmansadr, Robert Sim Meta Arcade: A Configurable Environment Suite for Deep Reinforcement Learning and Meta-Learning
Edward W Staley, Chace Ashcraft, Benjamin Stoler, Jared Markowitz, Gautam Vallabha, Christopher Ratto, Kapil Katyal Meta-Learning from Sparse Recovery
Beicheng Lou, Nathan Zhao, Jiahui Wang Meta-Learning Reliable Priors in the Function Space
Jonas Rothfuss, Dominique Heyn, Jinfan Chen, Andreas Krause MGIC: Multigrid-in-Channels Neural Network Architectures
Moshe Eliasof, Jonathan Ephrath, Lars Ruthotto, Eran Treister MHER: Model-Based Hindsight Experience Replay
Rui Yang, Meng Fang, Lei Han, Yali Du, Feng Luo, Xiu Li Mismatched No More: Joint Model-Policy Optimization for Model-Based RL
Benjamin Eysenbach, Alexander Khazatsky, Sergey Levine, Ruslan Salakhutdinov Modern Hopfield Networks for Return Decomposition for Delayed Rewards
Michael Widrich, Markus Hofmarcher, Vihang Prakash Patil, Angela Bitto-Nemling, Sepp Hochreiter Multi-Domain Ensembles for Domain Generalization
Kowshik Thopalli, Sameeksha Katoch, Jayaraman J. Thiagarajan, Pavan K. Turaga, Andreas Spanias Multi-Modal Self-Supervised Pre-Training for Large-Scale Genome Data
Shentong Mo, Xi Fu, Chenyang Hong, Yizhen Chen, Yuxuan Zheng, Xiangru Tang, Yanyan Lan, Zhiqiang Shen, Eric Xing NAM: Normalization-Based Attention Module
Yichao Liu, Zongru Shao, Yueyang Teng, Nico Hoffmann Neural ODE Processes: A Short Summary
Alexander Luke Ian Norcliffe, Cristian Bodnar, Ben Day, Jacob Moss, Pietro Lio Neural Solvers for Fast and Accurate Numerical Optimal Control
Federico Berto, Stefano Massaroli, Michael Poli, Jinkyoo Park NeurInt: Learning to Interpolate Through Neural ODEs
Avinandan Bose, Aniket Das, Yatin Dandi, Piyush Rai No DICE: An Investigation of the Bias-Variance Tradeoff in Meta-Gradients
Risto Vuorio, Jacob Austin Beck, Gregory Farquhar, Jakob Nicolaus Foerster, Shimon Whiteson Nonparametric Approach to Uncertainty Quantification for Deterministic Neural Networks
Nikita Yurevich Kotelevskii, Alexander Fishkov, Kirill Fedyanin, Aleksandr Petiushko, Maxim Panov Off-Policy Correction for Multi-Agent Reinforcement Learning
Michał Zawalski, Błażej Osiński, Henryk Michalewski, Piotr Miłoś Offline Meta-Reinforcement Learning with Online Self-Supervision
Vitchyr H. Pong, Ashvin Nair, Laura Smith, Catherine Huang, Sergey Levine Offline Meta-Reinforcement Learning with Online Self-Supervision
Vitchyr H. Pong, Ashvin Nair, Laura Smith, Catherine Huang, Sergey Levine Offline Policy Selection Under Uncertainty
Mengjiao Yang, Bo Dai, Ofir Nachum, George Tucker, Dale Schuurmans On Second Order Behaviour in Augmented Neural ODEs: A Short Summary
Alexander Luke Ian Norcliffe, Cristian Bodnar, Ben Day, Nikola Simidjievski, Pietro Lio On the Limitations of Multimodal VAEs
Imant Daunhawer, Thomas M. Sutter, Kieran Chin-Cheong, Emanuele Palumbo, Julia E Vogt On the Pitfalls of Label Differential Privacy
Andres Munoz Medina, Robert Istvan Busa-Fekete, Umar Syed, Sergei Vassilvitskii On the Practical Consistency of Meta-Reinforcement Learning Algorithms
Zheng Xiong, Luisa M Zintgraf, Jacob Austin Beck, Risto Vuorio, Shimon Whiteson On the Reliability of Machine Learning Applications in Manufacturing Environments
Nicolas Jourdan, Sagar Sen, Enrique Garcia, Erik Johannes Husom, Tobias Biegel, Joachim Metternich On the Role of Pre-Training for Meta Few-Shot Learning
Chia-You Chen, Hsuan-Tien Lin, Masashi Sugiyama, Gang Niu One Pass ImageNet
Huiyi Hu, Ang Li, Daniele Calandriello, Dilan Gorur Opacus: User-Friendly Differential Privacy Library in PyTorch
Ashkan Yousefpour, Igor Shilov, Alexandre Sablayrolles, Davide Testuggine, Karthik Prasad, Mani Malek, John Nguyen, Sayan Ghosh, Akash Bharadwaj, Jessica Zhao, Graham Cormode, Ilya Mironov Optimal Representations for Covariate Shifts
Yann Dubois, Yangjun Ruan, Chris J. Maddison OstrichRL: A Musculoskeletal Ostrich Simulation to Study Bio-Mechanical Locomotion
Vittorio La Barbera, Fabio Pardo, Yuval Tassa, Monica Daley, Christopher Richards, Petar Kormushev, John Hutchinson Palette: Image-to-Image Diffusion Models
Chitwan Saharia, William Chan, Huiwen Chang, Chris A. Lee, Jonathan Ho, Tim Salimans, David J. Fleet, Mohammad Norouzi Particle Dynamics for Learning EBMs
Kirill Neklyudov, Priyank Jaini, Max Welling PCA Subspaces Are Not Always Optimal for Bayesian Learning
Alexandre Bense, Amir Joudaki, Tim G. J. Rudner, Vincent Fortuin Photoacoustic Imaging with Conditional Priors from Normalizing Flows
Rafael Orozco, Ali Siahkoohi, Gabrio Rizzuti, Tristan van Leeuwen, Felix Johan Herrmann Photon-Limited Deblurring Using Algorithm Unrolling
Yash Sanghvi, Abhiram Gnanasambandam, Stanley Chan Physical Benchmarking for AI-Generated Cosmic Web
Xiaofeng Dong, Nesar Soorve Ramachandra, Salman Habib, Katrin Heitmann, Michael Buehlmann, Sandeep Madireddy Physics-Based Learned Diffuser for Single-Shot 3D Imaging
Eric Markley, Fanglin Linda Liu, Michael Kellman, Nick Antipa, Laura Waller PixMix: Dreamlike Pictures Comprehensively Improve Safety Measures
Dan Hendrycks, Andy Zou, Mantas Mazeika, Leonard Tang, Dawn Song, Jacob Steinhardt Policy Gradients Incorporating the Future
David Venuto, Elaine Lau, Doina Precup, Ofir Nachum Policy Optimization via Optimal Policy Evaluation
Alberto Maria Metelli, Samuele Meta, Marcello Restelli Population Level Privacy Leakage in Binary Classification Wtih Label Noise
Robert Istvan Busa-Fekete, Andres Munoz Medina, Umar Syed, Sergei Vassilvitskii Private Confidence Sets
Karan Chadha, John Duchi, Rohith Kuditipudi Probabilistic Hierarchical Forecasting with Deep Poisson Mixtures
Kin Gutierrez Olivares, Nganba Meetei, Ruijun Ma, Rohan Reddy, Mengfei Cao Proof Extraction for Logical Neural Networks
Thabang Lebese, Ndivhuwo Makondo, Cristina Cornelio, Naweed Khan RASL: Relational Algebra in Scikit-Learn Pipelines
Chirag Sahni, Kiran Kate, Avraham Shinnar, Hoang Thanh Lam, Martin Hirzel Re-Labeling Domains Improves Multi-Domain Generalization
Kowshik Thopalli, Pavan K. Turaga, Jayaraman J. Thiagarajan Reconstructing Test Labels from Noisy Loss Scores (Extended Abstract)
Abhinav Aggarwal, Shiva Kasiviswanathan, Zekun Xu, Oluwaseyi Feyisetan, Nathanael Teissier Regression Modeling on DNA Encoded Libraries
Ralph Ma, Gabriel H. S. Dreiman, Fiorella Ruggiu, Adam Joseph Riesselman, Bowen Liu, Keith James, Mohammad Sultan, Daphne Koller Reinforcement Explanation Learning
Siddhant Agarwal, Owais Iqbal, Sree Aditya Buridi, Madda Manjusha, Abir Das Return Dispersion as an Estimator of Learning Potential for Prioritized Level Replay
Iryna Korshunova, Minqi Jiang, Jack Parker-Holder, Tim Rocktäschel, Edward Grefenstette Return Dispersion as an Estimator of Learning Potential for Prioritized Level Replay
Iryna Korshunova, Minqi Jiang, Jack Parker-Holder, Tim Rocktäschel, Edward Grefenstette Robust Algorithmic Collusion
Nicolas Eschenbaum, Philipp Zahn Robust Compressed Sensing MR Imaging with Deep Generative Priors
Ajil Jalal, Marius Arvinte, Giannis Daras, Eric Price, Alex Dimakis, Jonathan Tamir Robust Fine-Tuning of Zero-Shot Models
Mitchell Wortsman, Gabriel Ilharco, Jong Wook Kim, Mike Li, Simon Kornblith, Rebecca Roelofs, Raphael Gontijo-Lopes, Hanna Hajishirzi, Ali Farhadi, Hongseok Namkoong, Ludwig Schmidt Robust Robotic Control from Pixels Using Contrastive Recurrent State-Space Models
Nitish Srivastava, Walter Talbott, Martin Bertran Lopez, Shuangfei Zhai, Joshua M. Susskind Scalable Geometric Deep Learning on Molecular Graphs
Nathan C. Frey, Siddharth Samsi, Joseph McDonald, Lin Li, Connor W. Coley, Vijay Gadepally Score-Based Generative Classifiers
Roland S. Zimmermann, Lukas Schott, Yang Song, Benjamin Adric Dunn, David A. Klindt Self-Imitation Learning from Demonstrations
Georgiy Pshikhachev, Dmitry Ivanov, Vladimir Egorov, Aleksei Shpilman Self-Supervised Anomaly Detection via Neural Autoregressive Flows with Active Learning
Jiaxin Zhang, Kyle Saleeby, Thomas Feldhausen, Sirui Bi, Alex Plotkowski, David Womble Semi-Local Convolutions for LiDAR Scan Processing
Larissa Triess, David Peter, Johann Marius Zöllner Semi-Supervised Graph Neural Network for Particle-Level Noise Removal
Tianchun Li, Shikun Liu, Yongbin Feng, Nhan Tran, Miaoyuan Liu, Pan Li Shape-Tailored Deep Neural Networks with PDEs
Naeemullah Khan, Angira Sharma, Philip Torr, Ganesh Sundaramoorthi Shift and Scale Is Detrimental to Few-Shot Transfer
Moslem Yazdanpanah, Aamer Abdul Rahman, Christian Desrosiers, Mohammad Havaei, Eugene Belilovsky, Samira Ebrahimi Kahou Simulated User Studies for Explanation Evaluation
Valerie Chen, Gregory Plumb, Nicholay Topin, Ameet Talwalkar Single Image Super-Resolution with Uncertainty Estimation for Lunar Satellite Images
Jose Ignacio Delgado-Centeno, Paula Harder, Ben Moseley, Valentin Bickel, Siddha Ganju, Miguel Olivares-Mendez, Alfredo Kalaitzis Skill-Based Meta-Reinforcement Learning
Taewook Nam, Shao-Hua Sun, Karl Pertsch, Sung Ju Hwang, Joseph J Lim Skill-Based Meta-Reinforcement Learning
Taewook Nam, Shao-Hua Sun, Karl Pertsch, Sung Ju Hwang, Joseph J Lim Smooth Transfer Learning for Source-to-Target Generalization
Keita Takayama, Ikuro Sato, Teppei Suzuki, Rei Kawakami, Kuniaki Uto, Koichi Shinoda SoK: Privacy-Preserving Clustering (Extended Abstract)
Aditya Hegde, Helen Möllering, Thomas Schneider, Hossein Yalame SSFD: Self-Supervised Feature Distance as an MR Image Reconstruction Quality Metric
Philip M Adamson, Beliz Gunel, Jeffrey Dominic, Arjun D Desai, Daniel Spielman, Shreyas Vasanawala, John M. Pauly, Akshay Chaudhari StarCraft II Unplugged: Large Scale Offline Reinforcement Learning
Michael Mathieu, Sherjil Ozair, Srivatsan Srinivasan, Caglar Gulcehre, Shangtong Zhang, Ray Jiang, Tom Le Paine, Konrad Zolna, Richard Powell, Julian Schrittwieser, David Choi, Petko Georgiev, Daniel Kenji Toyama, Aja Huang, Roman Ring, Igor Babuschkin, Timo Ewalds, Mahyar Bordbar, Sarah Henderson, Sergio Gómez Colmenarejo, Aaron van den Oord, Wojciech M. Czarnecki, Nando de Freitas, Oriol Vinyals Status-Quo Policy Gradient in Multi-Agent Reinforcement Learning
Pinkesh Badjatiya, Mausoom Sarkar, Nikaash Puri, Jayakumar Subramanian, Abhishek Sinha, Siddharth Singh, Balaji Krishnamurthy Stochastic Video Prediction with Perceptual Loss
Donghun Lee, Ingook Jang, Seonghyun Kim, Chanwon Park, Junhee Park Strength Through Diversity: Robust Behavior Learning via Mixture Policies
Tim Seyde, Wilko Schwarting, Igor Gilitschenski, Markus Wulfmeier, Daniela Rus Successor Feature Neural Episodic Control
David Emukpere, Xavier Alameda-Pineda, Chris Reinke Synthesizing Video Trajectory Queries
Stephen Mell, Favyen Bastani, Stephan Zdancewic, Osbert Bastani Target Entropy Annealing for Discrete Soft Actor-Critic
Yaosheng Xu, Dailin Hu, Litian Liang, Stephen Marcus McAleer, Pieter Abbeel, Roy Fox Task Attended Meta-Learning for Few-Shot Learning
Aroof Aimen, Sahil Sidheekh, Bharat Ladrecha, Narayanan Chatapuram Krishnan Task-Driven Discovery of Perceptual Schemas for Generalization in Reinforcement Learning
Wilka Torrico Carvalho, Andrew Kyle Lampinen, Kyriacos Nikiforou, Felix Hill, Murray Shanahan Task-Induced Representation Learning
Jun Yamada, Karl Pertsch, Anisha Gunjal, Joseph J Lim Temporal-Difference Value Estimation via Uncertainty-Guided Soft Updates
Litian Liang, Yaosheng Xu, Stephen Marcus McAleer, Dailin Hu, Alexander Ihler, Pieter Abbeel, Roy Fox That Escalated Quickly: Compounding Complexity by Editing Levels at the Frontier of Agent Capabilities
Jack Parker-Holder, Minqi Jiang, Michael D Dennis, Mikayel Samvelyan, Jakob Nicolaus Foerster, Edward Grefenstette, Tim Rocktäschel The Curse of Depth in Kernel Regime
Soufiane Hayou, Arnaud Doucet, Judith Rousseau The Effect of Model Size on Worst-Group Generalization
Alan Le Pham, Eunice Chan, Vikranth Srivatsa, Dhruba Ghosh, Yaoqing Yang, Yaodong Yu, Ruiqi Zhong, Joseph E. Gonzalez, Jacob Steinhardt Thinking Beyond Distributions in Testing Machine Learned Models
Negar Rostamzadeh, Ben Hutchinson, Christina Greer, Vinodkumar Prabhakaran This Earthquake Doesn't Exist
Artemii Novoselov, Krisztina Sinkovics, Goetz Bokelmann Towards Better Visual Explanations for Deep Image Classifiers
Agnieszka Grabska-Barwinska, Amal Rannen-Triki, Omar Rivasplata, András György Towards Data-Free Domain Generalization
Ahmed Frikha, Haokun Chen, Denis Krompaß, Thomas Runkler, Volker Tresp Towards Neural Functional Program Evaluation
Torsten Scholak, Jonathan Pilault, Joey Velez-Ginorio Transfer Learning for Bayesian HPO with End-to-End Landmark Meta-Features
Hadi Samer Jomaa, Sebastian Pineda Arango, Lars Schmidt-Thieme, Josif Grabocka Transferring Dexterous Manipulation from GPU Simulation to a Remote Real-World Trifinger
Arthur Allshire, Mayank Mittal, Varun Lodaya, Viktor Makoviychuk, Denys Makoviichuk, Felix Widmaier, Manuel Wuthrich, Stefan Bauer, Ankur Handa, Animesh Garg Transformers Can Do Bayesian-Inference by Meta-Learning on Prior-Data
Samuel Müller, Noah Hollmann, Sebastian Pineda Arango, Josif Grabocka, Frank Hutter Transparent Liquid Segmentation for Robotic Pouring
Gautham Narayan Narasimhan, Kai Zhang, Ben Eisner, Xingyu Lin, David Held Traversing Geodesics to Grow Biological Structures
Pranav Bhamidipati, Guruprasad Raghavan, Matt Thomson Type Inference as Optimization
Eirene V. Pandi, Earl T. Barr, Andrew D. Gordon, Charles Sutton Understanding the Effects of Dataset Characteristics on Offline Reinforcement Learning
Kajetan Schweighofer, Markus Hofmarcher, Marius-Constantin Dinu, Philipp Renz, Angela Bitto-Nemling, Vihang Prakash Patil, Sepp Hochreiter Unit-Level Surprise in Neural Networks
Cian Eastwood, Ian Mason, Chris Williams Unsupervised Attribute Alignment for Characterizing Distribution Shift
Matthew Lyle Olson, Shusen Liu, Rushil Anirudh, Jayaraman J. Thiagarajan, Weng-Keen Wong, Peer-timo Bremer Unsupervised Learning of Temporal Abstractions Using Slot-Based Transformers
Anand Gopalakrishnan, Kazuki Irie, Jürgen Schmidhuber, Sjoerd van Steenkiste URLB: Unsupervised Reinforcement Learning Benchmark
Michael Laskin, Denis Yarats, Hao Liu, Kimin Lee, Albert Zhan, Kevin Lu, Catherine Cang, Lerrel Pinto, Pieter Abbeel Using Distributionally Robust Optimization to Improve Robustness in Cancer Pathology
Surya Narayanan Hari, Eliezer Van Allen, Jackson Nyman, Nicita Mehta, Bowen Jiang, Haitham Elmarakeby, Felix Dietlein, Jacob Rosenthal, Eshna Sengupta, Renato Umeton, Alexander Chowdhury Value Function Spaces: Skill-Centric State Abstractions for Long-Horizon Reasoning
Dhruv Shah, Peng Xu, Yao Lu, Ted Xiao, Alexander T Toshev, Sergey Levine, Brian Ichter Vehicle Speed Estimation Using Computer Vision and Evolutionary Camera Calibration
Hector Mejia, Esteban Palomo, Ezequiel López-Rubio, Israel Pineda, Rigoberto Fonseca Vision-Guided Quadrupedal Locomotion in the Wild with Multi-Modal Delay Randomization
Chieko Sarah Imai, Minghao Zhang, Yuchen Zhang, Marcin Kierebiński, Ruihan Yang, Yuzhe Qin, Xiaolong Wang Visualizing the Sim2Real Gap in Robot Ego-Pose Estimation
Théo Jaunet, Guillaume Bono, Romain Vuillemot, Christian Wolf Visually Grounded Reasoning Across Languages and Cultures
Fangyu Liu, Emanuele Bugliarello, Edoardo Ponti, Siva Reddy, Nigel Collier, Desmond Elliott Wasserstein Distance Maximizing Intrinsic Control
Ishan Durugkar, Steven Stenberg Hansen, Stephen Spencer, Volodymyr Mnih Wish You Were Here: Hindsight Goal Selection for Long-Horizon Dexterous Manipulation
Todor Davchev, Oleg Sushkov, Jean-Baptiste Regli, Stefan Schaal, Yusuf Aytar, Markus Wulfmeier, Jon Scholz XCI-Sketch: Extraction of Color Information from Images for Generation of Colored Outlines and Sketches
V Manushree, Sameer Saxena, Parna Chowdhury, Manisimha Varma Manthena, Harsh Rathod, Ankita Ghosh, Sahil Khose Your Dataset Is a Multiset and You Should Compress It like One
Daniel Severo, James Townsend, Ashish J Khisti, Alireza Makhzani, Karen Ullrich