NeurIPSW 2022
1722 papers
3D Single-Cell Shape Analysis of Cancer Cells Using Geometric Deep Learning
Matt De Vries, Lucas G Dent, Nathan Curry, Leo Rowe-Brown, Adam Tyson, Chris Dunsby, Chris Bakal A 3D-Shape Similarity-Based Contrastive Approach to Molecular Representation Learning
Austin Atsango, Nathaniel Lee Diamant, Ziqing Lu, Tommaso Biancalani, Gabriele Scalia, Kangway V Chuang A Better Way to Decay: Proximal Gradient Training Algorithms for Neural Nets
Liu Yang, Jifan Zhang, Joseph Shenouda, Dimitris Papailiopoulos, Kangwook Lee, Robert D Nowak A Causal AI Suite for Decision-Making
Emre Kiciman, Eleanor Wiske Dillon, Darren Edge, Adam Foster, Agrin Hilmkil, Joel Jennings, Chao Ma, Robert Ness, Nick Pawlowski, Amit Sharma, Cheng Zhang A Closer Look at Novel Class Discovery from the Labeled Set
Ziyun Li, Jona Otholt, Ben Dai, Di Hu, Christoph Meinel, Haojin Yang A Deep Dive into Dataset Imbalance and Bias in Face Identification
Valeriia Cherepanova, Steven Reich, Samuel Dooley, Hossein Souri, John P Dickerson, Micah Goldblum, Tom Goldstein A Deep Dive into Dataset Imbalance and Bias in Face Identification
Valeriia Cherepanova, Steven Reich, Samuel Dooley, Hossein Souri, John P Dickerson, Micah Goldblum, Tom Goldstein A Deep Learning and Data Archaeology Approach for Mosquito Repellent Discovery
Jennifer N. Wei, Marnix Vlot, Benjamin Manuel Sanchez, Brian Lee, Luuk Berning, Martijn W Vos, Rob W.M. Henderson, Wesley Wei Qian, D. Michael Ando, Kurt M. Groetsch, Richard C Gerkin, Alexander B Wiltschko, Koen J Dechering A Framework for Predictable Actor-Critic Control
Josiah D Coad, James Ault, Jeff Hykin, Guni Sharon A General Framework for Reward Function Distances
Erik Jenner, Joar Max Viktor Skalse, Adam Gleave A General Framework for Safe Decision Making: A Convex Duality Approach
Martino Bernasconi, Federico Cacciamani, Nicola Gatti, Francesco Trovò A Generic Diffusion-Based Approach for 3D Human Pose Prediction in the Wild
Saeed Saadatnejad, Ali Rasekh, Mohammadreza Mofayezi, Yasamin Medghalchi, Sara Rajabzadeh, Taylor Mordan, Alexandre Alahi A Mechanistic Lens on Mode Connectivity
Ekdeep Singh Lubana, Eric J Bigelow, Robert P. Dick, David Krueger, Hidenori Tanaka A Mixture-of-Expert Approach to RL-Based Dialogue Management
Yinlam Chow, Azamat Tulepbergenov, Ofir Nachum, Dhawal Gupta, Moonkyung Ryu, Mohammad Ghavamzadeh, Craig Boutilier A Neural ODE Interpretation of Transformer Layers
Yaofeng Desmond Zhong, Tongtao Zhang, Amit Chakraborty, Biswadip Dey A New Graph Node Classification Benchmark: Learning Structure from Histology Cell Graphs
Claudia Vanea, Jonathan Campbell, Omri Dodi, Liis Salumäe, Karen Meir, Drorith Hochner, Hagit Hochner, Triin Laisk, Linda M Ernst, Cecilia Lindgren, Christoffer Nellaker A Novel Model-Based Attribute Inference Attack in Federated Learning
Ilias Driouich, Chuan Xu, Giovanni Neglia, Frederic Giroire, Eoin Thomas A Novel Stochastic Gradient Descent Algorithm for LearningPrincipal Subspaces
Charline Le Lan, Joshua Greaves, Jesse Farebrother, Mark Rowland, Fabian Pedregosa, Rishabh Agarwal, Marc G Bellemare A Novel Two-Level Causal Inference Framework for On-Road Vehicle Quality Issues Diagnosis
Qian Wang, Huanyi Shui, Thi Tu Trinh Tran, Milad Zafar Nezhad, Devesh Upadhyay, Kamran Paynabar, Anqi He A Pareto-Optimal Compositional Energy-Based Model for Sampling and Optimization of Protein Sequences
Natasa Tagasovska, Nathan C. Frey, Andreas Loukas, Isidro Hotzel, Julien Lafrance-Vanasse, Ryan Lewis Kelly, Yan Wu, Arvind Rajpal, Richard Bonneau, Kyunghyun Cho, Stephen Ra, Vladimir Gligorijevic A Ranking Game for Imitation Learning
Harshit Sikchi, Akanksha Saran, Wonjoon Goo, Scott Niekum A Self-Driving Laboratory Optimizes a Scalable Process for Making Functional Coatings
Connor Rupnow, Benjamin Patrick MacLeod, Mehrdad Mokhtari, Karry Ocean, Kevan E. Dettelbach, Daniel Lin, Fraser Glynn Lintel Parlane, Hsi N. Chiu, Michael B. Rooney, Christopher Waizenegger, Elija de Hoog, Curtis P. Berlinguette A Single-Cell Gene Expression Language Model
William Connell, Umair Khan, Michael Keiser A Source Data Privacy Framework for Synthetic Clinical Trial Data
Afrah Shafquat, Jason Mezey, Mandis Beigi, Jimeng Sun, Jacob W. Aptekar A Stochastic Optimization Framework for Fair Risk Minimization
Andrew Lowy, Sina Baharlouei, Rakesh Pavan, Meisam Razaviyayn, Ahmad Beirami A Stochastic Prox-Linear Method for CVaR Minimization
Si Yi Meng, Vasileios Charisopoulos, Robert M. Gower A Tale of Two Food Adventurers: The Challenges and Triumphs of Repeated Food Exposures in Avoidant/Restrictive Food Intake Disorder
Young Kyung Kim, Juan Matias Di Martino, Julia Nicholas, Ilana Pilato, Alannah Melissa Rivera-Cancel, Julia Gianneschi, Jalisa Jackson, Ellen Vernell Mines, Nancy Zucker, Guillermo Sapiro A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games
Samuel Sokota, Ryan D'Orazio, J Zico Kolter, Nicolas Loizou, Marc Lanctot, Ioannis Mitliagkas, Noam Brown, Christian Kroer A Unified Framework for Comparing Learning Algorithms
Harshay Shah, Sung Min Park, Andrew Ilyas, Aleksander Madry A Unifying Framework for Online Safe Optimization
Matteo Castiglioni, Andrea Celli, Alberto Marchesi, Giulia Romano, Nicola Gatti A Universal Abstraction for Hierarchical Hopfield Networks
Benjamin Hoover, Duen Horng Chau, Hendrik Strobelt, Dmitry Krotov A View from Somewhere: Human-Centric Face Representations
Jerone Theodore Alexander Andrews, Przemyslaw Joniak, Alice Xiang Accelerating Open Science for AI in Heliophysics
Dolores Garcia, Paul James Wright, Robert Jarolim, Mark CM Cheung, Meng Jin, James Parr Active Acquisition for Multimodal Temporal Data: A Challenging Decision-Making Task
Jannik Kossen, Cătălina Cangea, Eszter Vértes, Andrew Jaegle, Viorica Patraucean, Ira Ktena, Nenad Tomasev, Danielle Belgrave Active Bayesian Causal Inference
Christian Toth, Lars Lorch, Christian Knoll, Andreas Krause, Franz Pernkopf, Robert Peharz, Julius Von Kügelgen Active Bayesian Causal Inference
Christian Toth, Lars Lorch, Christian Knoll, Andreas Krause, Franz Pernkopf, Robert Peharz, Julius Von Kügelgen Active Learning over Multiple Domains in Natural Language Tasks
Shayne Longpre, Julia Rachel Reisler, Edward Greg Huang, Yi Lu, Andrew Frank, Nikhil Ramesh, Christopher DuBois Actively Learning Costly Reward Functions for Reinforcement Learning
André Eberhard, Houssam Metni, Georg Fahland, Alexander Stroh, Pascal Friederich Actor Prioritized Experience Replay
Baturay Saglam, Furkan Burak Mutlu, Doğan Can Çiçek, Suleyman Serdar Kozat Adapting Pretrained Vision-Language Foundational Models to Medical Imaging Domains
Pierre Joseph Marcel Chambon, Christian Bluethgen, Curtis Langlotz, Akshay Chaudhari Adaptive Bias Correction for Improved Subseasonal Forecast
Soukayna Mouatadid, Paulo Orenstein, Genevieve Elaine Flaspohler, Judah Cohen, Miruna Oprescu, Ernest Fraenkel, Lester Mackey Adaptive Methods for Nonconvex Continual Learning
Seungyub Han, Yeongmo Kim, Taehyun Cho, Jungwoo Lee Adaptive Pre-Training of Language Models for Better Logical Reasoning
Soumya Sanyal, Yichong Xu, Shuohang Wang, Ziyi Yang, Reid Pryzant, Wenhao Yu, Chenguang Zhu, Xiang Ren Adaptive Sampling for Probabilistic Forecasting Under Distribution Shift
Luca Masserano, Syama Sundar Rangapuram, Shubham Kapoor, Rajbir Singh Nirwan, Youngsuk Park, Michael Bohlke-Schneider Adding Neuroplasticity to a CNN-Based In-Silico Model of Neurodegeneration
Jasmine A Moore, Matthias Wilms, Kayson Fakhar, Fatemeh Hadaeghi, Claus C Hilgetag, Nils Forkert Adversarial Attacks on Feature Visualization Methods
Jonathan Marty, Eugene Belilovsky, Michael Eickenberg Adversarial Cheap Talk
Chris Lu, Timon Willi, Alistair Letcher, Jakob Nicolaus Foerster Adversarial Cheap Talk
Chris Lu, Timon Willi, Alistair Letcher, Jakob Nicolaus Foerster Adversarial Policies Beat Professional-Level Go AIs
Tony Tong Wang, Adam Gleave, Nora Belrose, Tom Tseng, Michael D Dennis, Yawen Duan, Viktor Pogrebniak, Sergey Levine, Stuart Russell Adversarial Policies Beat Professional-Level Go AIs
Tony Tong Wang, Adam Gleave, Nora Belrose, Tom Tseng, Michael D Dennis, Yawen Duan, Viktor Pogrebniak, Joseph Miller, Sergey Levine, Stuart Russell Agent-Based Graph Neural Networks
Karolis Martinkus, Pál András Papp, Benedikt Schesch, Roger Wattenhofer Agent-Controller Representations: Principled Offline RL with Rich Exogenous Information
Riashat Islam, Manan Tomar, Alex Lamb, Hongyu Zang, Yonathan Efroni, Dipendra Misra, Aniket Rajiv Didolkar, Xin Li, Harm van Seijen, Remi Tachet des Combes, John Langford Aging with GRACE: Lifelong Model Editing with Discrete Key-Value Adaptors
Thomas Hartvigsen, Swami Sankaranarayanan, Hamid Palangi, Yoon Kim, Marzyeh Ghassemi AIMHI: Protecting Sensitive Data Through Federated Co-Training
Amr Abourayya, Michael Kamp, Erman Ayday, Jens Kleesiek, Kanishka Rao, Geoffrey I. Webb, Bharat Rao Aligning Robot Representations with Humans
Andreea Bobu, Andi Peng, Pulkit Agrawal, Julie Shah, Anca Dragan Amortized Inference for Causal Structure Learning
Lars Lorch, Scott Sussex, Jonas Rothfuss, Andreas Krause, Bernhard Schölkopf An AI-Assisted Labeling Tool for Cataloging High-Resolution Images of Galaxies
Gustavo Perez, Sean Linden, Timothy McQuaid, Matteo Messa, Daniela Calzetti, Subhransu Maji Anonymization for Skeleton Action Recognition
Saemi Moon, Myeonghyeon Kim, Zhenyue Qin, Yang Liu, Dongwoo Kim Are You Using Test Log-Likelihood Correctly?
Sameer Deshpande, Soumya Ghosh, Tin D. Nguyen, Tamara Broderick Assistance with Large Language Models
Dmitrii Krasheninnikov, Egor Krasheninnikov, David Krueger Asynchronous Speedup in Decentralized Optimization
Mathieu Even, Hadrien Hendrikx, Laurent Massoulié At the Intersection of Conceptual Art and Deep Learning: The End of Signature
Kathleen M Lewis, Divya M Shanmugam, Jose Javier Gonzalez Ortiz, Agnieszka Kurant, John Guttag Attack-Agnostic Adversarial Detection
Jiaxin Cheng, Mohamed E. Hussein, Jayadev Billa, Wael AbdAlmageed Attention for Compositional Modularity
Oleksiy Ostapenko, Pau Rodriguez, Alexandre Lacoste, Laurent Charlin Automated Protein Function Description for Novel Class Discovery
Meet Barot, Vladimir Gligorijevic, Richard Bonneau, Kyunghyun Cho Autonomous Materials Discovery for Organic Photovoltaics
Changhyun Hwang, Seungjoo Yi, David Friday, Nicholas Henry Angello, Tiara Charis Torres-Flores, Nick Jackson, Martin D. Burke, Charles Schroeder, Ying Diao AutoRL-Bench 1.0
Gresa Shala, Sebastian Pineda Arango, André Biedenkapp, Frank Hutter, Josif Grabocka BAAT: Towards Sample-Specific Backdoor Attack with Clean Labels
Yiming Li, Mingyan Zhu, Chengxiao Luo, Haiqin Weng, Yong Jiang, Tao Wei, Shu-Tao Xia Bandits with Costly Reward Observations
Aaron David Tucker, Caleb Biddulph, Claire Wang, Thorsten Joachims Batch Size Selection by Stochastic Optimal Control
Jim Zhao, Aurelien Lucchi, Frank Norbert Proske, Antonio Orvieto, Hans Kersting Bayesian Dynamic Causal Discovery
Alexander Tong, Lazar Atanackovic, Jason Hartford, Yoshua Bengio Bayesian Optimization with a Neural Network Meta-Learned on Synthetic Data Only
Samuel Müller, Sebastian Pineda Arango, Matthias Feurer, Josif Grabocka, Frank Hutter Bayesian Q-Learning with Imperfect Expert Demonstrations
Fengdi Che, Xiru Zhu, Doina Precup, David Meger, Gregory Dudek Bayesian Q-Learning with Imperfect Expert Demonstrations
Fengdi Che, Xiru Zhu, Doina Precup, David Meger, Gregory Dudek Benchmarking the Effect of Poisoning Defenses on the Security and Bias of the Final Model
Nathalie Baracaldo, Kevin Eykholt, Farhan Ahmed, Yi Zhou, Shriti Priya, Taesung Lee, Swanand Kadhe, Yusong Tan, Sridevi Polavaram, Sterling Suggs, Yuyang Gao, David Slater Better State Exploration Using Action Sequence Equivalence
Nathan Grinsztajn, Toby Johnstone, Johan Ferret, Philippe Preux Beyond Protected Attributes: Disciplined Detection of Systematic Deviations in Data
Adebayo Oshingbesan, Winslow Georgos Omondi, Girmaw Abebe Tadesse, Celia Cintas, Skyler Speakman Bias Amplification in Image Classification
Melissa Hall, Laurens van der Maaten, Laura Gustafson, Maxwell Jones, Aaron Bryan Adcock BigScience: A Case Study in the Social Construction of a Multilingual Large Language Model
Christopher Akiki, Giada Pistilli, Margot Mieskes, Matthias Gallé, Thomas Wolf, Suzana Ilic, Yacine Jernite Biological Cartography: Building and Benchmarking Representations of Life
Safiye Celik, Jan-Christian Huetter, Sandra Melo, Nathan Lazar, Rahul Mohan, Conor Tillinghast, Tommaso Biancalani, Marta Fay, Berton Earnshaw, Imran S Haque Bitrate-Constrained DRO: Beyond Worst Case Robustness to Unknown Group Shifts
Amrith Setlur, Don Dennis, Benjamin Eysenbach, Aditi Raghunathan, Chelsea Finn, Virginia Smith, Sergey Levine BLaDE: Robust Exploration via Diffusion Models
Bilal Piot, Zhaohan Daniel Guo, Shantanu Thakoor, Mohammad Gheshlaghi Azar Boosting as Frank-Wolfe
Ryotaro Mitsuboshi, Kohei Hatano, Eiji Takimoto Boosting Offline Reinforcement Learning via Data Rebalancing
Yang Yue, Bingyi Kang, Xiao Ma, Zhongwen Xu, Gao Huang, Shuicheng Yan Branch-Train-Merge: Embarrassingly Parallel Training of Expert Language Models
Margaret Li, Suchin Gururangan, Tim Dettmers, Mike Lewis, Tim Althoff, Noah A. Smith, Luke Zettlemoyer Broken Neural Scaling Laws
Ethan Caballero, Kshitij Gupta, Irina Rish, David Krueger Building a Subspace of Policies for Scalable Continual Learning
Jean-Baptiste Gaya, Thang Doan, Lucas Caccia, Laure Soulier, Ludovic Denoyer, Roberta Raileanu Built to Last: Lessons on Fostering a Student ML Community
Elizabeth Lau, Valeriy Rotan, Ashwin Reddy, Michael Equi, Arjun Sripathy, John Ian Reyes So But Are You Sure? Quantifying Uncertainty in Model Explanations
Charles Thomas Marx, Youngsuk Park, Hilaf Hasson, Bernie Wang, Stefano Ermon, Luke Huan C-GATS: Conditional Generation of Anomalous Time Series
Vikramank Singh, Abishek Sankararaman, Murali Balakrishnan, Zhao Song C-MBA: Adversarial Attack for Cooperative MARL Using Learned Dynamics Model
Nhan H Pham, Lam M. Nguyen, Jie Chen, Hoang Thanh Lam, Subhro Das, Lily Weng Can Active Sampling Reduce Causal Confusion in Offline Reinforcement Learning?
Gunshi Gupta, Tim G. J. Rudner, Rowan Thomas McAllister, Adrien Gaidon, Yarin Gal Can Active Sampling Reduce Causal Confusion in Offline Reinforcement Learning?
Gunshi Gupta, Tim G. J. Rudner, Rowan Thomas McAllister, Adrien Gaidon, Yarin Gal Can Active Sampling Reduce Causal Confusion in Offline Reinforcement Learning?
Gunshi Gupta, Tim G. J. Rudner, Rowan Thomas McAllister, Adrien Gaidon, Yarin Gal Can Calibration Improve Sample Prioritization?
Ganesh Tata, Gautham Krishna Gudur, Gopinath Chennupati, Mohammad Emtiyaz Khan Can Large Language Models Build Causal Graphs?
Stephanie Long, Tibor Schuster, Alexandre Piché Can We Forecast and Detect Earthquakes from Heterogeneous Multivariate Time Series Data?
Luke Cullen, Asadullah Hill Galib, Andrew William Smith, Debvrat Varshney, Edward Brown, Peter Chi, Xiangning Chu, Filip Svoboda Canary in a Coalmine: Better Membership Inference with Ensembled Adversarial Queries
Yuxin Wen, Arpit Bansal, Hamid Kazemi, Eitan Borgnia, Micah Goldblum, Jonas Geiping, Tom Goldstein CAREER: Economic Prediction of Labor Sequence Data Under Distribution Shift
Keyon Vafa, Emil Palikot, Tianyu Du, Ayush Kanodia, Susan Athey, David Blei Case Study: Applying Decision Focused Learning in the Real World
Shresth Verma, Aditya Mate, Kai Wang, Aparna Taneja, Milind Tambe Causal Analysis of the TOPCAT Trial: Spironolactone for Preserved Cardiac Function Heart Failure
Francesca Raimondi, Tadhg O'Keeffe, Hana Chockler, Andrew Lawrence, Tamara Stemberga, Andre Franca, Maksim Sipos, Javed Butler, Shlomo Ben-Haim Causal Discovery for Modular World Models
Anson Lei, Bernhard Schölkopf, Ingmar Posner Causal Structural Hypothesis Testing and Data Generation Models
Sunay Gajanan Bhat, Omead Pooladzandi, Jeffrey Jiang, Gregory Pottie Certified Defences Against Adversarial Patch Attacks on Semantic Segmentation
Maksym Yatsura, Kaspar Sakmann, N. Grace Hua, Matthias Hein, Jan Hendrik Metzen Certified Defences Hurt Generalisation
Piersilvio De Bartolomeis, Jacob Clarysse, Fanny Yang, Amartya Sanyal Certified Defences Hurt Generalisation
Piersilvio De Bartolomeis, Jacob Clarysse, Fanny Yang, Amartya Sanyal Certified Graph Unlearning
Eli Chien, Chao Pan, Olgica Milenkovic Certified Robustness in Federated Learning
Motasem Alfarra, Juan Camilo Perez, Egor Shulgin, Peter Richtárik, Bernard Ghanem Certified Training: Small Boxes Are All You Need
Mark Niklas Mueller, Franziska Eckert, Marc Fischer, Martin Vechev Characterizing Anomalies with Explainable Classifiers
Naveen Durvasula, Valentine d'Hauteville, Keegan Hines, John P Dickerson Chemistry Guided Molecular Graph Transformer
Peisong Niu, Tian Zhou, Qingsong Wen, Liang Sun, Tao Yao Choreographer: Learning and Adapting Skills in Imagination
Pietro Mazzaglia, Tim Verbelen, Bart Dhoedt, Alexandre Lacoste, Sai Rajeswar Choreographer: Learning and Adapting Skills in Imagination
Pietro Mazzaglia, Tim Verbelen, Bart Dhoedt, Alexandre Lacoste, Sai Rajeswar ChromFormer: A Transformer-Based Model for 3D Genome Structure Prediction
Henry Valeyre, Pushpak Pati, Federico Gossi, Vignesh Ram Somnath, Adriano Martinelli, Marianna Rapsomaniki Chunking Space and Time with Information Geometry
Tim Verbelen, Daria de Tinguy, Pietro Mazzaglia, Ozan Catal, Adam Safron Client-Private Secure Aggregation for Privacy-Preserving Federated Learning
Parker Newton, Olivia Choudhury, Bill Horne, Vidya Ravipati, Divya Bhargavi, Ujjwal Ratan CLUTR: Curriculum Learning via Unsupervised Task Representation Learning
Abdus Salam Azad, Izzeddin Gur, Aleksandra Faust, Pieter Abbeel, Ion Stoica CLUTR: Curriculum Learning via Unsupervised Task Representation Learning
Abdus Salam Azad, Izzeddin Gur, Aleksandra Faust, Pieter Abbeel, Ion Stoica Co-Imitation: Learning Design and Behaviour by Imitation
Chang Rajani, Karol Arndt, David Blanco-Mulero, Kevin Sebastian Luck, Ville Kyrki COIN: Co-Cluster Infomax for Bipartite Graphs
Baoyu Jing, Yuchen Yan, Yada Zhu, Hanghang Tong Cold Posteriors Through PAC-Bayes
Konstantinos Pitas, Julyan Arbel Cold Posteriors Through PAC-Bayes
Konstantinos Pitas, Julyan Arbel Collaborating with Language Models for Embodied Reasoning
Ishita Dasgupta, Christine Kaeser-Chen, Kenneth Marino, Arun Ahuja, Sheila Babayan, Felix Hill, Rob Fergus Collaborating with Language Models for Embodied Reasoning
Ishita Dasgupta, Christine Kaeser-Chen, Kenneth Marino, Arun Ahuja, Sheila Babayan, Felix Hill, Rob Fergus Composing Task Knowledge with Modular Successor Feature Approximators
Wilka Torrico Carvalho, Angelos Filos, Richard Lewis, Honglak Lee, Satinder Singh Compression Supports Low-Dimensional Representations of Behavior Across Neural Circuits
Dale Zhou, Jason Z Kim, Adam R Pines, Valerie J Sydnor, David R Roalf, John Detre, Ruben C Gur, Raquel E Gur, Theodore Satterthwaite, Danielle Bassett Concept-Based Understanding of Emergent Multi-Agent Behavior
Niko Grupen, Natasha Jaques, Been Kim, Shayegan Omidshafiei Condensing Graphs via One-Step Gradient Matching
Wei Jin, Xianfeng Tang, Haoming Jiang, Zheng Li, Danqing Zhang, Jiliang Tang, Bing Yin Conditional Invariances for Conformer Invariant Protein Representations
Balasubramaniam Srinivasan, Vassilis N. Ioannidis, Soji Adeshina, Mayank Kakodkar, George Karypis, Bruno Ribeiro Conditional Neural Processes for Molecules
Miguel Garcia Ortegon, Andreas Bender, Sergio Bacallado Conditional Neural Processes for Molecules
Miguel Garcia Ortegon, Andreas Bender, Sergio Bacallado Conformer Search Using SE3-Transformers and Imitation Learning
Luca Thiede, Santiago Miret, Krzysztof Sadowski, Haoping Xu, Mariano Phielipp, Alan Aspuru-Guzik Constrained Imitation Q-Learning with Earth Mover’s Distance Reward
Wenyan Yang, Nataliya Strokina, Joni Pajarinen, Joni-kristian Kamarainen Constrained MDPs Can Be Solved by Eearly-Termination with Recurrent Models
Hao Sun, Ziping Xu, Zhenghao Peng, Meng Fang, Taiyi Wang, Bo Dai, Bolei Zhou Constraining Low-Level Representations to Define Effective Confidence Scores
Joao Monteiro, Pau Rodriguez, Pierre-Andre Noel, Issam H. Laradji, David Vazquez Contactless Oxygen Monitoring with Gated Transformer
Hao He, Yuan Yuan, Ying-Cong Chen, Peng Cao, Dina Katabi Contextual Squeeze-and-Excitation
Massimiliano Patacchiola, John F Bronskill, Aliaksandra Shysheya, Katja Hofmann, Sebastian Nowozin, Richard E Turner Contextual Transformer for Offline Meta Reinforcement Learning
Runji Lin, Ye Li, Xidong Feng, Zhaowei Zhang, Xian Hong Wu Fung, Haifeng Zhang, Jun Wang, Yali Du, Yaodong Yang Continuous PDE Dynamics Forecasting with Implicit Neural Representations
Yuan Yin, Matthieu Kirchmeyer, Jean-Yves Franceschi, Alain Rakotomamonjy, Patrick Gallinari Continuous Soft Pseudo-Labeling in ASR
Tatiana Likhomanenko, Ronan Collobert, Navdeep Jaitly, Samy Bengio Contrastive Example-Based Control
Kyle Beltran Hatch, Sarthak J Shetty, Benjamin Eysenbach, Tianhe Yu, Rafael Rafailov, Ruslan Salakhutdinov, Sergey Levine, Chelsea Finn Contrastive Example-Based Control
Kyle Beltran Hatch, Sarthak J Shetty, Benjamin Eysenbach, Tianhe Yu, Rafael Rafailov, Ruslan Salakhutdinov, Sergey Levine, Chelsea Finn Contrastive Graph Few-Shot Learning
Chunhui Zhang, Hongfu Liu, Jundong Li, Yanfang Ye, Chuxu Zhang Contrastive Learning on Synthetic Videos for GAN Latent Disentangling
Kevin Duarte, Wei-An Lin, Ratheesh Kalarot, Jingwan Lu, Eli Shechtman, Shabnam Ghadar, Mubarak Shah Contrastive Pre-Training for Multimodal Medical Time Series
Aniruddh Raghu, Payal Chandak, Ridwan Alam, John Guttag, Collin Stultz Contrastive Value Learning: Implicit Models for Simple Offline RL
Bogdan Mazoure, Benjamin Eysenbach, Ofir Nachum, Jonathan Tompson Control Graph as Unified IO for Morphology-Task Generalization
Hiroki Furuta, Yusuke Iwasawa, Yutaka Matsuo, Shixiang Shane Gu Control Graph as Unified IO for Morphology-Task Generalization
Hiroki Furuta, Yusuke Iwasawa, Yutaka Matsuo, Shixiang Shane Gu CORL: Research-Oriented Deep Offline Reinforcement Learning Library
Denis Tarasov, Alexander Nikulin, Dmitry Akimov, Vladislav Kurenkov, Sergey Kolesnikov Counterfactual Fairness in Synthetic Data Generation
Mahed Abroshan, Mohammad Mahdi Khalili, Andrew Elliott Counterfactual Generation Under Confounding
Abbavaram Gowtham Reddy, Saloni Dash, Amit Sharma, Vineeth N. Balasubramanian Cross-Device Federated Architecture Search
Stefanos Laskaridis, Javier Fernandez-Marques, Łukasz Dudziak Cryptographic Auditing for Collaborative Learning
Hidde Lycklama, Nicolas Küchler, Alexander Viand, Emanuel Opel, Lukas Burkhalter, Anwar Hithnawi Curiosity in Hindsight
Daniel Jarrett, Corentin Tallec, Florent Altché, Thomas Mesnard, Remi Munos, Michal Valko Cyclophobic Reinforcement Learning
Stefan Sylvius Wagner, Peter Arndt, Jan Robine, Stefan Harmeling DARTFormer: Finding the Best Type of Attention
Jason Ross Brown, Yiren Zhao, Ilia Shumailov, Robert D. Mullins DASH: Decentralized CASH for Federated Learning
Md Ibrahim Ibne Alam, Koushik Kar, Theodoros Salonidis, Horst Samulowitz Data-Heterogeneity-Aware Mixing for Decentralized Learning
Yatin Dandi, Anastasia Koloskova, Martin Jaggi, Sebastian U Stich Deceiving the CKA Similarity Measure in Deep Learning
MohammadReza Davari, Stefan Horoi, Amine Natik, Guillaume Lajoie, Guy Wolf, Eugene Belilovsky Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models
Liam H Fowl, Jonas Geiping, Steven Reich, Yuxin Wen, Wojciech Czaja, Micah Goldblum, Tom Goldstein Decision Making as Language Generation
Roland Memisevic, Sunny Panchal, Mingu Lee decOM: Similarity-Based Microbial Source Tracking of Ancient Oral Samples Using K-Mer-Based Methods
Camila Duitama González, Riccardo Vicedomini, Teo Lemane, Nicolas Rascovan, Hugues Richard, Rayan Chikhi Deconfounded Imitation Learning
Risto Vuorio, Pim De Haan, Johann Brehmer, Hanno Ackermann, Daniel Dijkman, Taco Cohen Deconvolution of Astronomical Images with Deep Neural Networks
Hong Wang, Sreevarsha Sreejith, Yuewei Lin, Nesar Soorve Ramachandra, Anže Slosar, Shinjae Yoo Deep End-to-End Causal Inference
Tomas Geffner, Javier Antoran, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman, Amit Sharma, Angus Lamb, Martin Kukla, Agrin Hilmkil, Joel Jennings, Nick Pawlowski, Miltiadis Allamanis, Cheng Zhang Deep Fitness Inference for Drug Discovery with Directed Evolution
Nathaniel Lee Diamant, Ziqing Lu, Christina Helmling, Kangway V Chuang, Christian Cunningham, Tommaso Biancalani, Gabriele Scalia, Max W Shen Deep Neural Imputation: A Framework for Recovering Incomplete Brain Recordings
Sabera J Talukder, Jennifer J. Sun, Matthew K Leonard, Bingni W Brunton, Yisong Yue DeepJoint: Robust Survival Modelling Under Clinical Presence Shift
Vincent Jeanselme, Glen Martin, Niels Peek, Matthew Sperrin, Brian Tom, Jessica Barrett DeepStruc: Towards Structure Solution from Pair Distribution Function Data Using Deep Generative Models
Emil Thyge Skaaning Kjær, Andy Sode Anker, Marcus Nørgaard Weng, Simon J. L. Billinge, Raghavendra Selvan, Kirsten M. Ø. Jensen Denoised Smoothing with Sample Rejection for Robustifying Pretrained Classifiers
Fatemeh Sheikholeslami, Wan-Yi Lin, Jan Hendrik Metzen, Huan Zhang, J Zico Kolter Denoising Deep Generative Models
Gabriel Loaiza-Ganem, Brendan Leigh Ross, Luhuan Wu, John Patrick Cunningham, Jesse C Cresswell, Anthony L. Caterini DensePure: Understanding Diffusion Models Towards Adversarial Robustness Zhongzhu Chen, Kun Jin, Jiongxiao Wang, Weili Nie, Mingyan Liu, Anima Anandkumar, Bo Li, Dawn Song Designing Active and Thermostable Enzymes with Sequence-Only Predictive Models
Clara Fannjiang, Micah Olivas, Eric R. Greene, Craig J. Markin, Bram Wallace, Ben Krause, Margaux M. Pinney, James Fraser, Polly M Fordyce, Ali Madani, Nikhil Naik Designing and Evolving Neuron-Specific Proteases
Han Spinner, Colin Hemez, Julia McCreary, David Ruchien Liu, Debora Susan Marks Designing Proteins Using Sparse Data
Ada Shaw, Jung-Eun Shin, Nicole Nisha Thadani, Alan Nawzad Amin, Debora Susan Marks DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking
Gabriele Corso, Hannes Stärk, Bowen Jing, Regina Barzilay, Tommi S. Jaakkola DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking
Gabriele Corso, Hannes Stärk, Bowen Jing, Regina Barzilay, Tommi S. Jaakkola Differentially Private Adaptive Optimization with Delayed Preconditioners
Tian Li, Manzil Zaheer, Ken Liu, Sashank J. Reddi, Hugh Brendan McMahan, Virginia Smith Differentially Private CutMix for Split Learning with Vision Transformer
Seungeun Oh, Jihong Park, Sihun Baek, Hyelin Nam, Praneeth Vepakomma, Ramesh Raskar, Mehdi Bennis, Seong-Lyun Kim Differentially Private Federated Learning with Normalized Updates
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Alexander Matt Turner, Aseem Saxena, Prasad Tadepalli Foundation Models for History Compression in Reinforcement Learning
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Manasa Kesapragada, R Shane Canon, Sean P Jungbluth, Marcin P Joachimiak, Adam P Arkin, Paramvir S Dehal Generative Pretraining for Black-Box Optimization
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Amur Ghose, Apurv Gupta, Yaoliang Yu, Pascal Poupart Geometry of Inter-Areal Interactions in Mouse Visual Cortex
Ramakrishnan Iyer, Josh Siegle, Gayathri Mahalingam, Shawn R Olsen, Stefan Mihalas GIST: Distributed Training for Large-Scale Graph Convolutional Networks
Cameron R. Wolfe, Jingkang Yang, Fangshuo Liao, Arindam Chowdhury, Chen Dun, Artun Bayer, Santiago Segarra, Anastasios Kyrillidis GNM: A General Navigation Model to Drive Any Robot
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Teddy Koker, Keegan Quigley, Will Spaeth, Nathan C. Frey, Lin Li Graph Contrastive Learning with Cross-View Reconstruction
Qianlong Wen, Zhongyu Ouyang, Chunhui Zhang, Yiyue Qian, Yanfang Ye, Chuxu Zhang Graph Inverse Reinforcement Learning from Diverse Videos
Sateesh Kumar, Jonathan Zamora, Nicklas Hansen, Rishabh Jangir, Xiaolong Wang Graph Neural Networks for Multimodal Single-Cell Data Integration
Hongzhi Wen, Jiayuan Ding, Wei Jin, Yiqi Wang, Yuying Xie, Jiliang Tang Graph Q-Learning for Combinatorial Optimization
Victoria Magdalena Dax, Jiachen Li, Kevin Leahy, Mykel Kochenderfer GraphCG: Unsupervised Discovery of Steerable Factors in Graphs
Shengchao Liu, Chengpeng Wang, Weili Nie, Hanchen Wang, Jiarui Lu, Bolei Zhou, Jian Tang GraphFramEx: Towards Systematic Evaluation of Explainability Methods for Graph Neural Networks
Kenza Amara, Zhitao Ying, Zitao Zhang, Zhihao Han, Yinan Shan, Ulrik Brandes, Sebastian Schemm Group Privacy for Personalized Federated Learning
Filippo Galli, Sayan Biswas, Kangsoo Jung, Tommaso Cucinotta, Catuscia Palamidessi Group SELFIES: A Robust Fragment-Based Molecular String Representation
Austin Henry Cheng, Andy Cai, Santiago Miret, Gustavo Malkomes, Mariano Phielipp, Alan Aspuru-Guzik GroupMixNorm Layer for Learning Fair Models
Anubha Pandey, Aditi Rai, Maneet Singh, Deepak Bhatt, Tanmoy Bhowmik Guiding Offline Reinforcement Learning Using Safety Expert
Richa Verma, Kartik Bharadwaj, Harshad Khadilkar, Balaraman Ravindran Hamiltonian Neural Koopman Operator
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Emiliano Diaz, Kenza Tazi, Ashwin S. Braude, Daniel Okoh, Kara Lamb, Duncan Watson-Parris, Paula Harder, Nis Meinert Identifying Latent Distances with Finslerian Geometry
Alison Pouplin, David Eklund, Carl Henrik Ek, Søren Hauberg Identifying Structure in the MIMIC ICU Dataset
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Harman Singh, Poorva Garg, Mohit Gupta, Kevin Shah, Arnab Kumar Mondal, Dinesh Khandelwal, Parag Singla, Dinesh Garg Imitating Human Behaviour with Diffusion Models
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Alexandre Piché, Rafael Pardinas, David Vazquez, Igor Mordatch, Igor Mordatch, Christopher Pal Importance of Synthesizing High-Quality Data for Text-to-SQL Parsing
Yiyun Zhao, Jiarong Jiang, Yiqun Hu, Wuwei Lan, Henghui Zhu, Anuj Chauhan, Alexander Hanbo Li, Lin Pan, Jun Wang, Chung-Wei Hang, Sheng Zhang, Mingwen Dong, Joseph Lilien, Patrick Ng, Zhiguo Wang, Vittorio Castelli, Bing Xiang Improved Deep Neural Network Generalization Using M-Sharpness-Aware Minimization
Kayhan Behdin, Qingquan Song, Aman Gupta, David Durfee, Ayan Acharya, Sathiya Keerthi, Rahul Mazumder Improving Cross-Modal Attention via Object Detection
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Ragja Palakkadavath, Thanh Nguyen-Tang, Sunil Gupta, Svetha Venkatesh Improving ECG-Based COVID-19 Diagnosis and Mortality Predictions Using Pre-Pandemic Medical Records at Population-Scale
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Ariel Ricardo Ramos Vela, Johannes F. Lutzeyer, Anastasios Giovanidis, Michalis Vazirgiannis Improving the Robustness of Conditional Language Models by Detecting and Removing Input Noise
Kundan Krishna, Yao Zhao, Jie Ren, Balaji Lakshminarayanan, Jiaming Luo, Mohammad Saleh, Peter J Liu Improving Zero-Shot Generalization and Robustness of Multi-Modal Models
Yunhao Ge, Jie Ren, Ming-Hsuan Yang, Yuxiao Wang, Andrew Gallagher, Hartwig Adam, Laurent Itti, Balaji Lakshminarayanan, Jiaping Zhao In-Context Policy Iteration
Ethan Brooks, Logan A Walls, Richard Lewis, Satinder Singh In-Context Reinforcement Learning with Algorithm Distillation
Michael Laskin, Luyu Wang, Junhyuk Oh, Emilio Parisotto, Stephen Spencer, Richie Steigerwald, Dj Strouse, Steven Stenberg Hansen, Angelos Filos, Ethan Brooks, Maxime Gazeau, Himanshu Sahni, Satinder Singh, Volodymyr Mnih In-Context Reinforcement Learning with Algorithm Distillation
Michael Laskin, Luyu Wang, Junhyuk Oh, Emilio Parisotto, Stephen Spencer, Richie Steigerwald, Dj Strouse, Steven Stenberg Hansen, Angelos Filos, Ethan Brooks, Maxime Gazeau, Himanshu Sahni, Satinder Singh, Volodymyr Mnih Inferring Mood Disorder Symptoms from Multivariate Time-Series Sensory Data
Bryan M. Li, Filippo Corponi, Gerard Anmella, Ariadna Mas, Miriam Sanabra, Diego Hidalgo-Mazzei, Antonio Vergari Influencer Detection with Dynamic Graph Neural Networks
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Bradley Thomas Baker, Noah Lewis, Debratta Saha, Md Abdur Rahaman, Sergey Plis, Vince Calhoun Information Recovery via Matrix Completion for Piezoresponse Force Microscopy Data
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Adrien Ali Taiga, Rishabh Agarwal, Jesse Farebrother, Aaron Courville, Marc G Bellemare Is Brightfield All You Need for MoA Prediction?
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