أبحاث عن الذكاء الذكاء الاصطناعي

تحتوي هذه المادة على روابط لأكثر من 1500 ورقة علمية عن الذكاء الاصطناعي.

A graph similarity for deep learning Seongmin Ok
An Unsupervised Information-Theoretic Perceptual Quality Metric Sangnie Bhardwaj, Ian Fischer, Johannes Ballé, Troy Chinen
Self-Supervised MultiModal Versatile Networks Jean-Baptiste Alayrac, Adria Recasens, Rosalia Schneider, Relja Arandjelović, Jason Ramapuram, Jeffrey De Fauw, Lucas Smaira, Sander Dieleman, Andrew Zisserman
Benchmarking Deep Inverse Models over time, and the Neural-Adjoint method Simiao Ren, Willie Padilla, Jordan Malof
Off-Policy Evaluation and Learning for External Validity under a Covariate Shift Masatoshi Uehara, Masahiro Kato, Shota Yasui
Neural Methods for Point-wise Dependency Estimation Yao-Hung Hubert Tsai, Han Zhao, Makoto Yamada, Louis-Philippe Morency, Russ R. Salakhutdinov
Fast and Flexible Temporal Point Processes with Triangular Maps Oleksandr Shchur, Nicholas Gao, Marin Biloš, Stephan Günnemann
Backpropagating Linearly Improves Transferability of Adversarial Examples Yiwen Guo, Qizhang Li, Hao Chen
PyGlove: Symbolic Programming for Automated Machine Learning Daiyi Peng, Xuanyi Dong, Esteban Real, Mingxing Tan, Yifeng Lu, Gabriel Bender, Hanxiao Liu, Adam Kraft, Chen Liang, Quoc Le
Fourier Sparse Leverage Scores and Approximate Kernel Learning Tamas Erdelyi, Cameron Musco, Christopher Musco
Improved Algorithms for Online Submodular Maximization via First-order Regret Bounds Nicholas Harvey, Christopher Liaw, Tasuku Soma
Synbols: Probing Learning Algorithms with Synthetic Datasets Alexandre Lacoste, Pau Rodríguez López, Frederic Branchaud-Charron, Parmida Atighehchian, Massimo Caccia, Issam Hadj Laradji, Alexandre Drouin, Matthew Craddock, Laurent Charlin, David Vázquez
Adversarially Robust Streaming Algorithms via Differential Privacy Avinatan Hasidim, Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer
Trading Personalization for Accuracy: Data Debugging in Collaborative Filtering Long Chen, Yuan Yao, Feng Xu, Miao Xu, Hanghang Tong
Cascaded Text Generation with Markov Transformers Yuntian Deng, Alexander Rush
Improving Local Identifiability in Probabilistic Box Embeddings Shib Dasgupta, Michael Boratko, Dongxu Zhang, Luke Vilnis, Xiang Li, Andrew McCallum
Permute-and-Flip: A new mechanism for differentially private selection Ryan McKenna, Daniel R. Sheldon
Deep reconstruction of strange attractors from time series William Gilpin
Reciprocal Adversarial Learning via Characteristic Functions Shengxi Li, Zeyang Yu, Min Xiang, Danilo Mandic
Statistical Guarantees of Distributed Nearest Neighbor Classification Jiexin Duan, Xingye Qiao, Guang Cheng
Stein Self-Repulsive Dynamics: Benefits From Past Samples Mao Ye, Tongzheng Ren, Qiang Liu
The Statistical Complexity of Early-Stopped Mirror Descent Tomas Vaskevicius, Varun Kanade, Patrick Rebeschini
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach Amir-Hossein Karimi, Bodo Julius von Kügelgen, Bernhard Schölkopf, Isabel Valera
Quantitative Propagation of Chaos for SGD in Wide Neural Networks Valentin De Bortoli, Alain Durmus, Xavier Fontaine, Umut Simsekli
A Causal View on Robustness of Neural Networks Cheng Zhang, Kun Zhang, Yingzhen Li
Minimax Classification with 0-1 Loss and Performance Guarantees Santiago Mazuelas, Andrea Zanoni, Aritz Pérez
How to Learn a Useful Critic? Model-based Action-Gradient-Estimator Policy Optimization Pierluca D’Oro, Wojciech Jaśkowski
Coresets for Regressions with Panel Data Lingxiao Huang, K Sudhir, Nisheeth Vishnoi
Learning Composable Energy Surrogates for PDE Order Reduction Alex Beatson, Jordan Ash, Geoffrey Roeder, Tianju Xue, Ryan P. Adams
Efficient Contextual Bandits with Continuous Actions Maryam Majzoubi, Chicheng Zhang, Rajan Chari, Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins
Achieving Equalized Odds by Resampling Sensitive Attributes Yaniv Romano, Stephen Bates, Emmanuel Candes
Multi-Robot Collision Avoidance under Uncertainty with Probabilistic Safety Barrier Certificates Wenhao Luo, Wen Sun, Ashish Kapoor
Hard Shape-Constrained Kernel Machines Pierre-Cyril Aubin-Frankowski, Zoltan Szabo
A Closer Look at the Training Strategy for Modern Meta-Learning JIAXIN CHEN, Xiao-Ming Wu, Yanke Li, Qimai LI, Li-Ming Zhan, Fu-lai Chung
On the Value of Out-of-Distribution Testing: An Example of Goodhart’s Law Damien Teney, Ehsan Abbasnejad, Kushal Kafle, Robik Shrestha, Christopher Kanan, Anton van den Hengel
Generalised Bayesian Filtering via Sequential Monte Carlo Ayman Boustati, Omer Deniz Akyildiz, Theodoros Damoulas, Adam Johansen
Deterministic Approximation for Submodular Maximization over a Matroid in Nearly Linear Time Kai Han, zongmai Cao, Shuang Cui, Benwei Wu
Flows for simultaneous manifold learning and density estimation Johann Brehmer, Kyle Cranmer
Simultaneous Preference and Metric Learning from Paired Comparisons Austin Xu, Mark Davenport
Efficient Variational Inference for Sparse Deep Learning with Theoretical Guarantee Jincheng Bai, Qifan Song, Guang Cheng
Learning Manifold Implicitly via Explicit Heat-Kernel Learning Yufan Zhou, Changyou Chen, Jinhui Xu
Deep Relational Topic Modeling via Graph Poisson Gamma Belief Network Chaojie Wang, Hao Zhang, Bo Chen, Dongsheng Wang, Zhengjue Wang, Mingyuan Zhou
One-bit Supervision for Image Classification Hengtong Hu, Lingxi Xie, Zewei Du, Richang Hong, Qi Tian
What is being transferred in transfer learning? Behnam Neyshabur, Hanie Sedghi, Chiyuan Zhang
Submodular Maximization Through Barrier Functions Ashwinkumar Badanidiyuru, Amin Karbasi, Ehsan Kazemi, Jan Vondrak
Neural Networks with Recurrent Generative Feedback Yujia Huang, James Gornet, Sihui Dai, Zhiding Yu, Tan Nguyen, Doris Tsao, Anima Anandkumar
Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction Jinheon Baek, Dong Bok Lee, Sung Ju Hwang
Exploiting weakly supervised visual patterns to learn from partial annotations Kaustav Kundu, Joseph Tighe
Improving Inference for Neural Image Compression Yibo Yang, Robert Bamler, Stephan Mandt
Neuron Merging: Compensating for Pruned Neurons Woojeong Kim, Suhyun Kim, Mincheol Park, Geunseok Jeon
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence Kihyuk Sohn, David Berthelot, Nicholas Carlini, Zizhao Zhang, Han Zhang, Colin A. Raffel, Ekin Dogus Cubuk, Alexey Kurakin, Chun-Liang Li
Reinforcement Learning with Combinatorial Actions: An Application to Vehicle Routing Arthur Delarue, Ross Anderson, Christian Tjandraatmadja
Towards Playing Full MOBA Games with Deep Reinforcement Learning Deheng Ye, Guibin Chen, Wen Zhang, Sheng Chen, Bo Yuan, Bo Liu, Jia Chen, Zhao Liu, Fuhao Qiu, Hongsheng Yu, Yinyuting Yin, Bei Shi, Liang Wang, Tengfei Shi, Qiang Fu, Wei Yang, Lanxiao Huang, Wei Liu
Rankmax: An Adaptive Projection Alternative to the Softmax Function Weiwei Kong, Walid Krichene, Nicolas Mayoraz, Steffen Rendle, Li Zhang
Online Agnostic Boosting via Regret Minimization Nataly Brukhim, Xinyi Chen, Elad Hazan, Shay Moran
Causal Intervention for Weakly-Supervised Semantic Segmentation Dong Zhang, Hanwang Zhang, Jinhui Tang, Xian-Sheng Hua, Qianru Sun
Belief Propagation Neural Networks Jonathan Kuck, Shuvam Chakraborty, Hao Tang, Rachel Luo, Jiaming Song, Ashish Sabharwal, Stefano Ermon
Over-parameterized Adversarial Training: An Analysis Overcoming the Curse of Dimensionality Yi Zhang, Orestis Plevrakis, Simon S. Du, Xingguo Li, Zhao Song, Sanjeev Arora
Post-training Iterative Hierarchical Data Augmentation for Deep Networks Adil Khan, Khadija Fraz
Debugging Tests for Model Explanations Julius Adebayo, Michael Muelly, Ilaria Liccardi, Been Kim
Robust compressed sensing using generative models Ajil Jalal, Liu Liu, Alexandros G. Dimakis, Constantine Caramanis
Fairness without Demographics through Adversarially Reweighted Learning Preethi Lahoti, Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, Ed Chi
Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model Alex Lee, Anusha Nagabandi, Pieter Abbeel, Sergey Levine
Ridge Rider: Finding Diverse Solutions by Following Eigenvectors of the Hessian Jack Parker-Holder, Luke Metz, Cinjon Resnick, Hengyuan Hu, Adam Lerer, Alistair Letcher, Alexander Peysakhovich, Aldo Pacchiano, Jakob Foerster
The route to chaos in routing games: When is price of anarchy too optimistic? Thiparat Chotibut, Fryderyk Falniowski, Michał Misiurewicz, Georgios Piliouras
Online Algorithm for Unsupervised Sequential Selection with Contextual Information Arun Verma, Manjesh Kumar Hanawal, Csaba Szepesvari, Venkatesh Saligrama
Adapting Neural Architectures Between Domains Yanxi Li, Zhaohui Yang, Yunhe Wang, Chang Xu
What went wrong and when? Instance-wise feature importance for time-series black-box models Sana Tonekaboni, Shalmali Joshi, Kieran Campbell, David K. Duvenaud, Anna Goldenberg
Towards Better Generalization of Adaptive Gradient Methods Yingxue Zhou, Belhal Karimi, Jinxing Yu, Zhiqiang Xu, Ping Li
Learning Guidance Rewards with Trajectory-space Smoothing Tanmay Gangwani, Yuan Zhou, Jian Peng
Variance Reduction via Accelerated Dual Averaging for Finite-Sum Optimization Chaobing Song, Yong Jiang, Yi Ma
Tree! I am no Tree! I am a low dimensional Hyperbolic Embedding Rishi Sonthalia, Anna Gilbert
Deep Structural Causal Models for Tractable Counterfactual Inference Nick Pawlowski, Daniel Coelho de Castro, Ben Glocker
Convolutional Generation of Textured 3D Meshes Dario Pavllo, Graham Spinks, Thomas Hofmann, Marie-Francine Moens, Aurelien Lucchi
A Statistical Framework for Low-bitwidth Training of Deep Neural Networks Jianfei Chen, Yu Gai, Zhewei Yao, Michael W. Mahoney, Joseph E. Gonzalez
Better Set Representations For Relational Reasoning Qian Huang, Horace He, Abhay Singh, Yan Zhang, Ser Nam Lim, Austin R. Benson
AutoSync: Learning to Synchronize for Data-Parallel Distributed Deep Learning Hao Zhang, Yuan Li, Zhijie Deng, Xiaodan Liang, Lawrence Carin, Eric Xing
A Combinatorial Perspective on Transfer Learning Jianan Wang, Eren Sezener, David Budden, Marcus Hutter, Joel Veness
Hardness of Learning Neural Networks with Natural Weights Amit Daniely, Gal Vardi
Higher-Order Spectral Clustering of Directed Graphs Steinar Laenen, He Sun
Primal-Dual Mesh Convolutional Neural Networks Francesco Milano, Antonio Loquercio, Antoni Rosinol, Davide Scaramuzza, Luca Carlone
The Advantage of Conditional Meta-Learning for Biased Regularization and Fine Tuning Giulia Denevi, Massimiliano Pontil, Carlo Ciliberto
Watch out! Motion is Blurring the Vision of Your Deep Neural Networks Qing Guo, Felix Juefei-Xu, Xiaofei Xie, Lei Ma, Jian Wang, Bing Yu, Wei Feng, Yang Liu
Sinkhorn Barycenter via Functional Gradient Descent Zebang Shen, Zhenfu Wang, Alejandro Ribeiro, Hamed Hassani
Coresets for Near-Convex Functions Murad Tukan, Alaa Maalouf, Dan Feldman
Bayesian Deep Ensembles via the Neural Tangent Kernel Bobby He, Balaji Lakshminarayanan, Yee Whye Teh
Improved Schemes for Episodic Memory-based Lifelong Learning Yunhui Guo, Mingrui Liu, Tianbao Yang, Tajana Rosing
Adaptive Sampling for Stochastic Risk-Averse Learning Sebastian Curi, Kfir Y. Levy, Stefanie Jegelka, Andreas Krause
Deep Wiener Deconvolution: Wiener Meets Deep Learning for Image Deblurring Jiangxin Dong, Stefan Roth, Bernt Schiele
Discovering Reinforcement Learning Algorithms Junhyuk Oh, Matteo Hessel, Wojciech M. Czarnecki, Zhongwen Xu, Hado P. van Hasselt, Satinder Singh, David Silver
Taming Discrete Integration via the Boon of Dimensionality Jeffrey Dudek, Dror Fried, Kuldeep S Meel
Blind Video Temporal Consistency via Deep Video Prior Chenyang Lei, Yazhou Xing, Qifeng Chen
Simplify and Robustify Negative Sampling for Implicit Collaborative Filtering Jingtao Ding, Yuhan Quan, Quanming Yao, Yong Li, Depeng Jin
Model Selection for Production System via Automated Online Experiments Zhenwen Dai, Praveen Chandar, Ghazal Fazelnia, Benjamin Carterette, Mounia Lalmas
On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems Panayotis Mertikopoulos, Nadav Hallak, Ali Kavis, Volkan Cevher
Automatic Perturbation Analysis for Scalable Certified Robustness and Beyond Kaidi Xu, Zhouxing Shi, Huan Zhang, Yihan Wang, Kai-Wei Chang, Minlie Huang, Bhavya Kailkhura, Xue Lin, Cho-Jui Hsieh
Adaptation Properties Allow Identification of Optimized Neural Codes Luke Rast, Jan Drugowitsch
Global Convergence and Variance Reduction for a Class of Nonconvex-Nonconcave Minimax Problems Junchi Yang, Negar Kiyavash, Niao He
Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity Kaiqing Zhang, Sham Kakade, Tamer Basar, Lin Yang
Conservative Q-Learning for Offline Reinforcement Learning Aviral Kumar, Aurick Zhou, George Tucker, Sergey Levine
Online Influence Maximization under Linear Threshold Model Shuai Li, Fang Kong, Kejie Tang, Qizhi Li, Wei Chen
Ensembling geophysical models with Bayesian Neural Networks Ushnish Sengupta, Matt Amos, Scott Hosking, Carl Edward Rasmussen, Matthew Juniper, Paul Young
Delving into the Cyclic Mechanism in Semi-supervised Video Object Segmentation Yuxi Li, Ning Xu, Jinlong Peng, John See, Weiyao Lin
Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability Christopher Frye, Colin Rowat, Ilya Feige
Understanding Deep Architecture with Reasoning Layer Xinshi Chen, Yufei Zhang, Christoph Reisinger, Le Song
Planning in Markov Decision Processes with Gap-Dependent Sample Complexity Anders Jonsson, Emilie Kaufmann, Pierre Menard, Omar Darwiche Domingues, Edouard Leurent, Michal Valko
Provably Good Batch Off-Policy Reinforcement Learning Without Great Exploration Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill
Detection as Regression: Certified Object Detection with Median Smoothing Ping-yeh Chiang, Michael Curry, Ahmed Abdelkader, Aounon Kumar, John Dickerson, Tom Goldstein
Contextual Reserve Price Optimization in Auctions via Mixed Integer Programming Joey Huchette, Haihao Lu, Hossein Esfandiari, Vahab Mirrokni
ExpandNets: Linear Over-parameterization to Train Compact Convolutional Networks Shuxuan Guo, Jose M. Alvarez, Mathieu Salzmann
FleXOR: Trainable Fractional Quantization Dongsoo Lee, Se Jung Kwon, Byeongwook Kim, Yongkweon Jeon, Baeseong Park, Jeongin Yun
The Implications of Local Correlation on Learning Some Deep Functions Eran Malach, Shai Shalev-Shwartz
Learning to search efficiently for causally near-optimal treatments Samuel Håkansson, Viktor Lindblom, Omer Gottesman, Fredrik D. Johansson
A Game Theoretic Analysis of Additive Adversarial Attacks and Defenses Ambar Pal, Rene Vidal
Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts Bertrand Charpentier, Daniel Zügner, Stephan Günnemann
Recurrent Quantum Neural Networks Johannes Bausch
No-Regret Learning and Mixed Nash Equilibria: They Do Not Mix Emmanouil-Vasileios Vlatakis-Gkaragkounis, Lampros Flokas, Thanasis Lianeas, Panayotis Mertikopoulos, Georgios Piliouras
A Unifying View of Optimism in Episodic Reinforcement Learning Gergely Neu, Ciara Pike-Burke
Continuous Submodular Maximization: Beyond DR-Submodularity Moran Feldman, Amin Karbasi
An Asymptotically Optimal Primal-Dual Incremental Algorithm for Contextual Linear Bandits Andrea Tirinzoni, Matteo Pirotta, Marcello Restelli, Alessandro Lazaric
Assessing SATNet’s Ability to Solve the Symbol Grounding Problem Oscar Chang, Lampros Flokas, Hod Lipson, Michael Spranger
A Bayesian Nonparametrics View into Deep Representations Michał Jamroż, Marcin Kurdziel, Mateusz Opala
On the Similarity between the Laplace and Neural Tangent Kernels Amnon Geifman, Abhay Yadav, Yoni Kasten, Meirav Galun, David Jacobs, Basri Ronen
A causal view of compositional zero-shot recognition Yuval Atzmon, Felix Kreuk, Uri Shalit, Gal Chechik
HiPPO: Recurrent Memory with Optimal Polynomial Projections Albert Gu, Tri Dao, Stefano Ermon, Atri Rudra, Christopher Ré
Auto Learning Attention Benteng Ma, Jing Zhang, Yong Xia, Dacheng Tao
CASTLE: Regularization via Auxiliary Causal Graph Discovery Trent Kyono, Yao Zhang, Mihaela van der Schaar
Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect Kaihua Tang, Jianqiang Huang, Hanwang Zhang
Explainable Voting Dominik Peters, Ariel D. Procaccia, Alexandros Psomas, Zixin Zhou
Deep Archimedean Copulas Chun Kai Ling, Fei Fang, J. Zico Kolter
Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization Ben Letham, Roberto Calandra, Akshara Rai, Eytan Bakshy
UnModNet: Learning to Unwrap a Modulo Image for High Dynamic Range Imaging Chu Zhou, Hang Zhao, Jin Han, Chang Xu, Chao Xu, Tiejun Huang, Boxin Shi
Thunder: a Fast Coordinate Selection Solver for Sparse Learning Shaogang Ren, Weijie Zhao, Ping Li
Neural Networks Fail to Learn Periodic Functions and How to Fix It Liu Ziyin, Tilman Hartwig, Masahito Ueda
Distribution Matching for Crowd Counting Boyu Wang, Huidong Liu, Dimitris Samaras, Minh Hoai Nguyen
Correspondence learning via linearly-invariant embedding Riccardo Marin, Marie-Julie Rakotosaona, Simone Melzi, Maks Ovsjanikov
Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning Cong Zhang, Wen Song, Zhiguang Cao, Jie Zhang, Puay Siew Tan, Xu Chi
On Adaptive Attacks to Adversarial Example Defenses Florian Tramer, Nicholas Carlini, Wieland Brendel, Aleksander Madry
Sinkhorn Natural Gradient for Generative Models Zebang Shen, Zhenfu Wang, Alejandro Ribeiro, Hamed Hassani
Online Sinkhorn: Optimal Transport distances from sample streams Arthur Mensch, Gabriel Peyré
Ultrahyperbolic Representation Learning Marc Law, Jos Stam
Locally-Adaptive Nonparametric Online Learning Ilja Kuzborskij, Nicolò Cesa-Bianchi
Compositional Generalization via Neural-Symbolic Stack Machines Xinyun Chen, Chen Liang, Adams Wei Yu, Dawn Song, Denny Zhou
Graphon Neural Networks and the Transferability of Graph Neural Networks Luana Ruiz, Luiz Chamon, Alejandro Ribeiro
Unreasonable Effectiveness of Greedy Algorithms in Multi-Armed Bandit with Many Arms Mohsen Bayati, Nima Hamidi, Ramesh Johari, Khashayar Khosravi
Gamma-Models: Generative Temporal Difference Learning for Infinite-Horizon Prediction Michael Janner, Igor Mordatch, Sergey Levine
Deep Transformers with Latent Depth Xian Li, Asa Cooper Stickland, Yuqing Tang, Xiang Kong
Neural Mesh Flow: 3D Manifold Mesh Generation via Diffeomorphic Flows Kunal Gupta, Manmohan Chandraker
Statistical control for spatio-temporal MEG/EEG source imaging with desparsified mutli-task Lasso Jerome-Alexis Chevalier, Joseph Salmon, Alexandre Gramfort, Bertrand Thirion
A Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees Haoran Zhu, Pavankumar Murali, Dzung Phan, Lam Nguyen, Jayant Kalagnanam
Efficient Exact Verification of Binarized Neural Networks Kai Jia, Martin Rinard
Ultra-Low Precision 4-bit Training of Deep Neural Networks Xiao Sun, Naigang Wang, Chia-Yu Chen, Jiamin Ni, Ankur Agrawal, Xiaodong Cui, Swagath Venkataramani, Kaoutar El Maghraoui, Vijayalakshmi (Viji) Srinivasan, Kailash Gopalakrishnan
Bridging the Gap between Sample-based and One-shot Neural Architecture Search with BONAS Han Shi, Renjie Pi, Hang Xu, Zhenguo Li, James Kwok, Tong Zhang
On Numerosity of Deep Neural Networks Xi Zhang, Xiaolin Wu
Outlier Robust Mean Estimation with Subgaussian Rates via Stability Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia
Self-Supervised Relationship Probing Jiuxiang Gu, Jason Kuen, Shafiq Joty, Jianfei Cai, Vlad Morariu, Handong Zhao, Tong Sun
Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback Zifeng Wang, Xi Chen, Rui Wen, Shao-Lun Huang, Ercan Kuruoglu, Yefeng Zheng
Prophet Attention: Predicting Attention with Future Attention Fenglin Liu, Xuancheng Ren, Xian Wu, Shen Ge, Wei Fan, Yuexian Zou, Xu Sun
Language Models are Few-Shot Learners Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, Dario Amodei
Margins are Insufficient for Explaining Gradient Boosting Allan Grønlund, Lior Kamma, Kasper Green Larsen
Fourier-transform-based attribution priors improve the interpretability and stability of deep learning models for genomics Alex Tseng, Avanti Shrikumar, Anshul Kundaje
MomentumRNN: Integrating Momentum into Recurrent Neural Networks Tan Nguyen, Richard Baraniuk, Andrea Bertozzi, Stanley Osher, Bao Wang
Marginal Utility for Planning in Continuous or Large Discrete Action Spaces Zaheen Ahmad, Levi Lelis, Michael Bowling
Projected Stein Variational Gradient Descent Peng Chen, Omar Ghattas
Minimax Lower Bounds for Transfer Learning with Linear and One-hidden Layer Neural Networks Mohammadreza Mousavi Kalan, Zalan Fabian, Salman Avestimehr, Mahdi Soltanolkotabi
SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks Fabian Fuchs, Daniel Worrall, Volker Fischer, Max Welling
On the equivalence of molecular graph convolution and molecular wave function with poor basis set Masashi Tsubaki, Teruyasu Mizoguchi
The Power of Predictions in Online Control Chenkai Yu, Guanya Shi, Soon-Jo Chung, Yisong Yue, Adam Wierman
Learning Affordance Landscapes for Interaction Exploration in 3D Environments Tushar Nagarajan, Kristen Grauman
Cooperative Multi-player Bandit Optimization Ilai Bistritz, Nicholas Bambos
Tight First- and Second-Order Regret Bounds for Adversarial Linear Bandits Shinji Ito, Shuichi Hirahara, Tasuku Soma, Yuichi Yoshida
Just Pick a Sign: Optimizing Deep Multitask Models with Gradient Sign Dropout Zhao Chen, Jiquan Ngiam, Yanping Huang, Thang Luong, Henrik Kretzschmar, Yuning Chai, Dragomir Anguelov
A Loss Function for Generative Neural Networks Based on Watson’s Perceptual Model Steffen Czolbe, Oswin Krause, Ingemar Cox, Christian Igel
Dynamic Fusion of Eye Movement Data and Verbal Narrations in Knowledge-rich Domains Ervine Zheng, Qi Yu, Rui Li, Pengcheng Shi, Anne Haake
Scalable Multi-Agent Reinforcement Learning for Networked Systems with Average Reward Guannan Qu, Yiheng Lin, Adam Wierman, Na Li
Optimizing Neural Networks via Koopman Operator Theory Akshunna S. Dogra, William Redman
SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergence Sinho Chewi, Thibaut Le Gouic, Chen Lu, Tyler Maunu, Philippe Rigollet
Adversarial Robustness of Supervised Sparse Coding Jeremias Sulam, Ramchandran Muthukumar, Raman Arora
Differentiable Meta-Learning of Bandit Policies Craig Boutilier, Chih-wei Hsu, Branislav Kveton, Martin Mladenov, Csaba Szepesvari, Manzil Zaheer
Biologically Inspired Mechanisms for Adversarial Robustness Manish Vuyyuru Reddy, Andrzej Banburski, Nishka Pant, Tomaso Poggio
Statistical-Query Lower Bounds via Functional Gradients Surbhi Goel, Aravind Gollakota, Adam Klivans
Near-Optimal Reinforcement Learning with Self-Play Yu Bai, Chi Jin, Tiancheng Yu
Network Diffusions via Neural Mean-Field Dynamics Shushan He, Hongyuan Zha, Xiaojing Ye
Self-Distillation as Instance-Specific Label Smoothing Zhilu Zhang, Mert Sabuncu
Towards Problem-dependent Optimal Learning Rates Yunbei Xu, Assaf Zeevi
Cross-lingual Retrieval for Iterative Self-Supervised Training Chau Tran, Yuqing Tang, Xian Li, Jiatao Gu
Rethinking pooling in graph neural networks Diego Mesquita, Amauri Souza, Samuel Kaski
Pointer Graph Networks Petar Veličković, Lars Buesing, Matthew Overlan, Razvan Pascanu, Oriol Vinyals, Charles Blundell
Gradient Regularized V-Learning for Dynamic Treatment Regimes Yao Zhang, Mihaela van der Schaar
Faster Wasserstein Distance Estimation with the Sinkhorn Divergence Lénaïc Chizat, Pierre Roussillon, Flavien Léger, François-Xavier Vialard, Gabriel Peyré
Forethought and Hindsight in Credit Assignment Veronica Chelu, Doina Precup, Hado P. van Hasselt
Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification Hyun-Suk Lee, Yao Zhang, William Zame, Cong Shen, Jang-Won Lee, Mihaela van der Schaar
Rescuing neural spike train models from bad MLE Diego Arribas, Yuan Zhao, Il Memming Park
Lower Bounds and Optimal Algorithms for Personalized Federated Learning Filip Hanzely, Slavomír Hanzely, Samuel Horváth, Peter Richtarik
Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework Dinghuai Zhang, Mao Ye, Chengyue Gong, Zhanxing Zhu, Qiang Liu
Deep Imitation Learning for Bimanual Robotic Manipulation Fan Xie, Alexander Chowdhury, M. Clara De Paolis Kaluza, Linfeng Zhao, Lawson Wong, Rose Yu
Stationary Activations for Uncertainty Calibration in Deep Learning Lassi Meronen, Christabella Irwanto, Arno Solin
Ensemble Distillation for Robust Model Fusion in Federated Learning Tao Lin, Lingjing Kong, Sebastian U. Stich, Martin Jaggi
Falcon: Fast Spectral Inference on Encrypted Data Qian Lou, Wen-jie Lu, Cheng Hong, Lei Jiang
On Power Laws in Deep Ensembles Ekaterina Lobacheva, Nadezhda Chirkova, Maxim Kodryan, Dmitry P. Vetrov
Practical Quasi-Newton Methods for Training Deep Neural Networks Donald Goldfarb, Yi Ren, Achraf Bahamou
Approximation Based Variance Reduction for Reparameterization Gradients Tomas Geffner, Justin Domke
Inference Stage Optimization for Cross-scenario 3D Human Pose Estimation Jianfeng Zhang, Xuecheng Nie, Jiashi Feng
Consistent feature selection for analytic deep neural networks Vu C. Dinh, Lam S. Ho
Glance and Focus: a Dynamic Approach to Reducing Spatial Redundancy in Image Classification Yulin Wang, Kangchen Lv, Rui Huang, Shiji Song, Le Yang, Gao Huang
Information Maximization for Few-Shot Learning Malik Boudiaf, Imtiaz Ziko, Jérôme Rony, Jose Dolz, Pablo Piantanida, Ismail Ben Ayed
Inverse Reinforcement Learning from a Gradient-based Learner Giorgia Ramponi, Gianluca Drappo, Marcello Restelli
Bayesian Multi-type Mean Field Multi-agent Imitation Learning Fan Yang, Alina Vereshchaka, Changyou Chen, Wen Dong
Bayesian Robust Optimization for Imitation Learning Daniel Brown, Scott Niekum, Marek Petrik
Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance Lior Yariv, Yoni Kasten, Dror Moran, Meirav Galun, Matan Atzmon, Basri Ronen, Yaron Lipman
Riemannian Continuous Normalizing Flows Emile Mathieu, Maximilian Nickel
Attention-Gated Brain Propagation: How the brain can implement reward-based error backpropagation Isabella Pozzi, Sander Bohte, Pieter Roelfsema
Asymptotic Guarantees for Generative Modeling Based on the Smooth Wasserstein Distance Ziv Goldfeld, Kristjan Greenewald, Kengo Kato
Online Robust Regression via SGD on the l1 loss Scott Pesme, Nicolas Flammarion
PRANK: motion Prediction based on RANKing Yuriy Biktairov, Maxim Stebelev, Irina Rudenko, Oleh Shliazhko, Boris Yangel
Fighting Copycat Agents in Behavioral Cloning from Observation Histories Chuan Wen, Jierui Lin, Trevor Darrell, Dinesh Jayaraman, Yang Gao
Tight Nonparametric Convergence Rates for Stochastic Gradient Descent under the Noiseless Linear Model Raphaël Berthier, Francis Bach, Pierre Gaillard
Structured Prediction for Conditional Meta-Learning Ruohan Wang, Yiannis Demiris, Carlo Ciliberto
Optimal Lottery Tickets via Subset Sum: Logarithmic Over-Parameterization is Sufficient Ankit Pensia, Shashank Rajput, Alliot Nagle, Harit Vishwakarma, Dimitris Papailiopoulos
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Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization Sreejith Balakrishnan, Quoc Phong Nguyen, Bryan Kian Hsiang Low, Harold Soh
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On the Error Resistance of Hinge-Loss Minimization Kunal Talwar
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Object Goal Navigation using Goal-Oriented Semantic Exploration Devendra Singh Chaplot, Dhiraj Prakashchand Gandhi, Abhinav Gupta, Russ R. Salakhutdinov
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SuperLoss: A Generic Loss for Robust Curriculum Learning Thibault Castells, Philippe Weinzaepfel, Jerome Revaud
CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models Vijil Chenthamarakshan, Payel Das, Samuel Hoffman, Hendrik Strobelt, Inkit Padhi, Kar Wai Lim, Ben Hoover, Matteo Manica, Jannis Born, Teodoro Laino, Aleksandra Mojsilovic
Memory Based Trajectory-conditioned Policies for Learning from Sparse Rewards Yijie Guo, Jongwook Choi, Marcin Moczulski, Shengyu Feng, Samy Bengio, Mohammad Norouzi, Honglak Lee
Liberty or Depth: Deep Bayesian Neural Nets Do Not Need Complex Weight Posterior Approximations Sebastian Farquhar, Lewis Smith, Yarin Gal
Improving Sample Complexity Bounds for (Natural) Actor-Critic Algorithms Tengyu Xu, Zhe Wang, Yingbin Liang
Learning Differential Equations that are Easy to Solve Jacob Kelly, Jesse Bettencourt, Matthew J. Johnson, David K. Duvenaud
Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses Raef Bassily, Vitaly Feldman, Cristóbal Guzmán, Kunal Talwar
Influence-Augmented Online Planning for Complex Environments Jinke He, Miguel Suau de Castro, Frans Oliehoek
PAC-Bayes Learning Bounds for Sample-Dependent Priors Pranjal Awasthi, Satyen Kale, Stefani Karp, Mehryar Mohri
Reward-rational (implicit) choice: A unifying formalism for reward learning Hong Jun Jeon, Smitha Milli, Anca Dragan
Probabilistic Time Series Forecasting with Shape and Temporal Diversity Vincent LE GUEN, Nicolas THOME
Low Distortion Block-Resampling with Spatially Stochastic Networks Sarah Hong, Martin Arjovsky, Darryl Barnhart, Ian Thompson
Continual Deep Learning by Functional Regularisation of Memorable Past Pingbo Pan, Siddharth Swaroop, Alexander Immer, Runa Eschenhagen, Richard Turner, Mohammad Emtiyaz E. Khan
Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation Learning Pan Li, Yanbang Wang, Hongwei Wang, Jure Leskovec
Fast Fourier Convolution Lu Chi, Borui Jiang, Yadong Mu
Unsupervised Learning of Dense Visual Representations Pedro O. O. Pinheiro, Amjad Almahairi, Ryan Benmalek, Florian Golemo, Aaron C. Courville
Higher-Order Certification For Randomized Smoothing Jeet Mohapatra, Ching-Yun Ko, Tsui-Wei Weng, Pin-Yu Chen, Sijia Liu, Luca Daniel
Learning Structured Distributions From Untrusted Batches: Faster and Simpler Sitan Chen, Jerry Li, Ankur Moitra
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Variational Policy Gradient Method for Reinforcement Learning with General Utilities Junyu Zhang, Alec Koppel, Amrit Singh Bedi, Csaba Szepesvari, Mengdi Wang
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Temporal Positive-unlabeled Learning for Biomedical Hypothesis Generation via Risk Estimation Uchenna Akujuobi, Jun Chen, Mohamed Elhoseiny, Michael Spranger, Xiangliang Zhang
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Probabilistic Circuits for Variational Inference in Discrete Graphical Models Andy Shih, Stefano Ermon
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Robust Meta-learning for Mixed Linear Regression with Small Batches Weihao Kong, Raghav Somani, Sham Kakade, Sewoong Oh
Bayesian Deep Learning and a Probabilistic Perspective of Generalization Andrew G. Wilson, Pavel Izmailov
Unsupervised Learning of Object Landmarks via Self-Training Correspondence Dimitrios Mallis, Enrique Sanchez, Matthew Bell, Georgios Tzimiropoulos
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Learning Representations from Audio-Visual Spatial Alignment Pedro Morgado, Yi Li, Nuno Nvasconcelos
Generative View Synthesis: From Single-view Semantics to Novel-view Images Tewodros Amberbir Habtegebrial, Varun Jampani, Orazio Gallo, Didier Stricker
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Multi-Task Reinforcement Learning with Soft Modularization Ruihan Yang, Huazhe Xu, YI WU, Xiaolong Wang
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Improved Algorithms for Convex-Concave Minimax Optimization Yuanhao Wang, Jian Li
Deep Variational Instance Segmentation Jialin Yuan, Chao Chen, Fuxin Li
Learning Implicit Functions for Topology-Varying Dense 3D Shape Correspondence Feng Liu, Xiaoming Liu
Deep Multimodal Fusion by Channel Exchanging Yikai Wang, Wenbing Huang, Fuchun Sun, Tingyang Xu, Yu Rong, Junzhou Huang
Hierarchically Organized Latent Modules for Exploratory Search in Morphogenetic Systems Mayalen Etcheverry, Clément Moulin-Frier, Pierre-Yves Oudeyer
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Probabilistic Orientation Estimation with Matrix Fisher Distributions David Mohlin, Josephine Sullivan, Gérald Bianchi
Minimax Dynamics of Optimally Balanced Spiking Networks of Excitatory and Inhibitory Neurons Qianyi Li, Cengiz Pehlevan
Telescoping Density-Ratio Estimation Benjamin Rhodes, Kai Xu, Michael U. Gutmann
Towards Deeper Graph Neural Networks with Differentiable Group Normalization Kaixiong Zhou, Xiao Huang, Yuening Li, Daochen Zha, Rui Chen, Xia Hu
Stochastic Optimization for Performative Prediction Celestine Mendler-Dünner, Juan Perdomo, Tijana Zrnic, Moritz Hardt
Learning Differentiable Programs with Admissible Neural Heuristics Ameesh Shah, Eric Zhan, Jennifer Sun, Abhinav Verma, Yisong Yue, Swarat Chaudhuri
Improved guarantees and a multiple-descent curve for Column Subset Selection and the Nystrom method Michal Derezinski, Rajiv Khanna, Michael W. Mahoney
Domain Adaptation as a Problem of Inference on Graphical Models Kun Zhang, Mingming Gong, Petar Stojanov, Biwei Huang, QINGSONG LIU, Clark Glymour
Network size and size of the weights in memorization with two-layers neural networks Sebastien Bubeck, Ronen Eldan, Yin Tat Lee, Dan Mikulincer
Certifying Strategyproof Auction Networks Michael Curry, Ping-yeh Chiang, Tom Goldstein, John Dickerson
Continual Learning of Control Primitives : Skill Discovery via Reset-Games Kelvin Xu, Siddharth Verma, Chelsea Finn, Sergey Levine
HOI Analysis: Integrating and Decomposing Human-Object Interaction Yong-Lu Li, Xinpeng Liu, Xiaoqian Wu, Yizhuo Li, Cewu Lu
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Deep Direct Likelihood Knockoffs Mukund Sudarshan, Wesley Tansey, Rajesh Ranganath
Meta-Neighborhoods Siyuan Shan, Yang Li, Junier B. Oliva
Neural Dynamic Policies for End-to-End Sensorimotor Learning Shikhar Bahl, Mustafa Mukadam, Abhinav Gupta, Deepak Pathak
A new inference approach for training shallow and deep generalized linear models of noisy interacting neurons Gabriel Mahuas, Giulio Isacchini, Olivier Marre, Ulisse Ferrari, Thierry Mora
Decision-Making with Auto-Encoding Variational Bayes Romain Lopez, Pierre Boyeau, Nir Yosef, Michael Jordan, Jeffrey Regier
Attribution Preservation in Network Compression for Reliable Network Interpretation Geondo Park, June Yong Yang, Sung Ju Hwang, Eunho Yang
Feature Importance Ranking for Deep Learning Maksymilian Wojtas, Ke Chen
Causal Estimation with Functional Confounders Aahlad Puli, Adler Perotte , Rajesh Ranganath
Model Inversion Networks for Model-Based Optimization Aviral Kumar, Sergey Levine
Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks Umut Simsekli, Ozan Sener, George Deligiannidis, Murat A. Erdogdu
Exact expressions for double descent and implicit regularization via surrogate random design Michal Derezinski, Feynman T. Liang, Michael W. Mahoney
Certifying Confidence via Randomized Smoothing Aounon Kumar, Alexander Levine, Soheil Feizi, Tom Goldstein
Learning Physical Constraints with Neural Projections Shuqi Yang, Xingzhe He, Bo Zhu
Robust Optimization for Fairness with Noisy Protected Groups Serena Wang, Wenshuo Guo, Harikrishna Narasimhan, Andrew Cotter, Maya Gupta, Michael Jordan
Noise-Contrastive Estimation for Multivariate Point Processes Hongyuan Mei, Tom Wan, Jason Eisner
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Sparse Graphical Memory for Robust Planning Scott Emmons, Ajay Jain, Misha Laskin, Thanard Kurutach, Pieter Abbeel, Deepak Pathak
Second Order PAC-Bayesian Bounds for the Weighted Majority Vote Andres Masegosa, Stephan Lorenzen, Christian Igel, Yevgeny Seldin
Dirichlet Graph Variational Autoencoder Jia Li, Jianwei Yu, Jiajin Li, Honglei Zhang, Kangfei Zhao, Yu Rong, Hong Cheng, Junzhou Huang
Modeling Task Effects on Meaning Representation in the Brain via Zero-Shot MEG Prediction Mariya Toneva, Otilia Stretcu, Barnabas Poczos, Leila Wehbe, Tom M. Mitchell
Counterfactual Vision-and-Language Navigation: Unravelling the Unseen Amin Parvaneh, Ehsan Abbasnejad, Damien Teney, Qinfeng Shi, Anton van den Hengel
Robust Quantization: One Model to Rule Them All Moran Shkolnik, Brian Chmiel, Ron Banner, Gil Shomron, Yury Nahshan, Alex Bronstein, Uri Weiser
Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming Sumanth Dathathri, Krishnamurthy Dvijotham, Alexey Kurakin, Aditi Raghunathan, Jonathan Uesato, Rudy R. Bunel, Shreya Shankar, Jacob Steinhardt, Ian Goodfellow, Percy S. Liang, Pushmeet Kohli
Federated Accelerated Stochastic Gradient Descent Honglin Yuan, Tengyu Ma
Robust Density Estimation under Besov IPM Losses Ananya Uppal, Shashank Singh, Barnabas Poczos
An analytic theory of shallow networks dynamics for hinge loss classification Franco Pellegrini, Giulio Biroli
Fixed-Support Wasserstein Barycenters: Computational Hardness and Fast Algorithm Tianyi Lin, Nhat Ho, Xi Chen, Marco Cuturi, Michael Jordan
Learning to Orient Surfaces by Self-supervised Spherical CNNs Riccardo Spezialetti, Federico Stella, Marlon Marcon, Luciano Silva, Samuele Salti, Luigi Di Stefano
Adam with Bandit Sampling for Deep Learning Rui Liu, Tianyi Wu, Barzan Mozafari
Parabolic Approximation Line Search for DNNs Maximus Mutschler, Andreas Zell
Agnostic Learning of a Single Neuron with Gradient Descent Spencer Frei, Yuan Cao, Quanquan Gu
Statistical Efficiency of Thompson Sampling for Combinatorial Semi-Bandits Pierre Perrault, Etienne Boursier, Michal Valko, Vianney Perchet
Analytic Characterization of the Hessian in Shallow ReLU Models: A Tale of Symmetry Yossi Arjevani, Michael Field
Generative causal explanations of black-box classifiers Matthew O’Shaughnessy, Gregory Canal, Marissa Connor, Christopher Rozell, Mark Davenport
Sub-sampling for Efficient Non-Parametric Bandit Exploration Dorian Baudry, Emilie Kaufmann, Odalric-Ambrym Maillard
Learning under Model Misspecification: Applications to Variational and Ensemble methods Andres Masegosa
Language Through a Prism: A Spectral Approach for Multiscale Language Representations Alex Tamkin, Dan Jurafsky, Noah Goodman
DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensembles Huanrui Yang, Jingyang Zhang, Hongliang Dong, Nathan Inkawhich, Andrew Gardner, Andrew Touchet, Wesley Wilkes, Heath Berry, Hai Li
Towards practical differentially private causal graph discovery Lun Wang, Qi Pang, Dawn Song
Independent Policy Gradient Methods for Competitive Reinforcement Learning Constantinos Daskalakis, Dylan J. Foster, Noah Golowich
The Value Equivalence Principle for Model-Based Reinforcement Learning Christopher Grimm, Andre Barreto, Satinder Singh, David Silver
Structured Convolutions for Efficient Neural Network Design Yash Bhalgat, Yizhe Zhang, Jamie Menjay Lin, Fatih Porikli
Latent World Models For Intrinsically Motivated Exploration Aleksandr Ermolov, Nicu Sebe
Estimating Rank-One Spikes from Heavy-Tailed Noise via Self-Avoiding Walks Jingqiu Ding, Samuel Hopkins, David Steurer
Policy Improvement via Imitation of Multiple Oracles Ching-An Cheng, Andrey Kolobov, Alekh Agarwal
Training Generative Adversarial Networks by Solving Ordinary Differential Equations Chongli Qin, Yan Wu, Jost Tobias Springenberg, Andy Brock, Jeff Donahue, Timothy Lillicrap, Pushmeet Kohli
Learning of Discrete Graphical Models with Neural Networks Abhijith Jayakumar, Andrey Lokhov, Sidhant Misra, Marc Vuffray
RepPoints v2: Verification Meets Regression for Object Detection Yihong Chen, Zheng Zhang, Yue Cao, Liwei Wang, Stephen Lin, Han Hu
Unfolding the Alternating Optimization for Blind Super Resolution zhengxiong luo, Yan Huang, Shang Li, Liang Wang, Tieniu Tan
Entrywise convergence of iterative methods for eigenproblems Vasileios Charisopoulos, Austin R. Benson, Anil Damle
Learning Object-Centric Representations of Multi-Object Scenes from Multiple Views Li Nanbo, Cian Eastwood, Robert Fisher
A Catalyst Framework for Minimax Optimization Junchi Yang, Siqi Zhang, Negar Kiyavash, Niao He
Self-supervised Co-Training for Video Representation Learning Tengda Han, Weidi Xie, Andrew Zisserman
Gradient Estimation with Stochastic Softmax Tricks Max Paulus, Dami Choi, Daniel Tarlow, Andreas Krause, Chris J. Maddison
Meta-Learning Requires Meta-Augmentation Janarthanan Rajendran, Alexander Irpan, Eric Jang
SLIP: Learning to predict in unknown dynamical systems with long-term memory Paria Rashidinejad, Jiantao Jiao, Stuart Russell
Improving GAN Training with Probability Ratio Clipping and Sample Reweighting Yue Wu, Pan Zhou, Andrew G. Wilson, Eric Xing, Zhiting Hu
Bayesian Bits: Unifying Quantization and Pruning Mart van Baalen, Christos Louizos, Markus Nagel, Rana Ali Amjad, Ying Wang, Tijmen Blankevoort, Max Welling
On Testing of Samplers Kuldeep S Meel, Yash Pralhad Pote, Sourav Chakraborty
Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective Vu Nguyen, Vaden Masrani, Rob Brekelmans, Michael Osborne, Frank Wood
MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers Wenhui Wang, Furu Wei, Li Dong, Hangbo Bao, Nan Yang, Ming Zhou
Optimal Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization Yan Yan, Yi Xu, Qihang Lin, Wei Liu, Tianbao Yang
Woodbury Transformations for Deep Generative Flows You Lu, Bert Huang
Graph Contrastive Learning with Augmentations Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, Yang Shen
Gradient Surgery for Multi-Task Learning Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, Chelsea Finn
Bayesian Probabilistic Numerical Integration with Tree-Based Models Harrison Zhu, Xing Liu, Ruya Kang, Zhichao Shen, Seth Flaxman, Francois-Xavier Briol
Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel Stanislav Fort, Gintare Karolina Dziugaite, Mansheej Paul, Sepideh Kharaghani, Daniel M. Roy, Surya Ganguli
Graph Meta Learning via Local Subgraphs Kexin Huang, Marinka Zitnik
Stochastic Deep Gaussian Processes over Graphs Naiqi Li, Wenjie Li, Jifeng Sun, Yinghua Gao, Yong Jiang, Shu-Tao Xia
Bayesian Causal Structural Learning with Zero-Inflated Poisson Bayesian Networks Junsouk Choi, Robert Chapkin, Yang Ni
Evaluating Attribution for Graph Neural Networks Benjamin Sanchez-Lengeling, Jennifer Wei, Brian Lee, Emily Reif, Peter Wang, Wesley Wei Qian, Kevin McCloskey, Lucy Colwell , Alexander Wiltschko
On Second Order Behaviour in Augmented Neural ODEs Alexander Norcliffe, Cristian Bodnar, Ben Day, Nikola Simidjievski, Pietro Lió
Neuron Shapley: Discovering the Responsible Neurons Amirata Ghorbani, James Y. Zou
Stochastic Normalizing Flows Hao Wu, Jonas Köhler, Frank Noe
GPU-Accelerated Primal Learning for Extremely Fast Large-Scale Classification John T. Halloran, David M. Rocke
Random Reshuffling is Not Always Better Christopher M. De Sa
Model Agnostic Multilevel Explanations Karthikeyan Natesan Ramamurthy, Bhanukiran Vinzamuri, Yunfeng Zhang, Amit Dhurandhar
NeuMiss networks: differentiable programming for supervised learning with missing values. Marine Le Morvan, Julie Josse, Thomas Moreau, Erwan Scornet, Gael Varoquaux
Revisiting Parameter Sharing for Automatic Neural Channel Number Search Jiaxing Wang, Haoli Bai, Jiaxiang Wu, Xupeng Shi, Junzhou Huang, Irwin King, Michael Lyu, Jian Cheng
Differentially-Private Federated Linear Bandits Abhimanyu Dubey, Alex `Sandy’ Pentland
Is Plug-in Solver Sample-Efficient for Feature-based Reinforcement Learning? Qiwen Cui, Lin Yang
Learning Physical Graph Representations from Visual Scenes Daniel Bear, Chaofei Fan, Damian Mrowca, Yunzhu Li, Seth Alter, Aran Nayebi, Jeremy Schwartz, Li F. Fei-Fei, Jiajun Wu, Josh Tenenbaum, Daniel L. Yamins
Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking Anqi Wu, Estefany Kelly Buchanan, Matthew Whiteway, Michael Schartner, Guido Meijer, Jean-Paul Noel, Erica Rodriguez, Claire Everett, Amy Norovich, Evan Schaffer, Neeli Mishra, C. Daniel Salzman, Dora Angelaki, Andrés Bendesky, The International Brain Laboratory The International Brain Laboratory, John P. Cunningham, Liam Paninski
Meta-learning from Tasks with Heterogeneous Attribute Spaces Tomoharu Iwata, Atsutoshi Kumagai
Estimating decision tree learnability with polylogarithmic sample complexity Guy Blanc, Neha Gupta, Jane Lange, Li-Yang Tan
Sparse Symplectically Integrated Neural Networks Daniel DiPietro, Shiying Xiong, Bo Zhu
Continuous Object Representation Networks: Novel View Synthesis without Target View Supervision Nicolai Hani, Selim Engin, Jun-Jee Chao, Volkan Isler
Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence Thomas Sutter, Imant Daunhawer, Julia Vogt
Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers Kiwon Um, Robert Brand, Yun (Raymond) Fei, Philipp Holl, Nils Thuerey
Reinforcement Learning with General Value Function Approximation: Provably Efficient Approach via Bounded Eluder Dimension Ruosong Wang, Russ R. Salakhutdinov, Lin Yang
Predicting Training Time Without Training Luca Zancato, Alessandro Achille, Avinash Ravichandran, Rahul Bhotika, Stefano Soatto
How does This Interaction Affect Me? Interpretable Attribution for Feature Interactions Michael Tsang, Sirisha Rambhatla, Yan Liu
Optimal Adaptive Electrode Selection to Maximize Simultaneously Recorded Neuron Yield John Choi, Krishan Kumar, Mohammad Khazali, Katie Wingel, Mahdi Choudhury, Adam S. Charles, Bijan Pesaran
Neurosymbolic Reinforcement Learning with Formally Verified Exploration Greg Anderson, Abhinav Verma, Isil Dillig, Swarat Chaudhuri
Wavelet Flow: Fast Training of High Resolution Normalizing Flows Jason Yu, Konstantinos Derpanis, Marcus A. Brubaker
Multi-task Batch Reinforcement Learning with Metric Learning Jiachen Li, Quan Vuong, Shuang Liu, Minghua Liu, Kamil Ciosek, Henrik Christensen, Hao Su
On 1/n neural representation and robustness Josue Nassar, Piotr Sokol, Sueyeon Chung, Kenneth D. Harris, Il Memming Park
Boundary thickness and robustness in learning models Yaoqing Yang, Rajiv Khanna, Yaodong Yu, Amir Gholami, Kurt Keutzer, Joseph E. Gonzalez, Kannan Ramchandran, Michael W. Mahoney
Demixed shared component analysis of neural population data from multiple brain areas Yu Takagi, Steven Kennerley, Jun-ichiro Hirayama, Laurence Hunt
Learning Kernel Tests Without Data Splitting Jonas Kübler, Wittawat Jitkrittum, Bernhard Schölkopf, Krikamol Muandet
Unsupervised Data Augmentation for Consistency Training Qizhe Xie, Zihang Dai, Eduard Hovy, Thang Luong, Quoc Le
Subgroup-based Rank-1 Lattice Quasi-Monte Carlo Yueming LYU, Yuan Yuan, Ivor Tsang
Minibatch vs Local SGD for Heterogeneous Distributed Learning Blake E. Woodworth, Kumar Kshitij Patel, Nati Srebro
Multi-task Causal Learning with Gaussian Processes Virginia Aglietti, Theodoros Damoulas, Mauricio Álvarez, Javier Gonzalez
Proximity Operator of the Matrix Perspective Function and its Applications Joong-Ho Won
Generative 3D Part Assembly via Dynamic Graph Learning 佳磊黄, Guanqi Zhan, Qingnan Fan, Kaichun Mo, Lin Shao, Baoquan Chen, Leonidas J. Guibas, Hao Dong
Improving Natural Language Processing Tasks with Human Gaze-Guided Neural Attention Ekta Sood, Simon Tannert, Philipp Mueller, Andreas Bulling
The Power of Comparisons for Actively Learning Linear Classifiers Max Hopkins, Daniel Kane, Shachar Lovett
From Boltzmann Machines to Neural Networks and Back Again Surbhi Goel, Adam Klivans, Frederic Koehler
Crush Optimism with Pessimism: Structured Bandits Beyond Asymptotic Optimality Kwang-Sung Jun, Chicheng Zhang
Pruning neural networks without any data by iteratively conserving synaptic flow Hidenori Tanaka, Daniel Kunin, Daniel L. Yamins, Surya Ganguli
Detecting Interactions from Neural Networks via Topological Analysis Zirui Liu, Qingquan Song, Kaixiong Zhou, Ting-Hsiang Wang, Ying Shan, Xia Hu
Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systems Aman Sinha, Matthew O’Kelly, Russ Tedrake, John C. Duchi
Interpretable and Personalized Apprenticeship Scheduling: Learning Interpretable Scheduling Policies from Heterogeneous User Demonstrations Rohan Paleja, Andrew Silva, Letian Chen, Matthew Gombolay
Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes Mengdi Xu, Wenhao Ding, Jiacheng Zhu, ZUXIN LIU, Baiming Chen, Ding Zhao
Benchmarking Deep Learning Interpretability in Time Series Predictions Aya Abdelsalam Ismail, Mohamed Gunady, Hector Corrada Bravo, Soheil Feizi
Federated Principal Component Analysis Andreas Grammenos, Rodrigo Mendoza Smith, Jon Crowcroft, Cecilia Mascolo
(De)Randomized Smoothing for Certifiable Defense against Patch Attacks Alexander Levine, Soheil Feizi
SMYRF – Efficient Attention using Asymmetric Clustering Giannis Daras, Nikita Kitaev, Augustus Odena, Alexandros G. Dimakis
Introducing Routing Uncertainty in Capsule Networks Fabio De Sousa Ribeiro, Georgios Leontidis, Stefanos Kollias
A Simple and Efficient Smoothing Method for Faster Optimization and Local Exploration Kevin Scaman, Ludovic DOS SANTOS, Merwan Barlier, Igor Colin
Hyperparameter Ensembles for Robustness and Uncertainty Quantification Florian Wenzel, Jasper Snoek, Dustin Tran, Rodolphe Jenatton
Neutralizing Self-Selection Bias in Sampling for Sortition Bailey Flanigan, Paul Goelz, Anupam Gupta, Ariel D. Procaccia
On the Convergence of Smooth Regularized Approximate Value Iteration Schemes Elena Smirnova, Elvis Dohmatob
Off-Policy Evaluation via the Regularized Lagrangian Mengjiao Yang, Ofir Nachum, Bo Dai, Lihong Li, Dale Schuurmans
The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learning Harm Van Seijen, Hadi Nekoei, Evan Racah, Sarath Chandar
Neural Power Units Niklas Maximilian Heim, Tomas Pevny, Vasek Smidl
Towards Scalable Bayesian Learning of Causal DAGs Jussi Viinikka, Antti Hyttinen, Johan Pensar, Mikko Koivisto
A Dictionary Approach to Domain-Invariant Learning in Deep Networks Ze Wang, Xiuyuan Cheng, Guillermo Sapiro, Qiang Qiu
Bootstrapping neural processes Juho Lee, Yoonho Lee, Jungtaek Kim, Eunho Yang, Sung Ju Hwang, Yee Whye Teh
Large-Scale Adversarial Training for Vision-and-Language Representation Learning Zhe Gan, Yen-Chun Chen, Linjie Li, Chen Zhu, Yu Cheng, Jingjing Liu
Most ReLU Networks Suffer from $ℓ^{2}$ Adversarial Perturbations Amit Daniely, Hadas Shacham
Compositional Visual Generation with Energy Based Models Yilun Du, Shuang Li, Igor Mordatch
Factor Graph Grammars David Chiang, Darcey Riley
Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs Nikolaos Karalias, Andreas Loukas
Autoregressive Score Matching Chenlin Meng, Lantao Yu, Yang Song, Jiaming Song, Stefano Ermon
Debiasing Distributed Second Order Optimization with Surrogate Sketching and Scaled Regularization Michal Derezinski, Burak Bartan, Mert Pilanci, Michael W. Mahoney
Neural Controlled Differential Equations for Irregular Time Series Patrick Kidger, James Morrill, James Foster, Terry Lyons
On Efficiency in Hierarchical Reinforcement Learning Zheng Wen, Doina Precup, Morteza Ibrahimi, Andre Barreto, Benjamin Van Roy, Satinder Singh
On Correctness of Automatic Differentiation for Non-Differentiable Functions Wonyeol Lee, Hangyeol Yu, Xavier Rival, Hongseok Yang
Probabilistic Linear Solvers for Machine Learning Jonathan Wenger, Philipp Hennig
Dynamic Regret of Policy Optimization in Non-Stationary Environments Yingjie Fei, Zhuoran Yang, Zhaoran Wang, Qiaomin Xie
Multipole Graph Neural Operator for Parametric Partial Differential Equations Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Andrew Stuart, Kaushik Bhattacharya, Anima Anandkumar
BlockGAN: Learning 3D Object-aware Scene Representations from Unlabelled Images Thu H. Nguyen-Phuoc, Christian Richardt, Long Mai, Yongliang Yang, Niloy Mitra
Online Structured Meta-learning Huaxiu Yao, Yingbo Zhou, Mehrdad Mahdavi, Zhenhui (Jessie) Li, Richard Socher, Caiming Xiong
Learning Strategic Network Emergence Games Rakshit Trivedi, Hongyuan Zha
Towards Interpretable Natural Language Understanding with Explanations as Latent Variables Wangchunshu Zhou, Jinyi Hu, Hanlin Zhang, Xiaodan Liang, Maosong Sun, Chenyan Xiong, Jian Tang
The Mean-Squared Error of Double Q-Learning Wentao Weng, Harsh Gupta, Niao He, Lei Ying, R. Srikant
What Makes for Good Views for Contrastive Learning? Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, Phillip Isola
Denoising Diffusion Probabilistic Models Jonathan Ho, Ajay Jain, Pieter Abbeel
Barking up the right tree: an approach to search over molecule synthesis DAGs John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin Segler, José Miguel Hernández-Lobato
On Uniform Convergence and Low-Norm Interpolation Learning Lijia Zhou, D.J. Sutherland, Nati Srebro
Bandit Samplers for Training Graph Neural Networks Ziqi Liu, Zhengwei Wu, Zhiqiang Zhang, Jun Zhou, Shuang Yang, Le Song, Yuan Qi
Sampling from a k-DPP without looking at all items Daniele Calandriello, Michal Derezinski, Michal Valko
Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence Bastian Rieck, Tristan Yates, Christian Bock, Karsten Borgwardt, Guy Wolf, Nicholas Turk-Browne, Smita Krishnaswamy
Hierarchical Poset Decoding for Compositional Generalization in Language Yinuo Guo, Zeqi Lin, Jian-Guang Lou, Dongmei Zhang
Evaluating and Rewarding Teamwork Using Cooperative Game Abstractions Tom Yan, Christian Kroer, Alexander Peysakhovich
Exchangeable Neural ODE for Set Modeling Yang Li, Haidong Yi, Christopher Bender, Siyuan Shan, Junier B. Oliva
Profile Entropy: A Fundamental Measure for the Learnability and Compressibility of Distributions Yi Hao, Alon Orlitsky
CoADNet: Collaborative Aggregation-and-Distribution Networks for Co-Salient Object Detection Qijian Zhang, Runmin Cong, Junhui Hou, Chongyi Li, Yao Zhao
Regularized linear autoencoders recover the principal components, eventually Xuchan Bao, James Lucas, Sushant Sachdeva, Roger B. Grosse
Semi-Supervised Partial Label Learning via Confidence-Rated Margin Maximization Wei Wang, Min-Ling Zhang
GramGAN: Deep 3D Texture Synthesis From 2D Exemplars Tiziano Portenier, Siavash Arjomand Bigdeli, Orcun Goksel
UWSOD: Toward Fully-Supervised-Level Capacity Weakly Supervised Object Detection Yunhang Shen, Rongrong Ji, Zhiwei Chen, Yongjian Wu, Feiyue Huang
Learning Restricted Boltzmann Machines with Sparse Latent Variables Guy Bresler, Rares-Darius Buhai
Sample Complexity of Asynchronous Q-Learning: Sharper Analysis and Variance Reduction Gen Li, Yuting Wei, Yuejie Chi, Yuantao Gu, Yuxin Chen
Curriculum learning for multilevel budgeted combinatorial problems Adel Nabli, Margarida Carvalho
FedSplit: an algorithmic framework for fast federated optimization Reese Pathak, Martin J. Wainwright
Estimation and Imputation in Probabilistic Principal Component Analysis with Missing Not At Random Data Aude Sportisse, Claire Boyer, Julie Josses
Correlation Robust Influence Maximization Louis Chen, Divya Padmanabhan, Chee Chin Lim, Karthik Natarajan
Neuronal Gaussian Process Regression Johannes Friedrich
Nonconvex Sparse Graph Learning under Laplacian Constrained Graphical Model Jiaxi Ying, José Vinícius de Miranda Cardoso , Daniel Palomar
Synthetic Data Generators — Sequential and Private Olivier Bousquet, Roi Livni, Shay Moran
Uncertainty Quantification for Inferring Hawkes Networks Haoyun Wang, Liyan Xie, Alex Cuozzo, Simon Mak, Yao Xie
Implicit Distributional Reinforcement Learning Yuguang Yue, Zhendong Wang, Mingyuan Zhou
Auxiliary Task Reweighting for Minimum-data Learning Baifeng Shi, Judy Hoffman, Kate Saenko, Trevor Darrell, Huijuan Xu
Small Nash Equilibrium Certificates in Very Large Games Brian Zhang, Tuomas Sandholm
Training Linear Finite-State Machines Arash Ardakani, Amir Ardakani, Warren Gross
Efficient active learning of sparse halfspaces with arbitrary bounded noise Chicheng Zhang, Jie Shen, Pranjal Awasthi
Swapping Autoencoder for Deep Image Manipulation Taesung Park, Jun-Yan Zhu, Oliver Wang, Jingwan Lu, Eli Shechtman, Alexei Efros, Richard Zhang
Self-Supervised Few-Shot Learning on Point Clouds Charu Sharma, Manohar Kaul
Faster Differentially Private Samplers via Rényi Divergence Analysis of Discretized Langevin MCMC Arun Ganesh, Kunal Talwar
Learning identifiable and interpretable latent models of high-dimensional neural activity using pi-VAE Ding Zhou, Xue-Xin Wei
RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning Caglar Gulcehre, Ziyu Wang, Alexander Novikov, Thomas Paine, Sergio Gómez, Konrad Zolna, Rishabh Agarwal, Josh S. Merel, Daniel J. Mankowitz, Cosmin Paduraru, Gabriel Dulac-Arnold, Jerry Li, Mohammad Norouzi, Matthew Hoffman, Nicolas Heess, Nando de Freitas
Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Jiankang Deng, Gang Niu, Masashi Sugiyama
Interior Point Solving for LP-based prediction+optimisation Jayanta Mandi, Tias Guns
A simple normative network approximates local non-Hebbian learning in the cortex Siavash Golkar, David Lipshutz, Yanis Bahroun, Anirvan Sengupta, Dmitri Chklovskii
Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networks Roman Pogodin, Peter Latham
Understanding the Role of Training Regimes in Continual Learning Seyed Iman Mirzadeh, Mehrdad Farajtabar, Razvan Pascanu, Hassan Ghasemzadeh
Fair regression with Wasserstein barycenters Evgenii Chzhen, Christophe Denis, Mohamed Hebiri, Luca Oneto, Massimiliano Pontil
Training Stronger Baselines for Learning to Optimize Tianlong Chen, Weiyi Zhang, Zhou Jingyang, Shiyu Chang, Sijia Liu, Lisa Amini, Zhangyang Wang
Exactly Computing the Local Lipschitz Constant of ReLU Networks Matt Jordan, Alexandros G. Dimakis
Strictly Batch Imitation Learning by Energy-based Distribution Matching Daniel Jarrett, Ioana Bica, Mihaela van der Schaar
On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Sampling Method Ye He, Krishnakumar Balasubramanian, Murat A. Erdogdu
A Single-Loop Smoothed Gradient Descent-Ascent Algorithm for Nonconvex-Concave Min-Max Problems Jiawei Zhang, Peijun Xiao, Ruoyu Sun, Zhiquan Luo
Generating Correct Answers for Progressive Matrices Intelligence Tests Niv Pekar, Yaniv Benny, Lior Wolf
HyNet: Learning Local Descriptor with Hybrid Similarity Measure and Triplet Loss Yurun Tian, Axel Barroso Laguna, Tony Ng, Vassileios Balntas, Krystian Mikolajczyk
Preference learning along multiple criteria: A game-theoretic perspective Kush Bhatia, Ashwin Pananjady, Peter Bartlett, Anca Dragan, Martin J. Wainwright
Multi-Plane Program Induction with 3D Box Priors Yikai Li, Jiayuan Mao, Xiuming Zhang, Bill Freeman, Josh Tenenbaum, Noah Snavely, Jiajun Wu
Online Neural Connectivity Estimation with Noisy Group Testing Anne Draelos, John Pearson
Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free Haotao N. Wang, Tianlong Chen, Shupeng Gui, TingKuei Hu, Ji Liu, Zhangyang Wang
Implicit Neural Representations with Periodic Activation Functions Vincent Sitzmann, Julien Martel, Alexander Bergman, David Lindell, Gordon Wetzstein
Rotated Binary Neural Network Mingbao Lin, Rongrong Ji, Zihan Xu, Baochang Zhang, Yan Wang, Yongjian Wu, Feiyue Huang, Chia-Wen Lin
Community detection in sparse time-evolving graphs with a dynamical Bethe-Hessian Lorenzo Dall’Amico, Romain Couillet, Nicolas Tremblay
Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness Jeremiah Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax Weiss, Balaji Lakshminarayanan
Adaptive Learning of Rank-One Models for Efficient Pairwise Sequence Alignment Govinda Kamath, Tavor Baharav, Ilan Shomorony
Hierarchical nucleation in deep neural networks Diego Doimo, Aldo Glielmo, Alessio Ansuini, Alessandro Laio
Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains Matthew Tancik, Pratul Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan Barron, Ren Ng
Graph Geometry Interaction Learning Shichao Zhu, Shirui Pan, Chuan Zhou, Jia Wu, Yanan Cao, Bin Wang
Differentiable Augmentation for Data-Efficient GAN Training Shengyu Zhao, Zhijian Liu, Ji Lin, Jun-Yan Zhu, Song Han
Heuristic Domain Adaptation Shuhao Cui, Xuan Jin, Shuhui Wang, Yuan He, Qingming Huang
Learning Certified Individually Fair Representations Anian Ruoss, Mislav Balunovic, Marc Fischer, Martin Vechev
Part-dependent Label Noise: Towards Instance-dependent Label Noise Xiaobo Xia, Tongliang Liu, Bo Han, Nannan Wang, Mingming Gong, Haifeng Liu, Gang Niu, Dacheng Tao, Masashi Sugiyama
Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization Jianyu Wang, Qinghua Liu, Hao Liang, Gauri Joshi, H. Vincent Poor
An Improved Analysis of (Variance-Reduced) Policy Gradient and Natural Policy Gradient Methods Yanli Liu, Kaiqing Zhang, Tamer Basar, Wotao Yin
Geometric Exploration for Online Control Orestis Plevrakis, Elad Hazan
Automatic Curriculum Learning through Value Disagreement Yunzhi Zhang, Pieter Abbeel, Lerrel Pinto
MRI Banding Removal via Adversarial Training Aaron Defazio, Tullie Murrell, Michael Recht
The NetHack Learning Environment Heinrich Küttler, Nantas Nardelli, Alexander Miller, Roberta Raileanu, Marco Selvatici, Edward Grefenstette, Tim Rocktäschel
Language and Visual Entity Relationship Graph for Agent Navigation Yicong Hong, Cristian Rodriguez, Yuankai Qi, Qi Wu, Stephen Gould
ICAM: Interpretable Classification via Disentangled Representations and Feature Attribution Mapping Cher Bass, Mariana da Silva, Carole Sudre, Petru-Daniel Tudosiu, Stephen Smith, Emma Robinson
Spectra of the Conjugate Kernel and Neural Tangent Kernel for linear-width neural networks Zhou Fan, Zhichao Wang
No-Regret Learning Dynamics for Extensive-Form Correlated Equilibrium Andrea Celli, Alberto Marchesi, Gabriele Farina, Nicola Gatti
Estimating weighted areas under the ROC curve Andreas Maurer, Massimiliano Pontil
Can Implicit Bias Explain Generalization? Stochastic Convex Optimization as a Case Study Assaf Dauber, Meir Feder, Tomer Koren, Roi Livni
Generalized Hindsight for Reinforcement Learning Alexander Li, Lerrel Pinto, Pieter Abbeel
Critic Regularized Regression Ziyu Wang, Alexander Novikov, Konrad Zolna, Josh S. Merel, Jost Tobias Springenberg, Scott E. Reed, Bobak Shahriari, Noah Siegel, Caglar Gulcehre, Nicolas Heess, Nando de Freitas
Boosting Adversarial Training with Hypersphere Embedding Tianyu Pang, Xiao Yang, Yinpeng Dong, Taufik Xu, Jun Zhu, Hang Su
Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, Danai Koutra
Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows Ruizhi Deng, Bo Chang, Marcus A. Brubaker, Greg Mori, Andreas Lehrmann
Efficient Online Learning of Optimal Rankings: Dimensionality Reduction via Gradient Descent Dimitris Fotakis, Thanasis Lianeas, Georgios Piliouras, Stratis Skoulakis
Training Normalizing Flows with the Information Bottleneck for Competitive Generative Classification Lynton Ardizzone, Radek Mackowiak, Carsten Rother, Ullrich Köthe
Detecting Hands and Recognizing Physical Contact in the Wild Supreeth Narasimhaswamy, Trung Nguyen, Minh Hoai Nguyen
On the Theory of Transfer Learning: The Importance of Task Diversity Nilesh Tripuraneni, Michael Jordan, Chi Jin
Finite-Time Analysis of Round-Robin Kullback-Leibler Upper Confidence Bounds for Optimal Adaptive Allocation with Multiple Plays and Markovian Rewards Vrettos Moulos
Neural Star Domain as Primitive Representation Yuki Kawana, Yusuke Mukuta, Tatsuya Harada
Off-Policy Interval Estimation with Lipschitz Value Iteration Ziyang Tang, Yihao Feng, Na Zhang, Jian Peng, Qiang Liu
Inverse Rational Control with Partially Observable Continuous Nonlinear Dynamics Minhae Kwon, Saurabh Daptardar, Paul R. Schrater, Zachary Pitkow
Deep Statistical Solvers Balthazar Donon, Zhengying Liu, Wenzhuo LIU, Isabelle Guyon, Antoine Marot, Marc Schoenauer
Distributionally Robust Parametric Maximum Likelihood Estimation Viet Anh Nguyen, Xuhui Zhang, Jose Blanchet, Angelos Georghiou
Secretary and Online Matching Problems with Machine Learned Advice Antonios Antoniadis, Themis Gouleakis, Pieter Kleer, Pavel Kolev
Deep Transformation-Invariant Clustering Tom Monnier, Thibault Groueix, Mathieu Aubry
Overfitting Can Be Harmless for Basis Pursuit, But Only to a Degree Peizhong Ju, Xiaojun Lin, Jia Liu
Improving Generalization in Reinforcement Learning with Mixture Regularization KAIXIN WANG, Bingyi Kang, Jie Shao, Jiashi Feng
Pontryagin Differentiable Programming: An End-to-End Learning and Control Framework Wanxin Jin, Zhaoran Wang, Zhuoran Yang, Shaoshuai Mou
Learning from Aggregate Observations Yivan Zhang, Nontawat Charoenphakdee, Zhenguo Wu, Masashi Sugiyama
The Devil is in the Detail: A Framework for Macroscopic Prediction via Microscopic Models Yingxiang Yang, Negar Kiyavash, Le Song, Niao He
Subgraph Neural Networks Emily Alsentzer, Samuel Finlayson, Michelle Li, Marinka Zitnik
Demystifying Orthogonal Monte Carlo and Beyond Han Lin, Haoxian Chen, Krzysztof M. Choromanski, Tianyi Zhang, Clement Laroche
Optimal Robustness-Consistency Trade-offs for Learning-Augmented Online Algorithms Alexander Wei, Fred Zhang
A Scalable Approach for Privacy-Preserving Collaborative Machine Learning Jinhyun So, Basak Guler, Salman Avestimehr
Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Search Jaehyeon Kim, Sungwon Kim, Jungil Kong, Sungroh Yoon
Towards Learning Convolutions from Scratch Behnam Neyshabur
Cycle-Contrast for Self-Supervised Video Representation Learning Quan Kong, Wenpeng Wei, Ziwei Deng, Tomoaki Yoshinaga, Tomokazu Murakami
Posterior Re-calibration for Imbalanced Datasets Junjiao Tian, Yen-Cheng Liu, Nathaniel Glaser, Yen-Chang Hsu, Zsolt Kira
Novelty Search in Representational Space for Sample Efficient Exploration Ruo Yu Tao, Vincent Francois-Lavet, Joelle Pineau
Robust Reinforcement Learning via Adversarial training with Langevin Dynamics Parameswaran Kamalaruban, Yu-Ting Huang, Ya-Ping Hsieh, Paul Rolland, Cheng Shi, Volkan Cevher
Adversarial Blocking Bandits Nicholas Bishop, Hau Chan, Debmalya Mandal, Long Tran-Thanh
Online Algorithms for Multi-shop Ski Rental with Machine Learned Advice Shufan Wang, Jian Li, Shiqiang Wang
Multi-label Contrastive Predictive Coding Jiaming Song, Stefano Ermon
Rotation-Invariant Local-to-Global Representation Learning for 3D Point Cloud SEOHYUN KIM, JaeYoo Park, Bohyung Han
Learning Invariants through Soft Unification Nuri Cingillioglu, Alessandra Russo
One Solution is Not All You Need: Few-Shot Extrapolation via Structured MaxEnt RL Saurabh Kumar, Aviral Kumar, Sergey Levine, Chelsea Finn
Variational Bayesian Monte Carlo with Noisy Likelihoods Luigi Acerbi
Finite-Sample Analysis of Contractive Stochastic Approximation Using Smooth Convex Envelopes Zaiwei Chen, Siva Theja Maguluri, Sanjay Shakkottai, Karthikeyan Shanmugam
Self-Supervised Generative Adversarial Compression Chong Yu, Jeff Pool
An efficient nonconvex reformulation of stagewise convex optimization problems Rudy R. Bunel, Oliver Hinder, Srinadh Bhojanapalli, Krishnamurthy Dvijotham
From Finite to Countable-Armed Bandits Anand Kalvit, Assaf Zeevi
Adversarial Distributional Training for Robust Deep Learning Yinpeng Dong, Zhijie Deng, Tianyu Pang, Jun Zhu, Hang Su
Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes Andrew Foong, Wessel Bruinsma, Jonathan Gordon, Yann Dubois, James Requeima, Richard Turner
Theory-Inspired Path-Regularized Differential Network Architecture Search Pan Zhou, Caiming Xiong, Richard Socher, Steven Chu Hong Hoi
Conic Descent and its Application to Memory-efficient Optimization over Positive Semidefinite Matrices John C. Duchi, Oliver Hinder, Andrew Naber, Yinyu Ye
Learning the Geometry of Wave-Based Imaging Konik Kothari, Maarten de Hoop, Ivan Dokmanić
Greedy inference with structure-exploiting lazy maps Michael Brennan, Daniele Bigoni, Olivier Zahm, Alessio Spantini, Youssef Marzouk
Nimble: Lightweight and Parallel GPU Task Scheduling for Deep Learning Woosuk Kwon, Gyeong-In Yu, Eunji Jeong, Byung-Gon Chun
Finding the Homology of Decision Boundaries with Active Learning Weizhi Li, Gautam Dasarathy, Karthikeyan Natesan Ramamurthy, Visar Berisha
Reinforced Molecular Optimization with Neighborhood-Controlled Grammars Chencheng Xu, Qiao Liu, Minlie Huang, Tao Jiang
Natural Policy Gradient Primal-Dual Method for Constrained Markov Decision Processes Dongsheng Ding, Kaiqing Zhang, Tamer Basar, Mihailo Jovanovic
Classification Under Misspecification: Halfspaces, Generalized Linear Models, and Evolvability Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau
Certified Defense to Image Transformations via Randomized Smoothing Marc Fischer, Maximilian Baader, Martin Vechev
Estimation of Skill Distribution from a Tournament Ali Jadbabaie, Anuran Makur, Devavrat Shah
Reparameterizing Mirror Descent as Gradient Descent Ehsan Amid, Manfred K. K. Warmuth
General Control Functions for Causal Effect Estimation from IVs Aahlad Puli, Rajesh Ranganath
Optimal Algorithms for Stochastic Multi-Armed Bandits with Heavy Tailed Rewards Kyungjae Lee, Hongjun Yang, Sungbin Lim, Songhwai Oh
Certified Robustness of Graph Convolution Networks for Graph Classification under Topological Attacks Hongwei Jin, Zhan Shi, Venkata Jaya Shankar Ashish Peruri, Xinhua Zhang
Zero-Resource Knowledge-Grounded Dialogue Generation Linxiao Li, Can Xu, Wei Wu, YUFAN ZHAO, Xueliang Zhao, Chongyang Tao
Targeted Adversarial Perturbations for Monocular Depth Prediction Alex Wong, Safa Cicek, Stefano Soatto
Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties Jakob Lindinger, David Reeb, Christoph Lippert, Barbara Rakitsch
Offline Imitation Learning with a Misspecified Simulator Shengyi Jiang, Jingcheng Pang, Yang Yu
Multi-Fidelity Bayesian Optimization via Deep Neural Networks Shibo Li, Wei Xing, Robert Kirby, Shandian Zhe
PlanGAN: Model-based Planning With Sparse Rewards and Multiple Goals Henry Charlesworth, Giovanni Montana
Bad Global Minima Exist and SGD Can Reach Them Shengchao Liu, Dimitris Papailiopoulos, Dimitris Achlioptas
Optimal Prediction of the Number of Unseen Species with Multiplicity Yi Hao, Ping Li
Characterizing Optimal Mixed Policies: Where to Intervene and What to Observe Sanghack Lee, Elias Bareinboim
Factor Graph Neural Networks Zhen Zhang, Fan Wu, Wee Sun Lee
A Closer Look at Accuracy vs. Robustness Yao-Yuan Yang, Cyrus Rashtchian, Hongyang Zhang, Russ R. Salakhutdinov, Kamalika Chaudhuri
Curriculum Learning by Dynamic Instance Hardness Tianyi Zhou, Shengjie Wang, Jeff A. Bilmes
Spin-Weighted Spherical CNNs Carlos Esteves, Ameesh Makadia, Kostas Daniilidis
Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks David Bieber, Charles Sutton, Hugo Larochelle, Daniel Tarlow
AutoPrivacy: Automated Layer-wise Parameter Selection for Secure Neural Network Inference Qian Lou, Song Bian, Lei Jiang
Baxter Permutation Process Masahiro Nakano, Akisato Kimura, Takeshi Yamada, Naonori Ueda
Characterizing emergent representations in a space of candidate learning rules for deep networks Yinan Cao, Christopher Summerfield, Andrew Saxe
Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation Rasool Fakoor, Jonas W. Mueller, Nick Erickson, Pratik Chaudhari, Alexander J. Smola
Adaptive Probing Policies for Shortest Path Routing Aditya Bhaskara, Sreenivas Gollapudi, Kostas Kollias, Kamesh Munagala
Approximate Heavily-Constrained Learning with Lagrange Multiplier Models Harikrishna Narasimhan, Andrew Cotter, Yichen Zhou, Serena Wang, Wenshuo Guo
Faster Randomized Infeasible Interior Point Methods for Tall/Wide Linear Programs Agniva Chowdhury, Palma London, Haim Avron, Petros Drineas
Sliding Window Algorithms for k-Clustering Problems Michele Borassi, Alessandro Epasto, Silvio Lattanzi, Sergei Vassilvitskii, Morteza Zadimoghaddam
AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning Ximeng Sun, Rameswar Panda, Rogerio Feris, Kate Saenko
Approximate Cross-Validation for Structured Models Soumya Ghosh, William T. Stephenson, Tin D. Nguyen, Sameer Deshpande, Tamara Broderick
Exemplar VAE: Linking Generative Models, Nearest Neighbor Retrieval, and Data Augmentation Sajad Norouzi, David J. Fleet, Mohammad Norouzi
Debiased Contrastive Learning Ching-Yao Chuang, Joshua Robinson, Yen-Chen Lin, Antonio Torralba, Stefanie Jegelka
UCSG-NET- Unsupervised Discovering of Constructive Solid Geometry Tree Kacper Kania, Maciej Zieba, Tomasz Kajdanowicz
Generalized Boosting Arun Suggala, Bingbin Liu, Pradeep Ravikumar
COT-GAN: Generating Sequential Data via Causal Optimal Transport Tianlin Xu, Li Kevin Wenliang, Michael Munn, Beatrice Acciaio
Impossibility Results for Grammar-Compressed Linear Algebra Amir Abboud, Arturs Backurs, Karl Bringmann, Marvin Künnemann
Understanding spiking networks through convex optimization Allan Mancoo, Sander Keemink, Christian K. Machens
Better Full-Matrix Regret via Parameter-Free Online Learning Ashok Cutkosky
Large-Scale Methods for Distributionally Robust Optimization Daniel Levy, Yair Carmon, John C. Duchi, Aaron Sidford
Analysis and Design of Thompson Sampling for Stochastic Partial Monitoring Taira Tsuchiya, Junya Honda, Masashi Sugiyama
Bandit Linear Control Asaf Cassel, Tomer Koren
Refactoring Policy for Compositional Generalizability using Self-Supervised Object Proposals Tongzhou Mu, Jiayuan Gu, Zhiwei Jia, Hao Tang, Hao Su
PEP: Parameter Ensembling by Perturbation Alireza Mehrtash, Purang Abolmaesumi, Polina Golland, Tina Kapur, Demian Wassermann, William Wells
Theoretical Insights Into Multiclass Classification: A High-dimensional Asymptotic View Christos Thrampoulidis, Samet Oymak, Mahdi Soltanolkotabi
Adversarial Example Games Joey Bose, Gauthier Gidel, Hugo Berard, Andre Cianflone, Pascal Vincent, Simon Lacoste-Julien, Will Hamilton
Residual Distillation: Towards Portable Deep Neural Networks without Shortcuts Guilin Li, Junlei Zhang, Yunhe Wang, Chuanjian Liu, Matthias Tan, Yunfeng Lin, Wei Zhang, Jiashi Feng, Tong Zhang
Provably Efficient Neural Estimation of Structural Equation Models: An Adversarial Approach Luofeng Liao, You-Lin Chen, Zhuoran Yang, Bo Dai, Mladen Kolar, Zhaoran Wang
Security Analysis of Safe and Seldonian Reinforcement Learning Algorithms Pinar Ozisik, Philip S. Thomas
Learning to Play Sequential Games versus Unknown Opponents Pier Giuseppe Sessa, Ilija Bogunovic, Maryam Kamgarpour, Andreas Krause
Further Analysis of Outlier Detection with Deep Generative Models Ziyu Wang, Bin Dai, David Wipf, Jun Zhu
Bridging Imagination and Reality for Model-Based Deep Reinforcement Learning Guangxiang Zhu, Minghao Zhang, Honglak Lee, Chongjie Zhang
Neural Networks Learning and Memorization with (almost) no Over-Parameterization Amit Daniely
Exploiting Higher Order Smoothness in Derivative-free Optimization and Continuous Bandits Arya Akhavan, Massimiliano Pontil, Alexandre Tsybakov
Towards a Combinatorial Characterization of Bounded-Memory Learning Alon Gonen, Shachar Lovett, Michal Moshkovitz
Chaos, Extremism and Optimism: Volume Analysis of Learning in Games Yun Kuen Cheung, Georgios Piliouras
On Regret with Multiple Best Arms Yinglun Zhu, Robert Nowak
Matrix Completion with Hierarchical Graph Side Information Adel Elmahdy, Junhyung Ahn, Changho Suh, Soheil Mohajer
Is Long Horizon RL More Difficult Than Short Horizon RL? Ruosong Wang, Simon S. Du, Lin Yang, Sham Kakade
Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation: Bayesian inference for latent Gaussian models and beyond Charles Margossian, Aki Vehtari, Daniel Simpson, Raj Agrawal
Adversarial Learning for Robust Deep Clustering Xu Yang, Cheng Deng, Kun Wei, Junchi Yan, Wei Liu
Learning Mutational Semantics Brian Hie, Ellen Zhong, Bryan Bryson, Bonnie Berger
Learning to Learn Variational Semantic Memory Xiantong Zhen, Yingjun Du, Huan Xiong, Qiang Qiu, Cees Snoek, Ling Shao
Myersonian Regression Allen Liu, Renato Leme, Jon Schneider
Learnability with Indirect Supervision Signals Kaifu Wang, Qiang Ning, Dan Roth
Towards Safe Policy Improvement for Non-Stationary MDPs Yash Chandak, Scott Jordan, Georgios Theocharous, Martha White, Philip S. Thomas
Finer Metagenomic Reconstruction via Biodiversity Optimization Simon Foucart, David Koslicki
Causal Discovery in Physical Systems from Videos Yunzhu Li, Antonio Torralba, Anima Anandkumar, Dieter Fox, Animesh Garg
Glyph: Fast and Accurately Training Deep Neural Networks on Encrypted Data Qian Lou, Bo Feng, Geoffrey Charles Fox, Lei Jiang
Smoothed Analysis of Online and Differentially Private Learning Nika Haghtalab, Tim Roughgarden, Abhishek Shetty
Self-Paced Deep Reinforcement Learning Pascal Klink, Carlo D’Eramo, Jan R. Peters, Joni Pajarinen
Kalman Filtering Attention for User Behavior Modeling in CTR Prediction Hu Liu, Jing LU, Xiwei Zhao, Sulong Xu, Hao Peng, Yutong Liu, Zehua Zhang, Jian Li, Junsheng Jin, Yongjun Bao, Weipeng Yan
Towards Maximizing the Representation Gap between In-Domain & Out-of-Distribution Examples Jay Nandy, Wynne Hsu, Mong Li Lee
Fully Convolutional Mesh Autoencoder using Efficient Spatially Varying Kernels Yi Zhou, Chenglei Wu, Zimo Li, Chen Cao, Yuting Ye, Jason Saragih, Hao Li, Yaser Sheikh
GNNGuard: Defending Graph Neural Networks against Adversarial Attacks Xiang Zhang, Marinka Zitnik
Geo-PIFu: Geometry and Pixel Aligned Implicit Functions for Single-view Human Reconstruction Tong He, John Collomosse, Hailin Jin, Stefano Soatto
Optimal visual search based on a model of target detectability in natural images Shima Rashidi, Krista Ehinger, Andrew Turpin, Lars Kulik
Towards Convergence Rate Analysis of Random Forests for Classification Wei Gao, Zhi-Hua Zhou
List-Decodable Mean Estimation via Iterative Multi-Filtering Ilias Diakonikolas, Daniel Kane, Daniel Kongsgaard
Exact Recovery of Mangled Clusters with Same-Cluster Queries Marco Bressan, Nicolò Cesa-Bianchi, Silvio Lattanzi, Andrea Paudice
Steady State Analysis of Episodic Reinforcement Learning Huang Bojun
Direct Feedback Alignment Scales to Modern Deep Learning Tasks and Architectures Julien Launay, Iacopo Poli, François Boniface, Florent Krzakala
Bayesian Optimization for Iterative Learning Vu Nguyen, Sebastian Schulze, Michael Osborne
Minimax Bounds for Generalized Linear Models Kuan-Yun Lee, Thomas Courtade
Projection Robust Wasserstein Distance and Riemannian Optimization Tianyi Lin, Chenyou Fan, Nhat Ho, Marco Cuturi, Michael Jordan
CoinDICE: Off-Policy Confidence Interval Estimation Bo Dai, Ofir Nachum, Yinlam Chow, Lihong Li, Csaba Szepesvari, Dale Schuurmans
Simple and Fast Algorithm for Binary Integer and Online Linear Programming Xiaocheng Li, Chunlin Sun, Yinyu Ye
Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reduction Yaodong Yu, Kwan Ho Ryan Chan, Chong You, Chaobing Song, Yi Ma
Learning Rich Rankings Arjun Seshadri, Stephen Ragain, Johan Ugander
Color Visual Illusions: A Statistics-based Computational Model Elad Hirsch, Ayellet Tal
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela
Universal guarantees for decision tree induction via a higher-order splitting criterion Guy Blanc, Neha Gupta, Jane Lange, Li-Yang Tan
Trade-offs and Guarantees of Adversarial Representation Learning for Information Obfuscation Han Zhao, Jianfeng Chi, Yuan Tian, Geoffrey J. Gordon
A Boolean Task Algebra for Reinforcement Learning Geraud Nangue Tasse, Steven James, Benjamin Rosman
Learning with Differentiable Pertubed Optimizers Quentin Berthet, Mathieu Blondel, Olivier Teboul, Marco Cuturi, Jean-Philippe Vert, Francis Bach
Optimal Learning from Verified Training Data Nicholas Bishop, Long Tran-Thanh, Enrico Gerding
Online Linear Optimization with Many Hints Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit
Dynamical mean-field theory for stochastic gradient descent in Gaussian mixture classification Francesca Mignacco, Florent Krzakala, Pierfrancesco Urbani, Lenka Zdeborová
Causal Discovery from Soft Interventions with Unknown Targets: Characterization and Learning Amin Jaber, Murat Kocaoglu, Karthikeyan Shanmugam, Elias Bareinboim
Exploiting the Surrogate Gap in Online Multiclass Classification Dirk van der Hoeven
The Pitfalls of Simplicity Bias in Neural Networks Harshay Shah, Kaustav Tamuly, Aditi Raghunathan, Prateek Jain, Praneeth Netrapalli
Automatically Learning Compact Quality-aware Surrogates for Optimization Problems Kai Wang, Bryan Wilder, Andrew Perrault, Milind Tambe
Empirical Likelihood for Contextual Bandits Nikos Karampatziakis, John Langford, Paul Mineiro
Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver? Vitaly Kurin, Saad Godil, Shimon Whiteson, Bryan Catanzaro
Non-reversible Gaussian processes for identifying latent dynamical structure in neural data Virginia Rutten, Alberto Bernacchia, Maneesh Sahani, Guillaume Hennequin
Listening to Sounds of Silence for Speech Denoising Ruilin Xu, Rundi Wu, Yuko Ishiwaka, Carl Vondrick, Changxi Zheng
BoxE: A Box Embedding Model for Knowledge Base Completion Ralph Abboud, Ismail Ceylan, Thomas Lukasiewicz, Tommaso Salvatori
Coherent Hierarchical Multi-Label Classification Networks Eleonora Giunchiglia, Thomas Lukasiewicz
Walsh-Hadamard Variational Inference for Bayesian Deep Learning Simone Rossi, Sebastien Marmin, Maurizio Filippone
Federated Bayesian Optimization via Thompson Sampling Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet
MultiON: Benchmarking Semantic Map Memory using Multi-Object Navigation Saim Wani, Shivansh Patel, Unnat Jain, Angel Chang, Manolis Savva
Neural Complexity Measures Yoonho Lee, Juho Lee, Sung Ju Hwang, Eunho Yang, Seungjin Choi
Optimal Iterative Sketching Methods with the Subsampled Randomized Hadamard Transform Jonathan Lacotte, Sifan Liu, Edgar Dobriban, Mert Pilanci
Provably adaptive reinforcement learning in metric spaces Tongyi Cao, Akshay Krishnamurthy
ShapeFlow: Learnable Deformation Flows Among 3D Shapes Chiyu Jiang, Jingwei Huang, Andrea Tagliasacchi, Leonidas J. Guibas
Self-Supervised Learning by Cross-Modal Audio-Video Clustering Humam Alwassel, Dhruv Mahajan, Bruno Korbar, Lorenzo Torresani, Bernard Ghanem, Du Tran
Optimal Query Complexity of Secure Stochastic Convex Optimization Wei Tang, Chien-Ju Ho, Yang Liu
DynaBERT: Dynamic BERT with Adaptive Width and Depth Lu Hou, Zhiqi Huang, Lifeng Shang, Xin Jiang, Xiao Chen, Qun Liu
Generalization Bound of Gradient Descent for Non-Convex Metric Learning MINGZHI DONG, Xiaochen Yang, Rui Zhu, Yujiang Wang, Jing-Hao Xue
Dynamic Submodular Maximization Morteza Monemizadeh
Inference for Batched Bandits Kelly Zhang, Lucas Janson, Susan Murphy
Approximate Cross-Validation with Low-Rank Data in High Dimensions William T. Stephenson, Madeleine Udell, Tamara Broderick
GANSpace: Discovering Interpretable GAN Controls Erik Härkönen, Aaron Hertzmann, Jaakko Lehtinen, Sylvain Paris
Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization Samuel Daulton, Maximilian Balandat, Eytan Bakshy
Neuron-level Structured Pruning using Polarization Regularizer Tao Zhuang, Zhixuan Zhang, Yuheng Huang, Xiaoyi Zeng, Kai Shuang, Xiang Li
Limits on Testing Structural Changes in Ising Models Aditya Gangrade, Bobak Nazer, Venkatesh Saligrama
Field-wise Learning for Multi-field Categorical Data Zhibin Li, Jian Zhang, Yongshun Gong, Yazhou Yao, Qiang Wu
Continual Learning in Low-rank Orthogonal Subspaces Arslan Chaudhry, Naeemullah Khan, Puneet Dokania, Philip Torr
Unsupervised Learning of Visual Features by Contrasting Cluster Assignments Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, Armand Joulin
Sharpened Generalization Bounds based on Conditional Mutual Information and an Application to Noisy, Iterative Algorithms Mahdi Haghifam, Jeffrey Negrea, Ashish Khisti, Daniel M. Roy, Gintare Karolina Dziugaite
Learning Deformable Tetrahedral Meshes for 3D Reconstruction Jun Gao, Wenzheng Chen, Tommy Xiang, Alec Jacobson, Morgan McGuire, Sanja Fidler
Information theoretic limits of learning a sparse rule Clément Luneau, jean barbier, Nicolas Macris
Self-supervised learning through the eyes of a child Emin Orhan, Vaibhav Gupta, Brenden M. Lake
Unsupervised Semantic Aggregation and Deformable Template Matching for Semi-Supervised Learning Tao Han, Junyu Gao, Yuan Yuan, Qi Wang
A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learning Arnu Pretorius, Scott Cameron, Elan van Biljon, Thomas Makkink, Shahil Mawjee, Jeremy du Plessis, Jonathan Shock, Alexandre Laterre, Karim Beguir
What shapes feature representations? Exploring datasets, architectures, and training Katherine Hermann, Andrew Lampinen
Optimal Best-arm Identification in Linear Bandits Yassir Jedra, Alexandre Proutiere
Data Diversification: A Simple Strategy For Neural Machine Translation Xuan-Phi Nguyen, Shafiq Joty, Kui Wu, Ai Ti Aw
Interstellar: Searching Recurrent Architecture for Knowledge Graph Embedding Yongqi Zhang, Quanming Yao, Lei Chen
CoSE: Compositional Stroke Embeddings Emre Aksan, Thomas Deselaers, Andrea Tagliasacchi, Otmar Hilliges
Learning Multi-Agent Coordination for Enhancing Target Coverage in Directional Sensor Networks Jing Xu, Fangwei Zhong, Yizhou Wang
Biological credit assignment through dynamic inversion of feedforward networks William Podlaski, Christian K. Machens
Discriminative Sounding Objects Localization via Self-supervised Audiovisual Matching Di Hu, Rui Qian, Minyue Jiang, Xiao Tan, Shilei Wen, Errui Ding, Weiyao Lin, Dejing Dou
Learning Multi-Agent Communication through Structured Attentive Reasoning Murtaza Rangwala, Ryan Williams
Private Identity Testing for High-Dimensional Distributions Clément L. Canonne, Gautam Kamath, Audra McMillan, Jonathan Ullman, Lydia Zakynthinou
On the Optimal Weighted $ℓ_{2}$ Regularization in Overparameterized Linear Regression Denny Wu, Ji Xu
An Efficient Asynchronous Method for Integrating Evolutionary and Gradient-based Policy Search Kyunghyun Lee, Byeong-Uk Lee, Ukcheol Shin, In So Kweon
MetaSDF: Meta-Learning Signed Distance Functions Vincent Sitzmann, Eric Chan, Richard Tucker, Noah Snavely, Gordon Wetzstein
Simple and Scalable Sparse k-means Clustering via Feature Ranking Zhiyue Zhang, Kenneth Lange, Jason Xu
Model-based Adversarial Meta-Reinforcement Learning Zichuan Lin, Garrett Thomas, Guangwen Yang, Tengyu Ma
Graph Policy Network for Transferable Active Learning on Graphs Shengding Hu, Zheng Xiong, Meng Qu, Xingdi Yuan, Marc-Alexandre Côté, Zhiyuan Liu, Jian Tang
Towards a Better Global Loss Landscape of GANs Ruoyu Sun, Tiantian Fang, Alexander Schwing
Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning Tabish Rashid, Gregory Farquhar, Bei Peng, Shimon Whiteson
BanditPAM: Almost Linear Time k-Medoids Clustering via Multi-Armed Bandits Mo Tiwari, Martin J. Zhang, James Mayclin, Sebastian Thrun, Chris Piech, Ilan Shomorony
UDH: Universal Deep Hiding for Steganography, Watermarking, and Light Field Messaging Chaoning Zhang, Philipp Benz, Adil Karjauv, Geng Sun, In Kweon
Evidential Sparsification of Multimodal Latent Spaces in Conditional Variational Autoencoders Masha Itkina, Boris Ivanovic, Ransalu Senanayake, Mykel J. Kochenderfer, Marco Pavone
An Unbiased Risk Estimator for Learning with Augmented Classes Yu-Jie Zhang, Peng Zhao, Lanjihong Ma, Zhi-Hua Zhou
AutoBSS: An Efficient Algorithm for Block Stacking Style Search yikang zhang, Jian Zhang, Zhao Zhong
Pushing the Limits of Narrow Precision Inferencing at Cloud Scale with Microsoft Floating Point Bita Darvish Rouhani, Daniel Lo, Ritchie Zhao, Ming Liu, Jeremy Fowers, Kalin Ovtcharov , Anna Vinogradsky, Sarah Massengill , Lita Yang, Ray Bittner, Alessandro Forin, Haishan Zhu, Taesik Na, Prerak Patel, Shuai Che, Lok Chand Koppaka , XIA SONG, Subhojit Som, Kaustav Das, Saurabh T, Steve Reinhardt , Sitaram Lanka, Eric Chung, Doug Burger
Stochastic Optimization with Laggard Data Pipelines Naman Agarwal, Rohan Anil, Tomer Koren, Kunal Talwar, Cyril Zhang
Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs Dasol Hwang, Jinyoung Park, Sunyoung Kwon, KyungMin Kim, Jung-Woo Ha, Hyunwoo J. Kim
GPS-Net: Graph-based Photometric Stereo Network Zhuokun Yao, Kun Li, Ying Fu, Haofeng Hu, Boxin Shi
Consistent Structural Relation Learning for Zero-Shot Segmentation Peike Li, Yunchao Wei, Yi Yang
Model Selection in Contextual Stochastic Bandit Problems Aldo Pacchiano, My Phan, Yasin Abbasi Yadkori, Anup Rao, Julian Zimmert, Tor Lattimore, Csaba Szepesvari
Truncated Linear Regression in High Dimensions Constantinos Daskalakis, Dhruv Rohatgi, Emmanouil Zampetakis
Incorporating Pragmatic Reasoning Communication into Emergent Language Yipeng Kang, Tonghan Wang, Gerard de Melo
Deep Subspace Clustering with Data Augmentation Mahdi Abavisani, Alireza Naghizadeh, Dimitris Metaxas, Vishal Patel
An Empirical Process Approach to the Union Bound: Practical Algorithms for Combinatorial and Linear Bandits Julian Katz-Samuels, Lalit Jain, zohar karnin, Kevin G. Jamieson
Can Graph Neural Networks Count Substructures? Zhengdao Chen, Lei Chen, Soledad Villar, Joan Bruna
A Bayesian Perspective on Training Speed and Model Selection Clare Lyle, Lisa Schut, Robin Ru, Yarin Gal, Mark van der Wilk
On the Modularity of Hypernetworks Tomer Galanti, Lior Wolf
Doubly Robust Off-Policy Value and Gradient Estimation for Deterministic Policies Nathan Kallus, Masatoshi Uehara
Provably Efficient Neural GTD for Off-Policy Learning Hoi-To Wai, Zhuoran Yang, Zhaoran Wang, Mingyi Hong
Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration Hanjun Dai, Rishabh Singh, Bo Dai, Charles Sutton, Dale Schuurmans
Stable and expressive recurrent vision models Drew Linsley, Alekh Karkada Ashok, Lakshmi Narasimhan Govindarajan, Rex Liu, Thomas Serre
Entropic Optimal Transport between Unbalanced Gaussian Measures has a Closed Form Hicham Janati, Boris Muzellec, Gabriel Peyré, Marco Cuturi
BRP-NAS: Prediction-based NAS using GCNs Lukasz Dudziak, Thomas Chau, Mohamed Abdelfattah, Royson Lee, Hyeji Kim, Nicholas Lane
Deep Shells: Unsupervised Shape Correspondence with Optimal Transport Marvin Eisenberger, Aysim Toker, Laura Leal-Taixé, Daniel Cremers
ISTA-NAS: Efficient and Consistent Neural Architecture Search by Sparse Coding Yibo Yang, Hongyang Li, Shan You, Fei Wang, Chen Qian, Zhouchen Lin
Rel3D: A Minimally Contrastive Benchmark for Grounding Spatial Relations in 3D Ankit Goyal, Kaiyu Yang, Dawei Yang, Jia Deng
Regularizing Black-box Models for Improved Interpretability Gregory Plumb, Maruan Al-Shedivat, Ángel Alexander Cabrera, Adam Perer, Eric Xing, Ameet Talwalkar
Trust the Model When It Is Confident: Masked Model-based Actor-Critic Feiyang Pan, Jia He, Dandan Tu, Qing He
Semi-Supervised Neural Architecture Search Renqian Luo, Xu Tan, Rui Wang, Tao Qin, Enhong Chen, Tie-Yan Liu
Consistency Regularization for Certified Robustness of Smoothed Classifiers Jongheon Jeong, Jinwoo Shin
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A Statistical Mechanics Framework for Task-Agnostic Sample Design in Machine Learning Bhavya Kailkhura, Jayaraman J. Thiagarajan, Qunwei Li, Jize Zhang, Yi Zhou, Timo Bremer
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A Bandit Learning Algorithm and Applications to Auction Design Kim Thang Nguyen
MetaPoison: Practical General-purpose Clean-label Data Poisoning W. Ronny Huang, Jonas Geiping, Liam Fowl, Gavin Taylor, Tom Goldstein
Sample Efficient Reinforcement Learning via Low-Rank Matrix Estimation Devavrat Shah, Dogyoon Song, Zhi Xu, Yuzhe Yang
Training Generative Adversarial Networks with Limited Data Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen, Timo Aila
Deeply Learned Spectral Total Variation Decomposition Tamara G. Grossmann, Yury Korolev, Guy Gilboa, Carola Schoenlieb
FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training Yonggan Fu, Haoran You, Yang Zhao, Yue Wang, Chaojian Li, Kailash Gopalakrishnan, Zhangyang Wang, Yingyan Lin
Improving Neural Network Training in Low Dimensional Random Bases Frithjof Gressmann, Zach Eaton-Rosen, Carlo Luschi
Safe Reinforcement Learning via Curriculum Induction Matteo Turchetta, Andrey Kolobov, Shital Shah, Andreas Krause, Alekh Agarwal
Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning Nino Vieillard, Tadashi Kozuno, Bruno Scherrer, Olivier Pietquin, Remi Munos, Matthieu Geist
How Robust are the Estimated Effects of Nonpharmaceutical Interventions against COVID-19? Mrinank Sharma, Sören Mindermann, Jan Brauner, Gavin Leech, Anna Stephenson, Tomáš Gavenčiak, Jan Kulveit, Yee Whye Teh, Leonid Chindelevitch, Yarin Gal
Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable Recourses Kaivalya Rawal, Himabindu Lakkaraju
Generalization error in high-dimensional perceptrons: Approaching Bayes error with convex optimization Benjamin Aubin, Florent Krzakala, Yue Lu, Lenka Zdeborová
Projection Efficient Subgradient Method and Optimal Nonsmooth Frank-Wolfe Method Kiran K. Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh
PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks Minh Vu, My T. Thai
Few-Cost Salient Object Detection with Adversarial-Paced Learning Dingwen Zhang, HaiBin Tian, Jungong Han
Minimax Estimation of Conditional Moment Models Nishanth Dikkala, Greg Lewis, Lester Mackey, Vasilis Syrgkanis
Causal Imitation Learning With Unobserved Confounders Junzhe Zhang, Daniel Kumor, Elias Bareinboim
Your GAN is Secretly an Energy-based Model and You Should Use Discriminator Driven Latent Sampling Tong Che, Ruixiang ZHANG, Jascha Sohl-Dickstein, Hugo Larochelle, Liam Paull, Yuan Cao, Yoshua Bengio
Learning Black-Box Attackers with Transferable Priors and Query Feedback Jiancheng YANG, Yangzhou Jiang, Xiaoyang Huang, Bingbing Ni, Chenglong Zhao
Locally Differentially Private (Contextual) Bandits Learning Kai Zheng, Tianle Cai, Weiran Huang, Zhenguo Li, Liwei Wang
Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax Andres Potapczynski, Gabriel Loaiza-Ganem, John P. Cunningham
Kernel Based Progressive Distillation for Adder Neural Networks Yixing Xu, Chang Xu, Xinghao Chen, Wei Zhang, Chunjing XU, Yunhe Wang
Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization Paul Barde, Julien Roy, Wonseok Jeon, Joelle Pineau, Chris Pal, Derek Nowrouzezahrai
Agree to Disagree: Adaptive Ensemble Knowledge Distillation in Gradient Space Shangchen Du, Shan You, Xiaojie Li, Jianlong Wu, Fei Wang, Chen Qian, Changshui Zhang
The Wasserstein Proximal Gradient Algorithm Adil SALIM, Anna Korba, Giulia Luise
Universally Quantized Neural Compression Eirikur Agustsson, Lucas Theis
Temporal Variability in Implicit Online Learning Nicolò Campolongo, Francesco Orabona
Investigating Gender Bias in Language Models Using Causal Mediation Analysis Jesse Vig, Sebastian Gehrmann, Yonatan Belinkov, Sharon Qian, Daniel Nevo, Yaron Singer, Stuart Shieber
Off-Policy Imitation Learning from Observations Zhuangdi Zhu, Kaixiang Lin, Bo Dai, Jiayu Zhou
Escaping Saddle-Point Faster under Interpolation-like Conditions Abhishek Roy, Krishnakumar Balasubramanian, Saeed Ghadimi, Prasant Mohapatra
Matérn Gaussian Processes on Riemannian Manifolds Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Deisenroth
Improved Techniques for Training Score-Based Generative Models Yang Song, Stefano Ermon
wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations Alexei Baevski, Yuhao Zhou, Abdelrahman Mohamed, Michael Auli
A Maximum-Entropy Approach to Off-Policy Evaluation in Average-Reward MDPs Nevena Lazic, Dong Yin, Mehrdad Farajtabar, Nir Levine, Dilan Gorur, Chris Harris, Dale Schuurmans
Instead of Rewriting Foreign Code for Machine Learning, Automatically Synthesize Fast Gradients William Moses, Valentin Churavy
Does Unsupervised Architecture Representation Learning Help Neural Architecture Search? Shen Yan, Yu Zheng, Wei Ao, Xiao Zeng, Mi Zhang
Value-driven Hindsight Modelling Arthur Guez, Fabio Viola, Theophane Weber, Lars Buesing, Steven Kapturowski, Doina Precup, David Silver, Nicolas Heess
Dynamic Regret of Convex and Smooth Functions Peng Zhao, Yu-Jie Zhang, Lijun Zhang, Zhi-Hua Zhou
On Convergence of Nearest Neighbor Classifiers over Feature Transformations Luka Rimanic, Cedric Renggli, Bo Li, Ce Zhang
Mitigating Manipulation in Peer Review via Randomized Reviewer Assignments Steven Jecmen, Hanrui Zhang, Ryan Liu, Nihar Shah, Vincent Conitzer, Fei Fang
Contrastive learning of global and local features for medical image segmentation with limited annotations Krishna Chaitanya, Ertunc Erdil, Neerav Karani, Ender Konukoglu
Self-Supervised Graph Transformer on Large-Scale Molecular Data Yu Rong, Yatao Bian, Tingyang Xu, Weiyang Xie, Ying WEI, Wenbing Huang, Junzhou Huang
Generative Neurosymbolic Machines Jindong Jiang, Sungjin Ahn
How many samples is a good initial point worth in Low-rank Matrix Recovery? Jialun Zhang, Richard Zhang
CSER: Communication-efficient SGD with Error Reset Cong Xie, Shuai Zheng, Oluwasanmi O. Koyejo, Indranil Gupta, Mu Li, Haibin Lin
Efficient estimation of neural tuning during naturalistic behavior Edoardo Balzani, Kaushik Lakshminarasimhan, Dora Angelaki, Cristina Savin
High-recall causal discovery for autocorrelated time series with latent confounders Andreas Gerhardus, Jakob Runge
Forget About the LiDAR: Self-Supervised Depth Estimators with MED Probability Volumes Juan Luis GonzalezBello, Munchurl Kim
Joint Contrastive Learning with Infinite Possibilities Qi Cai, Yu Wang, Yingwei Pan, Ting Yao, Tao Mei
Robust Gaussian Covariance Estimation in Nearly-Matrix Multiplication Time Jerry Li, Guanghao Ye
Adversarially-learned Inference via an Ensemble of Discrete Undirected Graphical Models Adarsh K Jeewajee, Leslie Kaelbling
GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators Dingfan Chen, Tribhuvanesh Orekondy, Mario Fritz
SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows Didrik Nielsen, Priyank Jaini, Emiel Hoogeboom, Ole Winther, Max Welling
Learning Causal Effects via Weighted Empirical Risk Minimization Yonghan Jung, Jin Tian, Elias Bareinboim
Revisiting the Sample Complexity of Sparse Spectrum Approximation of Gaussian Processes Minh Hoang, Nghia Hoang, Hai Pham, David Woodruff
Incorporating Interpretable Output Constraints in Bayesian Neural Networks Wanqian Yang, Lars Lorch, Moritz Graule, Himabindu Lakkaraju, Finale Doshi-Velez
Multi-Stage Influence Function Hongge Chen, Si Si, Yang Li, Ciprian Chelba, Sanjiv Kumar, Duane Boning, Cho-Jui Hsieh
Probabilistic Fair Clustering Seyed Esmaeili, Brian Brubach, Leonidas Tsepenekas, John Dickerson
Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty Miguel Monteiro, Loic Le Folgoc, Daniel Coelho de Castro, Nick Pawlowski, Bernardo Marques, Konstantinos Kamnitsas, Mark van der Wilk, Ben Glocker
ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICA Ilyes Khemakhem, Ricardo Monti, Diederik Kingma, Aapo Hyvarinen
Testing Determinantal Point Processes Khashayar Gatmiry, Maryam Aliakbarpour, Stefanie Jegelka
CogLTX: Applying BERT to Long Texts Ming Ding, Chang Zhou, Hongxia Yang, Jie Tang
f-GAIL: Learning f-Divergence for Generative Adversarial Imitation Learning Xin Zhang, Yanhua Li, Ziming Zhang, Zhi-Li Zhang
Non-parametric Models for Non-negative Functions Ulysse Marteau-Ferey, Francis Bach, Alessandro Rudi
Uncertainty Aware Semi-Supervised Learning on Graph Data Xujiang Zhao, Feng Chen, Shu Hu, Jin-Hee Cho
ConvBERT: Improving BERT with Span-based Dynamic Convolution Zi-Hang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan
Practical No-box Adversarial Attacks against DNNs Qizhang Li, Yiwen Guo, Hao Chen
Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model Gen Li, Yuting Wei, Yuejie Chi, Yuantao Gu, Yuxin Chen
Walking in the Shadow: A New Perspective on Descent Directions for Constrained Minimization Hassan Mortagy, Swati Gupta, Sebastian Pokutta
Path Sample-Analytic Gradient Estimators for Stochastic Binary Networks Alexander Shekhovtsov, Viktor Yanush, Boris Flach
Reward Propagation Using Graph Convolutional Networks Martin Klissarov, Doina Precup
LoopReg: Self-supervised Learning of Implicit Surface Correspondences, Pose and Shape for 3D Human Mesh Registration Bharat Lal Bhatnagar, Cristian Sminchisescu, Christian Theobalt, Gerard Pons-Moll
Fully Dynamic Algorithm for Constrained Submodular Optimization Silvio Lattanzi, Slobodan Mitrović, Ashkan Norouzi-Fard, Jakub M. Tarnawski, Morteza Zadimoghaddam
Robust Optimal Transport with Applications in Generative Modeling and Domain Adaptation Yogesh Balaji, Rama Chellappa, Soheil Feizi
Autofocused oracles for model-based design Clara Fannjiang, Jennifer Listgarten
Debiasing Averaged Stochastic Gradient Descent to handle missing values Aude Sportisse, Claire Boyer, Aymeric Dieuleveut, Julie Josses
Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning Younggyo Seo, Kimin Lee, Ignasi Clavera Gilaberte, Thanard Kurutach, Jinwoo Shin, Pieter Abbeel
CompRess: Self-Supervised Learning by Compressing Representations Soroush Abbasi Koohpayegani, Ajinkya Tejankar, Hamed Pirsiavash
Sample complexity and effective dimension for regression on manifolds Andrew McRae, Justin Romberg, Mark Davenport
The phase diagram of approximation rates for deep neural networks Dmitry Yarotsky, Anton Zhevnerchuk
Timeseries Anomaly Detection using Temporal Hierarchical One-Class Network Lifeng Shen, Zhuocong Li, James Kwok
EcoLight: Intersection Control in Developing Regions Under Extreme Budget and Network Constraints Sachin Chauhan, Kashish Bansal, Rijurekha Sen
Reconstructing Perceptive Images from Brain Activity by Shape-Semantic GAN Tao Fang, Yu Qi, Gang Pan
Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design Michael Dennis, Natasha Jaques, Eugene Vinitsky, Alexandre Bayen, Stuart Russell, Andrew Critch, Sergey Levine
A Spectral Energy Distance for Parallel Speech Synthesis Alexey Gritsenko, Tim Salimans, Rianne van den Berg, Jasper Snoek, Nal Kalchbrenner
Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations Joel Dapello, Tiago Marques, Martin Schrimpf, Franziska Geiger, David Cox, James J. DiCarlo
Learning from Positive and Unlabeled Data with Arbitrary Positive Shift Zayd Hammoudeh, Daniel Lowd
Deep Energy-based Modeling of Discrete-Time Physics Takashi Matsubara, Ai Ishikawa, Takaharu Yaguchi
Quantifying Learnability and Describability of Visual Concepts Emerging in Representation Learning Iro Laina, Ruth Fong, Andrea Vedaldi
Self-Learning Transformations for Improving Gaze and Head Redirection Yufeng Zheng, Seonwook Park, Xucong Zhang, Shalini De Mello, Otmar Hilliges
Language-Conditioned Imitation Learning for Robot Manipulation Tasks Simon Stepputtis, Joseph Campbell, Mariano Phielipp, Stefan Lee, Chitta Baral, Heni Ben Amor
POMDPs in Continuous Time and Discrete Spaces Bastian Alt, Matthias Schultheis, Heinz Koeppl
Exemplar Guided Active Learning Jason S. Hartford, Kevin Leyton-Brown, Hadas Raviv, Dan Padnos, Shahar Lev, Barak Lenz
Grasp Proposal Networks: An End-to-End Solution for Visual Learning of Robotic Grasps Chaozheng Wu, Jian Chen, Qiaoyu Cao, Jianchi Zhang, Yunxin Tai, Lin Sun, Kui Jia
Node Embeddings and Exact Low-Rank Representations of Complex Networks Sudhanshu Chanpuriya, Cameron Musco, Konstantinos Sotiropoulos, Charalampos Tsourakakis
Fictitious Play for Mean Field Games: Continuous Time Analysis and Applications Sarah Perrin, Julien Perolat, Mathieu Lauriere, Matthieu Geist, Romuald Elie, Olivier Pietquin
Steering Distortions to Preserve Classes and Neighbors in Supervised Dimensionality Reduction Benoît Colange, Jaakko Peltonen, Michael Aupetit, Denys Dutykh, Sylvain Lespinats
On Infinite-Width Hypernetworks Etai Littwin, Tomer Galanti, Lior Wolf, Greg Yang
Interferobot: aligning an optical interferometer by a reinforcement learning agent Dmitry Sorokin, Alexander Ulanov, Ekaterina Sazhina, Alexander Lvovsky
Program Synthesis with Pragmatic Communication Yewen Pu, Kevin Ellis, Marta Kryven, Josh Tenenbaum, Armando Solar-Lezama
Principal Neighbourhood Aggregation for Graph Nets Gabriele Corso, Luca Cavalleri, Dominique Beaini, Pietro Liò, Petar Veličković
Reliable Graph Neural Networks via Robust Aggregation Simon Geisler, Daniel Zügner, Stephan Günnemann
Instance Selection for GANs Terrance DeVries, Michal Drozdzal, Graham W. Taylor
Linear Disentangled Representations and Unsupervised Action Estimation Matthew Painter, Adam Prugel-Bennett, Jonathon Hare
Video Frame Interpolation without Temporal Priors Youjian Zhang, Chaoyue Wang, Dacheng Tao
Learning compositional functions via multiplicative weight updates Jeremy Bernstein, Jiawei Zhao, Markus Meister, Ming-Yu Liu, Anima Anandkumar, Yisong Yue
Sample Complexity of Uniform Convergence for Multicalibration Eliran Shabat, Lee Cohen, Yishay Mansour
Differentiable Neural Architecture Search in Equivalent Space with Exploration Enhancement Miao Zhang, Huiqi Li, Shirui Pan, Xiaojun Chang, Zongyuan Ge, Steven Su
The interplay between randomness and structure during learning in RNNs Friedrich Schuessler, Francesca Mastrogiuseppe, Alexis Dubreuil, Srdjan Ostojic, Omri Barak
A Generalized Neural Tangent Kernel Analysis for Two-layer Neural Networks Zixiang Chen, Yuan Cao, Quanquan Gu, Tong Zhang
Instance-wise Feature Grouping Aria Masoomi, Chieh Wu, Tingting Zhao, Zifeng Wang, Peter Castaldi, Jennifer Dy
Robust Disentanglement of a Few Factors at a Time Benjamin Estermann, Markus Marks, Mehmet Fatih Yanik
PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning Alekh Agarwal, Mikael Henaff, Sham Kakade, Wen Sun
Group Contextual Encoding for 3D Point Clouds Xu Liu, Chengtao Li, Jian Wang, Jingbo Wang, Boxin Shi, Xiaodong He
Latent Bandits Revisited Joey Hong, Branislav Kveton, Manzil Zaheer, Yinlam Chow, Amr Ahmed, Craig Boutilier
Is normalization indispensable for training deep neural network? Jie Shao, Kai Hu, Changhu Wang, Xiangyang Xue, Bhiksha Raj
Optimization and Generalization of Shallow Neural Networks with Quadratic Activation Functions Stefano Sarao Mannelli, Eric Vanden-Eijnden, Lenka Zdeborová
Intra Order-preserving Functions for Calibration of Multi-Class Neural Networks Amir Rahimi, Amirreza Shaban, Ching-An Cheng, Richard Hartley, Byron Boots
Linear Time Sinkhorn Divergences using Positive Features Meyer Scetbon, Marco Cuturi
VarGrad: A Low-Variance Gradient Estimator for Variational Inference Lorenz Richter, Ayman Boustati, Nikolas Nüsken, Francisco Ruiz, Omer Deniz Akyildiz
A Convolutional Auto-Encoder for Haplotype Assembly and Viral Quasispecies Reconstruction Ziqi Ke, Haris Vikalo
Promoting Stochasticity for Expressive Policies via a Simple and Efficient Regularization Method Qi Zhou, Yufei Kuang, Zherui Qiu, Houqiang Li, Jie Wang
Adversarial Counterfactual Learning and Evaluation for Recommender System Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, Kannan Achan
Memory-Efficient Learning of Stable Linear Dynamical Systems for Prediction and Control Georgios Mamakoukas, Orest Xherija, Todd Murphey
Evolving Normalization-Activation Layers Hanxiao Liu, Andy Brock, Karen Simonyan, Quoc Le
ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training Chia-Yu Chen, Jiamin Ni, Songtao Lu, Xiaodong Cui, Pin-Yu Chen, Xiao Sun, Naigang Wang, Swagath Venkataramani, Vijayalakshmi (Viji) Srinivasan, Wei Zhang, Kailash Gopalakrishnan
RelationNet++: Bridging Visual Representations for Object Detection via Transformer Decoder Cheng Chi, Fangyun Wei, Han Hu
Efficient Learning of Discrete Graphical Models Marc Vuffray, Sidhant Misra, Andrey Lokhov
Near-Optimal SQ Lower Bounds for Agnostically Learning Halfspaces and ReLUs under Gaussian Marginals Ilias Diakonikolas, Daniel Kane, Nikos Zarifis
Neurosymbolic Transformers for Multi-Agent Communication Jeevana Priya Inala, Yichen Yang, James Paulos, Yewen Pu, Osbert Bastani, Vijay Kumar, Martin Rinard, Armando Solar-Lezama
Fairness in Streaming Submodular Maximization: Algorithms and Hardness Marwa El Halabi, Slobodan Mitrović, Ashkan Norouzi-Fard, Jakab Tardos, Jakub M. Tarnawski
Smoothed Geometry for Robust Attribution Zifan Wang, Haofan Wang, Shakul Ramkumar, Piotr Mardziel, Matt Fredrikson, Anupam Datta
Fast Adversarial Robustness Certification of Nearest Prototype Classifiers for Arbitrary Seminorms Sascha Saralajew, Lars Holdijk, Thomas Villmann
Multi-agent active perception with prediction rewards Mikko Lauri, Frans Oliehoek
A Local Temporal Difference Code for Distributional Reinforcement Learning Pablo Tano, Peter Dayan, Alexandre Pouget
Learning with Optimized Random Features: Exponential Speedup by Quantum Machine Learning without Sparsity and Low-Rank Assumptions Hayata Yamasaki, Sathyawageeswar Subramanian, Sho Sonoda, Masato Koashi
CaSPR: Learning Canonical Spatiotemporal Point Cloud Representations Davis Rempe, Tolga Birdal, Yongheng Zhao, Zan Gojcic, Srinath Sridhar, Leonidas J. Guibas
Deep Automodulators Ari Heljakka, Yuxin Hou, Juho Kannala, Arno Solin
Convolutional Tensor-Train LSTM for Spatio-Temporal Learning Jiahao Su, Wonmin Byeon, Jean Kossaifi, Furong Huang, Jan Kautz, Anima Anandkumar
The Potts-Ising model for discrete multivariate data Zahra Razaee, Arash Amini
Interpretable multi-timescale models for predicting fMRI responses to continuous natural speech Shailee Jain, Vy Vo, Shivangi Mahto, Amanda LeBel, Javier S. Turek, Alexander Huth
Group-Fair Online Allocation in Continuous Time Semih Cayci, Swati Gupta, Atilla Eryilmaz
Decentralized TD Tracking with Linear Function Approximation and its Finite-Time Analysis Gang Wang, Songtao Lu, Georgios Giannakis, Gerald Tesauro, Jian Sun
Understanding Gradient Clipping in Private SGD: A Geometric Perspective Xiangyi Chen, Steven Z. Wu, Mingyi Hong
O(n) Connections are Expressive Enough: Universal Approximability of Sparse Transformers Chulhee Yun, Yin-Wen Chang, Srinadh Bhojanapalli, Ankit Singh Rawat, Sashank Reddi, Sanjiv Kumar
Identifying signal and noise structure in neural population activity with Gaussian process factor models Stephen Keeley, Mikio Aoi, Yiyi Yu, Spencer Smith, Jonathan W. Pillow
Equivariant Networks for Hierarchical Structures Renhao Wang, Marjan Albooyeh, Siamak Ravanbakhsh
MinMax Methods for Optimal Transport and Beyond: Regularization, Approximation and Numerics Luca De Gennaro Aquino, Stephan Eckstein
A Discrete Variational Recurrent Topic Model without the Reparametrization Trick Mehdi Rezaee, Francis Ferraro
Transferable Graph Optimizers for ML Compilers Yanqi Zhou, Sudip Roy, Amirali Abdolrashidi, Daniel Wong, Peter Ma, Qiumin Xu, Hanxiao Liu, Phitchaya Phothilimtha, Shen Wang, Anna Goldie, Azalia Mirhoseini, James Laudon
Learning with Operator-valued Kernels in Reproducing Kernel Krein Spaces Akash Saha, Balamurugan Palaniappan
Learning Bounds for Risk-sensitive Learning Jaeho Lee, Sejun Park, Jinwoo Shin
Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints Marc Finzi, Ke Alexander Wang, Andrew G. Wilson
Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency Robert Geirhos, Kristof Meding, Felix A. Wichmann
Provably Efficient Reinforcement Learning with Kernel and Neural Function Approximations Zhuoran Yang, Chi Jin, Zhaoran Wang, Mengdi Wang, Michael Jordan
Constant-Expansion Suffices for Compressed Sensing with Generative Priors Constantinos Daskalakis, Dhruv Rohatgi, Emmanouil Zampetakis
RANet: Region Attention Network for Semantic Segmentation Dingguo Shen, Yuanfeng Ji, Ping Li, Yi Wang, Di Lin
A random matrix analysis of random Fourier features: beyond the Gaussian kernel, a precise phase transition, and the corresponding double descent Zhenyu Liao, Romain Couillet, Michael W. Mahoney
Learning sparse codes from compressed representations with biologically plausible local wiring constraints Kion Fallah, Adam Willats, Ninghao Liu, Christopher Rozell
Self-Imitation Learning via Generalized Lower Bound Q-learning Yunhao Tang
Private Learning of Halfspaces: Simplifying the Construction and Reducing the Sample Complexity Haim Kaplan, Yishay Mansour, Uri Stemmer, Eliad Tsfadia
Directional Pruning of Deep Neural Networks Shih-Kang Chao, Zhanyu Wang, Yue Xing, Guang Cheng
Smoothly Bounding User Contributions in Differential Privacy Alessandro Epasto, Mohammad Mahdian, Jieming Mao, Vahab Mirrokni, Lijie Ren
Accelerating Training of Transformer-Based Language Models with Progressive Layer Dropping Minjia Zhang, Yuxiong He
Online Planning with Lookahead Policies Yonathan Efroni, Mohammad Ghavamzadeh, Shie Mannor
Learning Deep Attribution Priors Based On Prior Knowledge Ethan Weinberger, Joseph Janizek, Su-In Lee
Using noise to probe recurrent neural network structure and prune synapses Eli Moore, Rishidev Chaudhuri
NanoFlow: Scalable Normalizing Flows with Sublinear Parameter Complexity Sang-gil Lee, Sungwon Kim, Sungroh Yoon
Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge Chaoyang He, Murali Annavaram, Salman Avestimehr
Neural FFTs for Universal Texture Image Synthesis Morteza Mardani, Guilin Liu, Aysegul Dundar, Shiqiu Liu, Andrew Tao, Bryan Catanzaro
Graph Cross Networks with Vertex Infomax Pooling Maosen Li, Siheng Chen, Ya Zhang, Ivor Tsang
Instance-optimality in differential privacy via approximate inverse sensitivity mechanisms Hilal Asi, John C. Duchi
Calibration of Shared Equilibria in General Sum Partially Observable Markov Games Nelson Vadori, Sumitra Ganesh, Prashant Reddy, Manuela Veloso
MOPO: Model-based Offline Policy Optimization Tianhe Yu, Garrett Thomas, Lantao Yu, Stefano Ermon, James Y. Zou, Sergey Levine, Chelsea Finn, Tengyu Ma
Building powerful and equivariant graph neural networks with structural message-passing Clément Vignac, Andreas Loukas, Pascal Frossard
Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning Sebastian Curi, Felix Berkenkamp, Andreas Krause
Practical Low-Rank Communication Compression in Decentralized Deep Learning Thijs Vogels, Sai Praneeth Karimireddy, Martin Jaggi
Mutual exclusivity as a challenge for deep neural networks Kanishk Gandhi, Brenden M. Lake
3D Shape Reconstruction from Vision and Touch Edward Smith, Roberto Calandra, Adriana Romero, Georgia Gkioxari, David Meger, Jitendra Malik, Michal Drozdzal
GradAug: A New Regularization Method for Deep Neural Networks TAOJIANNAN YANG, Sijie Zhu, Chen Chen
An Equivalence between Loss Functions and Non-Uniform Sampling in Experience Replay Scott Fujimoto, David Meger, Doina Precup
Learning Utilities and Equilibria in Non-Truthful Auctions Hu Fu, Tao Lin
Rational neural networks Nicolas Boulle, Yuji Nakatsukasa, Alex Townsend
DISK: Learning local features with policy gradient Michał Tyszkiewicz, Pascal Fua, Eduard Trulls
Transfer Learning via $ℓ_{1}$ Regularization Masaaki Takada, Hironori Fujisawa
GOCor: Bringing Globally Optimized Correspondence Volumes into Your Neural Network Prune Truong, Martin Danelljan, Luc V. Gool, Radu Timofte
Deep Inverse Q-learning with Constraints Gabriel Kalweit, Maria Huegle, Moritz Werling, Joschka Boedecker
Optimistic Dual Extrapolation for Coherent Non-monotone Variational Inequalities Chaobing Song, Zhengyuan Zhou, Yichao Zhou, Yong Jiang, Yi Ma
Prediction with Corrupted Expert Advice Idan Amir, Idan Attias, Tomer Koren, Yishay Mansour, Roi Livni
Human Parsing Based Texture Transfer from Single Image to 3D Human via Cross-View Consistency Fang Zhao, Shengcai Liao, Kaihao Zhang, Ling Shao
Knowledge Augmented Deep Neural Networks for Joint Facial Expression and Action Unit Recognition Zijun Cui, Tengfei Song, Yuru Wang, Qiang Ji
Point process models for sequence detection in high-dimensional neural spike trains Alex Williams, Anthony Degleris, Yixin Wang, Scott Linderman
Adversarial Attacks on Linear Contextual Bandits Evrard Garcelon, Baptiste Roziere, Laurent Meunier, Jean Tarbouriech, Olivier Teytaud, Alessandro Lazaric, Matteo Pirotta
Meta-Consolidation for Continual Learning Joseph K J, Vineeth Nallure Balasubramanian
Organizing recurrent network dynamics by task-computation to enable continual learning Lea Duncker, Laura Driscoll, Krishna V. Shenoy, Maneesh Sahani, David Sussillo
Lifelong Policy Gradient Learning of Factored Policies for Faster Training Without Forgetting Jorge Mendez, Boyu Wang, Eric Eaton
Kernel Methods Through the Roof: Handling Billions of Points Efficiently Giacomo Meanti, Luigi Carratino, Lorenzo Rosasco, Alessandro Rudi
Spike and slab variational Bayes for high dimensional logistic regression Kolyan Ray, Botond Szabo, Gabriel Clara
Maximum-Entropy Adversarial Data Augmentation for Improved Generalization and Robustness Long Zhao, Ting Liu, Xi Peng, Dimitris Metaxas
Fast geometric learning with symbolic matrices Jean Feydy, Joan Glaunès, Benjamin Charlier, Michael Bronstein
MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler Zhining Liu, Pengfei Wei, Jing Jiang, Wei Cao, Jiang Bian, Yi Chang
CoinPress: Practical Private Mean and Covariance Estimation Sourav Biswas, Yihe Dong, Gautam Kamath, Jonathan Ullman
Planning with General Objective Functions: Going Beyond Total Rewards Ruosong Wang, Peilin Zhong, Simon S. Du, Russ R. Salakhutdinov, Lin Yang
Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networks Yimeng Min, Frederik Wenkel, Guy Wolf
KFC: A Scalable Approximation Algorithm for $k$ −center Fair Clustering Elfarouk Harb, Ho Shan Lam
Leveraging Predictions in Smoothed Online Convex Optimization via Gradient-based Algorithms Yingying Li, Na Li
Learning the Linear Quadratic Regulator from Nonlinear Observations Zakaria Mhammedi, Dylan J. Foster, Max Simchowitz, Dipendra Misra, Wen Sun, Akshay Krishnamurthy, Alexander Rakhlin, John Langford
Reconciling Modern Deep Learning with Traditional Optimization Analyses: The Intrinsic Learning Rate Zhiyuan Li, Kaifeng Lyu, Sanjeev Arora
Scalable Graph Neural Networks via Bidirectional Propagation Ming Chen, Zhewei Wei, Bolin Ding, Yaliang Li, Ye Yuan, Xiaoyong Du, Ji-Rong Wen
Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning Jaehyung Kim, Youngbum Hur, Sejun Park, Eunho Yang, Sung Ju Hwang, Jinwoo Shin
Assisted Learning: A Framework for Multi-Organization Learning Xun Xian, Xinran Wang, Jie Ding, Reza Ghanadan
The Strong Screening Rule for SLOPE Johan Larsson, Malgorzata Bogdan, Jonas Wallin
STLnet: Signal Temporal Logic Enforced Multivariate Recurrent Neural Networks Meiyi Ma, Ji Gao, Lu Feng, John Stankovic
Election Coding for Distributed Learning: Protecting SignSGD against Byzantine Attacks Jy-yong Sohn, Dong-Jun Han, Beongjun Choi, Jaekyun Moon
Reducing Adversarially Robust Learning to Non-Robust PAC Learning Omar Montasser, Steve Hanneke, Nati Srebro
Top-k Training of GANs: Improving GAN Performance by Throwing Away Bad Samples Samarth Sinha, Zhengli Zhao, Anirudh Goyal ALIAS PARTH GOYAL, Colin A. Raffel, Augustus Odena
Black-Box Optimization with Local Generative Surrogates Sergey Shirobokov, Vladislav Belavin, Michael Kagan, Andrei Ustyuzhanin, Atilim Gunes Baydin
Efficient Generation of Structured Objects with Constrained Adversarial Networks Luca Di Liello, Pierfrancesco Ardino, Jacopo Gobbi, Paolo Morettin, Stefano Teso, Andrea Passerini
Hard Example Generation by Texture Synthesis for Cross-domain Shape Similarity Learning Huan Fu, Shunming Li, Rongfei Jia, Mingming Gong, Binqiang Zhao, Dacheng Tao
Recovery of sparse linear classifiers from mixture of responses Venkata Gandikota, Arya Mazumdar, Soumyabrata Pal
Efficient Distance Approximation for Structured High-Dimensional Distributions via Learning Arnab Bhattacharyya, Sutanu Gayen, Kuldeep S Meel, N. V. Vinodchandran
A Single Recipe for Online Submodular Maximization with Adversarial or Stochastic Constraints Omid Sadeghi, Prasanna Raut, Maryam Fazel
Learning Sparse Prototypes for Text Generation Junxian He, Taylor Berg-Kirkpatrick, Graham Neubig
Implicit Rank-Minimizing Autoencoder Li Jing, Jure Zbontar, yann lecun
Storage Efficient and Dynamic Flexible Runtime Channel Pruning via Deep Reinforcement Learning Jianda Chen, Shangyu Chen, Sinno Jialin Pan
Task-Oriented Feature Distillation Linfeng Zhang, Yukang Shi, Zuoqiang Shi, Kaisheng Ma, Chenglong Bao
Entropic Causal Inference: Identifiability and Finite Sample Results Spencer Compton, Murat Kocaoglu, Kristjan Greenewald, Dmitriy Katz
Rewriting History with Inverse RL: Hindsight Inference for Policy Improvement Ben Eysenbach, XINYANG GENG, Sergey Levine, Russ R. Salakhutdinov
Variance-Reduced Off-Policy TDC Learning: Non-Asymptotic Convergence Analysis Shaocong Ma, Yi Zhou, Shaofeng Zou
AdaTune: Adaptive Tensor Program Compilation Made Efficient Menghao Li, Minjia Zhang, Chi Wang, Mingqin Li
When Do Neural Networks Outperform Kernel Methods? Behrooz Ghorbani, Song Mei, Theodor Misiakiewicz, Andrea Montanari
STEER : Simple Temporal Regularization For Neural ODE Arnab Ghosh, Harkirat Behl, Emilien Dupont, Philip Torr, Vinay Namboodiri
A Variational Approach for Learning from Positive and Unlabeled Data Hui Chen, Fangqing Liu, Yin Wang, Liyue Zhao, Hao Wu
Efficient Clustering Based On A Unified View Of $K$ -means And Ratio-cut Shenfei Pei, Feiping Nie, Rong Wang, Xuelong Li
Recurrent Switching Dynamical Systems Models for Multiple Interacting Neural Populations Joshua Glaser, Matthew Whiteway, John P. Cunningham, Liam Paninski, Scott Linderman
Coresets via Bilevel Optimization for Continual Learning and Streaming Zalán Borsos, Mojmir Mutny, Andreas Krause
Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs Feng Xie, Ruichu Cai, Biwei Huang, Clark Glymour, Zhifeng Hao, Kun Zhang
Understanding and Exploring the Network with Stochastic Architectures Zhijie Deng, Yinpeng Dong, Shifeng Zhang, Jun Zhu
All-or-nothing statistical and computational phase transitions in sparse spiked matrix estimation jean barbier, Nicolas Macris, Cynthia Rush
Deep Evidential Regression Alexander Amini, Wilko Schwarting, Ava Soleimany, Daniela Rus
Analytical Probability Distributions and Exact Expectation-Maximization for Deep Generative Networks Randall Balestriero, Sebastien PARIS, Richard Baraniuk
Bayesian Pseudocoresets Dionysis Manousakas, Zuheng Xu, Cecilia Mascolo, Trevor Campbell
See, Hear, Explore: Curiosity via Audio-Visual Association Victoria Dean, Shubham Tulsiani, Abhinav Gupta
Adversarial Training is a Form of Data-dependent Operator Norm Regularization Kevin Roth, Yannic Kilcher, Thomas Hofmann
A Biologically Plausible Neural Network for Slow Feature Analysis David Lipshutz, Charles Windolf, Siavash Golkar, Dmitri Chklovskii
Learning Feature Sparse Principal Subspace Lai Tian, Feiping Nie, Rong Wang, Xuelong Li
Online Adaptation for Consistent Mesh Reconstruction in the Wild Xueting Li, Sifei Liu, Shalini De Mello, Kihwan Kim, Xiaolong Wang, Ming-Hsuan Yang, Jan Kautz
Online learning with dynamics: A minimax perspective Kush Bhatia, Karthik Sridharan
Learning to Select Best Forecast Tasks for Clinical Outcome Prediction Yuan Xue, Nan Du, Anne Mottram, Martin Seneviratne, Andrew M. Dai
Stochastic Optimization with Heavy-Tailed Noise via Accelerated Gradient Clipping Eduard Gorbunov, Marina Danilova, Alexander Gasnikov
Adaptive Experimental Design with Temporal Interference: A Maximum Likelihood Approach Peter W. Glynn, Ramesh Johari, Mohammad Rasouli
From Trees to Continuous Embeddings and Back: Hyperbolic Hierarchical Clustering Ines Chami, Albert Gu, Vaggos Chatziafratis, Christopher Ré
The Autoencoding Variational Autoencoder Taylan Cemgil, Sumedh Ghaisas, Krishnamurthy Dvijotham, Sven Gowal, Pushmeet Kohli
A Fair Classifier Using Kernel Density Estimation Jaewoong Cho, Gyeongjo Hwang, Changho Suh
A Randomized Algorithm to Reduce the Support of Discrete Measures Francesco Cosentino, Harald Oberhauser, Alessandro Abate
Distributionally Robust Federated Averaging Yuyang Deng, Mohammad Mahdi Kamani, Mehrdad Mahdavi
Sharp uniform convergence bounds through empirical centralization Cyrus Cousins, Matteo Riondato
COBE: Contextualized Object Embeddings from Narrated Instructional Video Gedas Bertasius, Lorenzo Torresani
Knowledge Transfer in Multi-Task Deep Reinforcement Learning for Continuous Control Zhiyuan Xu, Kun Wu, Zhengping Che, Jian Tang, Jieping Ye
Finite Versus Infinite Neural Networks: an Empirical Study Jaehoon Lee, Samuel Schoenholz, Jeffrey Pennington, Ben Adlam, Lechao Xiao, Roman Novak, Jascha Sohl-Dickstein
Supermasks in Superposition Mitchell Wortsman, Vivek Ramanujan, Rosanne Liu, Aniruddha Kembhavi, Mohammad Rastegari, Jason Yosinski, Ali Farhadi
Nonasymptotic Guarantees for Spiked Matrix Recovery with Generative Priors Jorio Cocola, Paul Hand, Vlad Voroninski
Almost Optimal Model-Free Reinforcement Learningvia Reference-Advantage Decomposition Zihan Zhang, Yuan Zhou, Xiangyang Ji
Learning to Incentivize Other Learning Agents Jiachen Yang, Ang Li, Mehrdad Farajtabar, Peter Sunehag, Edward Hughes, Hongyuan Zha
Displacement-Invariant Matching Cost Learning for Accurate Optical Flow Estimation Jianyuan Wang, Yiran Zhong, Yuchao Dai, Kaihao Zhang, Pan Ji, Hongdong Li
Distributionally Robust Local Non-parametric Conditional Estimation Viet Anh Nguyen, Fan Zhang, Jose Blanchet, Erick Delage, Yinyu Ye
Robust Multi-Object Matching via Iterative Reweighting of the Graph Connection Laplacian Yunpeng Shi, Shaohan Li, Gilad Lerman
Meta-Gradient Reinforcement Learning with an Objective Discovered Online Zhongwen Xu, Hado P. van Hasselt, Matteo Hessel, Junhyuk Oh, Satinder Singh, David Silver
Learning Strategy-Aware Linear Classifiers Yiling Chen, Yang Liu, Chara Podimata
Upper Confidence Primal-Dual Reinforcement Learning for CMDP with Adversarial Loss Shuang Qiu, Xiaohan Wei, Zhuoran Yang, Jieping Ye, Zhaoran Wang
Calibrating Deep Neural Networks using Focal Loss Jishnu Mukhoti, Viveka Kulharia, Amartya Sanyal, Stuart Golodetz, Philip Torr, Puneet Dokania
Optimizing Mode Connectivity via Neuron Alignment Norman Tatro, Pin-Yu Chen, Payel Das, Igor Melnyk, Prasanna Sattigeri, Rongjie Lai
Information Theoretic Regret Bounds for Online Nonlinear Control Sham Kakade, Akshay Krishnamurthy, Kendall Lowrey, Motoya Ohnishi, Wen Sun
A kernel test for quasi-independence Tamara Fernandez, Wenkai Xu, Marc Ditzhaus, Arthur Gretton
First Order Constrained Optimization in Policy Space Yiming Zhang, Quan Vuong, Keith Ross
Learning Augmented Energy Minimization via Speed Scaling Etienne Bamas, Andreas Maggiori, Lars Rohwedder, Ola Svensson
Exploiting MMD and Sinkhorn Divergences for Fair and Transferable Representation Learning Luca Oneto, Michele Donini, Giulia Luise, Carlo Ciliberto, Andreas Maurer, Massimiliano Pontil
Deep Rao-Blackwellised Particle Filters for Time Series Forecasting Richard Kurle, Syama Sundar Rangapuram, Emmanuel de Bézenac, Stephan Günnemann, Jan Gasthaus
Why are Adaptive Methods Good for Attention Models? Jingzhao Zhang, Sai Praneeth Karimireddy, Andreas Veit, Seungyeon Kim, Sashank Reddi, Sanjiv Kumar, Suvrit Sra
Neural Sparse Representation for Image Restoration Yuchen Fan, Jiahui Yu, Yiqun Mei, Yulun Zhang, Yun Fu, Ding Liu, Thomas S. Huang
Boosting First-Order Methods by Shifting Objective: New Schemes with Faster Worst-Case Rates Kaiwen Zhou, Anthony Man-Cho So, James Cheng
Robust Sequence Submodular Maximization Gamal Sallam, Zizhan Zheng, Jie Wu, Bo Ji
Certified Monotonic Neural Networks Xingchao Liu, Xing Han, Na Zhang, Qiang Liu
System Identification with Biophysical Constraints: A Circuit Model of the Inner Retina Cornelius Schröder, David Klindt, Sarah Strauss, Katrin Franke, Matthias Bethge, Thomas Euler, Philipp Berens
Efficient Algorithms for Device Placement of DNN Graph Operators Jakub M. Tarnawski, Amar Phanishayee, Nikhil Devanur, Divya Mahajan, Fanny Nina Paravecino
Active Invariant Causal Prediction: Experiment Selection through Stability Juan Gamella, Christina Heinze-Deml
BOSS: Bayesian Optimization over String Spaces Henry Moss, David Leslie, Daniel Beck, Javier Gonzalez, Paul Rayson
Model Interpretability through the lens of Computational Complexity Pablo Barceló, Mikaël Monet, Jorge Pérez, Bernardo Subercaseaux
Markovian Score Climbing: Variational Inference with KL(p||q) Christian Naesseth, Fredrik Lindsten, David Blei
Improved Analysis of Clipping Algorithms for Non-convex Optimization Bohang Zhang, Jikai Jin, Cong Fang, Liwei Wang
Bias no more: high-probability data-dependent regret bounds for adversarial bandits and MDPs Chung-Wei Lee, Haipeng Luo, Chen-Yu Wei, Mengxiao Zhang
A Ranking-based, Balanced Loss Function Unifying Classification and Localisation in Object Detection Kemal Oksuz, Baris Can Cam, Emre Akbas, Sinan Kalkan
StratLearner: Learning a Strategy for Misinformation Prevention in Social Networks Guangmo Tong
A Unified Switching System Perspective and Convergence Analysis of Q-Learning Algorithms Donghwan Lee, Niao He
Kernel Alignment Risk Estimator: Risk Prediction from Training Data Arthur Jacot, Berfin Simsek, Francesco Spadaro, Clement Hongler, Franck Gabriel
Calibrating CNNs for Lifelong Learning Pravendra Singh, Vinay Kumar Verma, Pratik Mazumder, Lawrence Carin, Piyush Rai
Online Convex Optimization Over Erdos-Renyi Random Networks Jinlong Lei, Peng Yi, Yiguang Hong, Jie Chen, Guodong Shi
Robustness of Bayesian Neural Networks to Gradient-Based Attacks Ginevra Carbone, Matthew Wicker, Luca Laurenti, Andrea Patane’, Luca Bortolussi, Guido Sanguinetti
Parametric Instance Classification for Unsupervised Visual Feature learning Yue Cao, Zhenda Xie, Bin Liu, Yutong Lin, Zheng Zhang, Han Hu
Sparse Weight Activation Training Md Aamir Raihan, Tor Aamodt
Collapsing Bandits and Their Application to Public Health Intervention Aditya Mate, Jackson Killian, Haifeng Xu, Andrew Perrault, Milind Tambe
Neural Sparse Voxel Fields Lingjie Liu, Jiatao Gu, Kyaw Zaw Lin, Tat-Seng Chua, Christian Theobalt
A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game Encoding Bruno Lecouat, Jean Ponce, Julien Mairal
The Discrete Gaussian for Differential Privacy Clément L. Canonne, Gautam Kamath, Thomas Steinke
Robust Sub-Gaussian Principal Component Analysis and Width-Independent Schatten Packing Arun Jambulapati, Jerry Li, Kevin Tian
Adaptive Importance Sampling for Finite-Sum Optimization and Sampling with Decreasing Step-Sizes Ayoub El Hanchi, David Stephens
Learning efficient task-dependent representations with synaptic plasticity Colin Bredenberg, Eero Simoncelli, Cristina Savin
A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions Wei Deng, Guang Lin, Faming Liang
Error Bounds of Imitating Policies and Environments Tian Xu, Ziniu Li, Yang Yu
Disentangling Human Error from Ground Truth in Segmentation of Medical Images Le Zhang, Ryutaro Tanno, Moucheng Xu, Chen Jin, Joseph Jacob, Olga Cicarrelli, Frederik Barkhof, Daniel Alexander
Consequences of Misaligned AI Simon Zhuang, Dylan Hadfield-Menell
Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning Julien Roy, Paul Barde, Félix Harvey, Derek Nowrouzezahrai, Chris Pal
Emergent Reciprocity and Team Formation from Randomized Uncertain Social Preferences Bowen Baker
Hitting the High Notes: Subset Selection for Maximizing Expected Order Statistics Aranyak Mehta, Uri Nadav, Alexandros Psomas, Aviad Rubinstein
Towards Scale-Invariant Graph-related Problem Solving by Iterative Homogeneous GNNs Hao Tang, Zhiao Huang, Jiayuan Gu, Bao-Liang Lu, Hao Su
Regret Bounds without Lipschitz Continuity: Online Learning with Relative-Lipschitz Losses Yihan Zhou, Victor Sanches Portella, Mark Schmidt, Nicholas Harvey
The Lottery Ticket Hypothesis for Pre-trained BERT Networks Tianlong Chen, Jonathan Frankle, Shiyu Chang, Sijia Liu, Yang Zhang, Zhangyang Wang, Michael Carbin
Label-Aware Neural Tangent Kernel: Toward Better Generalization and Local Elasticity Shuxiao Chen, Hangfeng He, Weijie Su
Beyond Perturbations: Learning Guarantees with Arbitrary Adversarial Test Examples Shafi Goldwasser, Adam Tauman Kalai, Yael Kalai, Omar Montasser
AdvFlow: Inconspicuous Black-box Adversarial Attacks using Normalizing Flows Hadi Mohaghegh Dolatabadi, Sarah Erfani, Christopher Leckie
Few-shot Image Generation with Elastic Weight Consolidation Yijun Li, Richard Zhang, Jingwan (Cynthia) Lu, Eli Shechtman
On the Expressiveness of Approximate Inference in Bayesian Neural Networks Andrew Foong, David Burt, Yingzhen Li, Richard Turner
Non-Crossing Quantile Regression for Distributional Reinforcement Learning Fan Zhou, Jianing Wang, Xingdong Feng
Dark Experience for General Continual Learning: a Strong, Simple Baseline Pietro Buzzega, Matteo Boschini, Angelo Porrello, Davide Abati, SIMONE CALDERARA
Learning to Utilize Shaping Rewards: A New Approach of Reward Shaping Yujing Hu, Weixun Wang, Hangtian Jia, Yixiang Wang, Yingfeng Chen, Jianye Hao, Feng Wu, Changjie Fan
Neural encoding with visual attention Meenakshi Khosla, Gia Ngo, Keith Jamison, Amy Kuceyeski, Mert Sabuncu
On the linearity of large non-linear models: when and why the tangent kernel is constant Chaoyue Liu, Libin Zhu, Mikhail Belkin
PLLay: Efficient Topological Layer based on Persistent Landscapes Kwangho Kim, Jisu Kim, Manzil Zaheer, Joon Kim, Frederic Chazal, Larry Wasserman
Decentralized Langevin Dynamics for Bayesian Learning Anjaly Parayil, He Bai, Jemin George, Prudhvi Gurram
Shared Space Transfer Learning for analyzing multi-site fMRI data Muhammad Yousefnezhad, Alessandro Selvitella, Daoqiang Zhang, Andrew Greenshaw, Russell Greiner
The Diversified Ensemble Neural Network Shaofeng Zhang, Meng Liu, Junchi Yan
Inductive Quantum Embedding Santosh Kumar Srivastava, Dinesh Khandelwal, Dhiraj Madan, Dinesh Garg, Hima Karanam, L Venkata Subramaniam
Variational Bayesian Unlearning Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet
Batched Coarse Ranking in Multi-Armed Bandits Nikolai Karpov, Qin Zhang
Understanding and Improving Fast Adversarial Training Maksym Andriushchenko, Nicolas Flammarion
Coded Sequential Matrix Multiplication For Straggler Mitigation Nikhil Krishnan Muralee Krishnan, Seyederfan Hosseini, Ashish Khisti
Attack of the Tails: Yes, You Really Can Backdoor Federated Learning Hongyi Wang, Kartik Sreenivasan, Shashank Rajput, Harit Vishwakarma, Saurabh Agarwal, Jy-yong Sohn, Kangwook Lee, Dimitris Papailiopoulos
Certifiably Adversarially Robust Detection of Out-of-Distribution Data Julian Bitterwolf, Alexander Meinke, Matthias Hein
Domain Generalization via Entropy Regularization Shanshan Zhao, Mingming Gong, Tongliang Liu, Huan Fu, Dacheng Tao
Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels Massimiliano Patacchiola, Jack Turner, Elliot J. Crowley, Michael O’Boyle, Amos J. Storkey
Skeleton-bridged Point Completion: From Global Inference to Local Adjustment Yinyu Nie, Yiqun Lin, Xiaoguang Han, Shihui Guo, Jian Chang, Shuguang Cui, Jian.J Zhang
Compressing Images by Encoding Their Latent Representations with Relative Entropy Coding Gergely Flamich, Marton Havasi, José Miguel Hernández-Lobato
Improved Guarantees for k-means++ and k-means++ Parallel Konstantin Makarychev, Aravind Reddy, Liren Shan
Sparse Spectrum Warped Input Measures for Nonstationary Kernel Learning Anthony Tompkins, Rafael Oliveira, Fabio T. Ramos
An Efficient Adversarial Attack for Tree Ensembles Chong Zhang, Huan Zhang, Cho-Jui Hsieh
Learning Continuous System Dynamics from Irregularly-Sampled Partial Observations Zijie Huang, Yizhou Sun, Wei Wang
Online Bayesian Persuasion Matteo Castiglioni, Andrea Celli, Alberto Marchesi, Nicola Gatti
Robust Pre-Training by Adversarial Contrastive Learning Ziyu Jiang, Tianlong Chen, Ting Chen, Zhangyang Wang
Random Walk Graph Neural Networks Giannis Nikolentzos, Michalis Vazirgiannis
Explore Aggressively, Update Conservatively: Stochastic Extragradient Methods with Variable Stepsize Scaling Yu-Guan Hsieh, Franck Iutzeler, Jérôme Malick, Panayotis Mertikopoulos
Fast and Accurate $k$ -means++ via Rejection Sampling Vincent Cohen-Addad, Silvio Lattanzi, Ashkan Norouzi-Fard, Christian Sohler, Ola Svensson
Variational Amodal Object Completion Huan Ling, David Acuna, Karsten Kreis, Seung Wook Kim, Sanja Fidler
When Counterpoint Meets Chinese Folk Melodies Nan Jiang, Sheng Jin, Zhiyao Duan, Changshui Zhang
Sub-linear Regret Bounds for Bayesian Optimisation in Unknown Search Spaces Hung Tran-The, Sunil Gupta, Santu Rana, Huong Ha, Svetha Venkatesh
Universal Domain Adaptation through Self Supervision Kuniaki Saito, Donghyun Kim, Stan Sclaroff, Kate Saenko
Patch2Self: Denoising Diffusion MRI with Self-Supervised Learning Shreyas Fadnavis, Joshua Batson, Eleftherios Garyfallidis
Stochastic Normalization Zhi Kou, Kaichao You, Mingsheng Long, Jianmin Wang
Constrained episodic reinforcement learning in concave-convex and knapsack settings Kianté Brantley, Miro Dudik, Thodoris Lykouris, Sobhan Miryoosefi, Max Simchowitz, Aleksandrs Slivkins, Wen Sun
On Learning Ising Models under Huber’s Contamination Model Adarsh Prasad, Vishwak Srinivasan, Sivaraman Balakrishnan, Pradeep Ravikumar
Cross-validation Confidence Intervals for Test Error Pierre Bayle, Alexandre Bayle, Lucas Janson, Lester Mackey
DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation Alexandre Carlier, Martin Danelljan, Alexandre Alahi, Radu Timofte
Bayesian Attention Modules Xinjie Fan, Shujian Zhang, Bo Chen, Mingyuan Zhou
Robustness Analysis of Non-Convex Stochastic Gradient Descent using Biased Expectations Kevin Scaman, Cedric Malherbe
SoftFlow: Probabilistic Framework for Normalizing Flow on Manifolds Hyeongju Kim, Hyeonseung Lee, Woo Hyun Kang, Joun Yeop Lee, Nam Soo Kim
A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network Basile Confavreux, Friedemann Zenke, Everton Agnes, Timothy Lillicrap, Tim Vogels
Greedy Optimization Provably Wins the Lottery: Logarithmic Number of Winning Tickets is Enough Mao Ye, Lemeng Wu, Qiang Liu
Path Integral Based Convolution and Pooling for Graph Neural Networks Zheng Ma, Junyu Xuan, Yu Guang Wang, Ming Li, Pietro Liò
Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks Ioana Bica, James Jordon, Mihaela van der Schaar
Latent Dynamic Factor Analysis of High-Dimensional Neural Recordings Heejong Bong, Zongge Liu, Zhao Ren, Matthew Smith, Valerie Ventura, Kass E. Robert
Conditioning and Processing: Techniques to Improve Information-Theoretic Generalization Bounds Hassan Hafez-Kolahi, Zeinab Golgooni, Shohreh Kasaei, Mahdieh Soleymani
Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning Weili Nie, Zhiding Yu, Lei Mao, Ankit B. Patel, Yuke Zhu, Anima Anandkumar
GAN Memory with No Forgetting Yulai Cong, Miaoyun Zhao, Jianqiao Li, Sijia Wang, Lawrence Carin
Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based Games Yunqiu Xu, Meng Fang, Ling Chen, Yali Du, Joey Tianyi Zhou, Chengqi Zhang
Gaussian Gated Linear Networks David Budden, Adam Marblestone, Eren Sezener, Tor Lattimore, Gregory Wayne, Joel Veness
Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network Embedding Lin Lan, Pinghui Wang, Xuefeng Du, Kaikai Song, Jing Tao, Xiaohong Guan
Online Fast Adaptation and Knowledge Accumulation (OSAKA): a New Approach to Continual Learning Massimo Caccia, Pau Rodriguez, Oleksiy Ostapenko, Fabrice Normandin, Min Lin, Lucas Page-Caccia, Issam Hadj Laradji, Irina Rish, Alexandre Lacoste, David Vázquez, Laurent Charlin
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Simultaneously Learning Stochastic and Adversarial Episodic MDPs with Known Transition Tiancheng Jin, Haipeng Luo
Relative gradient optimization of the Jacobian term in unsupervised deep learning Luigi Gresele, Giancarlo Fissore, Adrián Javaloy, Bernhard Schölkopf, Aapo Hyvarinen
Self-Supervised Visual Representation Learning from Hierarchical Grouping Xiao Zhang, Michael Maire
Optimal Variance Control of the Score-Function Gradient Estimator for Importance-Weighted Bounds Valentin Liévin, Andrea Dittadi, Anders Christensen, Ole Winther
Explicit Regularisation in Gaussian Noise Injections Alexander Camuto, Matthew Willetts, Umut Simsekli, Stephen J. Roberts, Chris C. Holmes
Numerically Solving Parametric Families of High-Dimensional Kolmogorov Partial Differential Equations via Deep Learning Julius Berner, Markus Dablander, Philipp Grohs
Finite-Time Analysis for Double Q-learning Huaqing Xiong, Lin Zhao, Yingbin Liang, Wei Zhang
Learning to Detect Objects with a 1 Megapixel Event Camera Etienne Perot, Pierre de Tournemire, Davide Nitti, Jonathan Masci, Amos Sironi
End-to-End Learning and Intervention in Games Jiayang Li, Jing Yu, Yu Nie, Zhaoran Wang
Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms Dheeraj Nagaraj, Xian Wu, Guy Bresler, Prateek Jain, Praneeth Netrapalli
Predictive coding in balanced neural networks with noise, chaos and delays Jonathan Kadmon, Jonathan Timcheck, Surya Ganguli
Interpolation Technique to Speed Up Gradients Propagation in Neural ODEs Talgat Daulbaev, Alexandr Katrutsa, Larisa Markeeva, Julia Gusak, Andrzej Cichocki, Ivan Oseledets
On the Equivalence between Online and Private Learnability beyond Binary Classification Young Jung, Baekjin Kim, Ambuj Tewari
AViD Dataset: Anonymized Videos from Diverse Countries AJ Piergiovanni, Michael Ryoo
Probably Approximately Correct Constrained Learning Luiz Chamon, Alejandro Ribeiro
RATT: Recurrent Attention to Transient Tasks for Continual Image Captioning Riccardo Del Chiaro, Bartłomiej Twardowski, Andrew Bagdanov, Joost van de Weijer
Decisions, Counterfactual Explanations and Strategic Behavior Stratis Tsirtsis, Manuel Gomez Rodriguez
Hierarchical Patch VAE-GAN: Generating Diverse Videos from a Single Sample Shir Gur, Sagie Benaim, Lior Wolf
A Feasible Level Proximal Point Method for Nonconvex Sparse Constrained Optimization Digvijay Boob, Qi Deng, Guanghui Lan, Yilin Wang
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Comprehensive Attention Self-Distillation for Weakly-Supervised Object Detection Zeyi Huang, Yang Zou, B. V. K. Vijaya Kumar, Dong Huang
Linear Dynamical Systems as a Core Computational Primitive Shiva Kaul
Ratio Trace Formulation of Wasserstein Discriminant Analysis Hexuan Liu, Yunfeng Cai, You-Lin Chen, Ping Li
PAC-Bayes Analysis Beyond the Usual Bounds Omar Rivasplata, Ilja Kuzborskij, Csaba Szepesvari, John Shawe-Taylor
Few-shot Visual Reasoning with Meta-Analogical Contrastive Learning Youngsung Kim, Jinwoo Shin, Eunho Yang, Sung Ju Hwang
MPNet: Masked and Permuted Pre-training for Language Understanding Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu
Reinforcement Learning with Feedback Graphs Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan
Zap Q-Learning With Nonlinear Function Approximation Shuhang Chen, Adithya M Devraj, Fan Lu, Ana Busic, Sean Meyn
Lipschitz-Certifiable Training with a Tight Outer Bound Sungyoon Lee, Jaewook Lee, Saerom Park
Fast Adaptive Non-Monotone Submodular Maximization Subject to a Knapsack Constraint Georgios Amanatidis, Federico Fusco, Philip Lazos, Stefano Leonardi, Rebecca Reiffenhäuser
Conformal Symplectic and Relativistic Optimization Guilherme Franca, Jeremias Sulam, Daniel Robinson, Rene Vidal
Bayes Consistency vs. H-Consistency: The Interplay between Surrogate Loss Functions and the Scoring Function Class Mingyuan Zhang, Shivani Agarwal
Inverting Gradients – How easy is it to break privacy in federated learning? Jonas Geiping, Hartmut Bauermeister, Hannah Dröge, Michael Moeller
Dynamic allocation of limited memory resources in reinforcement learning Nisheet Patel, Luigi Acerbi, Alexandre Pouget
CryptoNAS: Private Inference on a ReLU Budget Zahra Ghodsi, Akshaj Kumar Veldanda, Brandon Reagen, Siddharth Garg
A Stochastic Path Integral Differential EstimatoR Expectation Maximization Algorithm Gersende Fort, Eric Moulines, Hoi-To Wai
CHIP: A Hawkes Process Model for Continuous-time Networks with Scalable and Consistent Estimation Makan Arastuie, Subhadeep Paul, Kevin Xu
SAC: Accelerating and Structuring Self-Attention via Sparse Adaptive Connection Xiaoya Li, Yuxian Meng, Mingxin Zhou, Qinghong Han, Fei Wu, Jiwei Li
Design Space for Graph Neural Networks Jiaxuan You, Zhitao Ying, Jure Leskovec
HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis Jungil Kong, Jaehyeon Kim, Jaekyoung Bae
Unbalanced Sobolev Descent Youssef Mroueh, Mattia Rigotti
Identifying Mislabeled Data using the Area Under the Margin Ranking Geoff Pleiss, Tianyi Zhang, Ethan Elenberg, Kilian Q. Weinberger
Combining Deep Reinforcement Learning and Search for Imperfect-Information Games Noam Brown, Anton Bakhtin, Adam Lerer, Qucheng Gong
High-Throughput Synchronous Deep RL Iou-Jen Liu, Raymond Yeh, Alexander Schwing
Contrastive Learning with Adversarial Examples Chih-Hui Ho, Nuno Nvasconcelos
Mixed Hamiltonian Monte Carlo for Mixed Discrete and Continuous Variables Guangyao Zhou
Adversarial Sparse Transformer for Time Series Forecasting Sifan Wu, Xi Xiao, Qianggang Ding, Peilin Zhao, Ying Wei, Junzhou Huang
The Surprising Simplicity of the Early-Time Learning Dynamics of Neural Networks Wei Hu, Lechao Xiao, Ben Adlam, Jeffrey Pennington
CLEARER: Multi-Scale Neural Architecture Search for Image Restoration Yuanbiao Gou, Boyun Li, Zitao Liu, Songfan Yang, Xi Peng
Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights Theofanis Karaletsos, Thang D. Bui
Compositional Explanations of Neurons Jesse Mu, Jacob Andreas
Calibrated Reliable Regression using Maximum Mean Discrepancy Peng Cui, Wenbo Hu, Jun Zhu
Directional convergence and alignment in deep learning Ziwei Ji, Matus Telgarsky
Functional Regularization for Representation Learning: A Unified Theoretical Perspective Siddhant Garg, Yingyu Liang
Provably Efficient Online Hyperparameter Optimization with Population-Based Bandits Jack Parker-Holder, Vu Nguyen, Stephen J. Roberts
Understanding Global Feature Contributions With Additive Importance Measures Ian Covert, Scott M. Lundberg, Su-In Lee
Online Non-Convex Optimization with Imperfect Feedback Amélie Héliou, Matthieu Martin, Panayotis Mertikopoulos, Thibaud Rahier
Co-Tuning for Transfer Learning Kaichao You, Zhi Kou, Mingsheng Long, Jianmin Wang
Multifaceted Uncertainty Estimation for Label-Efficient Deep Learning Weishi Shi, Xujiang Zhao, Feng Chen, Qi Yu
Continuous Surface Embeddings Natalia Neverova, David Novotny, Marc Szafraniec, Vasil Khalidov, Patrick Labatut, Andrea Vedaldi
Succinct and Robust Multi-Agent Communication With Temporal Message Control Sai Qian Zhang, Qi Zhang, Jieyu Lin
Big Bird: Transformers for Longer Sequences Manzil Zaheer, Guru Guruganesh, Kumar Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed
Neural Execution Engines: Learning to Execute Subroutines Yujun Yan, Kevin Swersky, Danai Koutra, Parthasarathy Ranganathan, Milad Hashemi
Random Reshuffling: Simple Analysis with Vast Improvements Konstantin Mishchenko, Ahmed Khaled Ragab Bayoumi, Peter Richtarik
Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors Karl Pertsch, Oleh Rybkin, Frederik Ebert, Shenghao Zhou, Dinesh Jayaraman, Chelsea Finn, Sergey Levine
Statistical Optimal Transport posed as Learning Kernel Embedding Saketha Nath Jagarlapudi, Pratik Kumar Jawanpuria
Dual-Resolution Correspondence Networks Xinghui Li, Kai Han, Shuda Li, Victor Prisacariu
Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization Abhinav Agrawal, Daniel R. Sheldon, Justin Domke
f-Divergence Variational Inference Neng Wan, Dapeng Li, NAIRA HOVAKIMYAN
Unfolding recurrence by Green’s functions for optimized reservoir computing Sandra Nestler, Christian Keup, David Dahmen, Matthieu Gilson, Holger Rauhut, Moritz Helias
The Dilemma of TriHard Loss and an Element-Weighted TriHard Loss for Person Re-Identification Yihao Lv, Youzhi Gu, Liu Xinggao
Disentangling by Subspace Diffusion David Pfau, Irina Higgins, Alex Botev, Sébastien Racanière
Towards Neural Programming Interfaces Zachary Brown, Nathaniel Robinson, David Wingate, Nancy Fulda
Discovering Symbolic Models from Deep Learning with Inductive Biases Miles Cranmer, Alvaro Sanchez Gonzalez, Peter Battaglia, Rui Xu, Kyle Cranmer, David Spergel, Shirley Ho
Real World Games Look Like Spinning Tops Wojciech M. Czarnecki, Gauthier Gidel, Brendan Tracey, Karl Tuyls, Shayegan Omidshafiei, David Balduzzi, Max Jaderberg
Cooperative Heterogeneous Deep Reinforcement Learning Han Zheng, Pengfei Wei, Jing Jiang, Guodong Long, Qinghua Lu, Chengqi Zhang
Mitigating Forgetting in Online Continual Learning via Instance-Aware Parameterization Hung-Jen Chen, An-Chieh Cheng, Da-Cheng Juan, Wei Wei, Min Sun
ImpatientCapsAndRuns: Approximately Optimal Algorithm Configuration from an Infinite Pool Gellert Weisz, András György, Wei-I Lin, Devon Graham, Kevin Leyton-Brown, Csaba Szepesvari, Brendan Lucier
Dense Correspondences between Human Bodies via Learning Transformation Synchronization on Graphs Xiangru Huang, Haitao Yang, Etienne Vouga, Qixing Huang
Reasoning about Uncertainties in Discrete-Time Dynamical Systems using Polynomial Forms. Sriram Sankaranarayanan, Yi Chou, Eric Goubault, Sylvie Putot
Applications of Common Entropy for Causal Inference Murat Kocaoglu, Sanjay Shakkottai, Alexandros G. Dimakis, Constantine Caramanis, Sriram Vishwanath
SGD with shuffling: optimal rates without component convexity and large epoch requirements Kwangjun Ahn, Chulhee Yun, Suvrit Sra
Unsupervised Joint k-node Graph Representations with Compositional Energy-Based Models Leonardo Cotta, Carlos H. C. Teixeira, Ananthram Swami, Bruno Ribeiro
Neural Manifold Ordinary Differential Equations Aaron Lou, Derek Lim, Isay Katsman, Leo Huang, Qingxuan Jiang, Ser Nam Lim, Christopher M. De Sa
CO-Optimal Transport Vayer Titouan, Ievgen Redko, Rémi Flamary, Nicolas Courty
Continuous Meta-Learning without Tasks James Harrison, Apoorva Sharma, Chelsea Finn, Marco Pavone
A mathematical theory of cooperative communication Pei Wang, Junqi Wang, Pushpi Paranamana, Patrick Shafto
Penalized Langevin dynamics with vanishing penalty for smooth and log-concave targets Avetik Karagulyan, Arnak Dalalyan
Learning Invariances in Neural Networks from Training Data Gregory Benton, Marc Finzi, Pavel Izmailov, Andrew G. Wilson
A Finite-Time Analysis of Two Time-Scale Actor-Critic Methods Yue Wu, Weitong ZHANG, Pan Xu, Quanquan Gu
Pruning Filter in Filter Fanxu Meng, Hao Cheng, Ke Li, Huixiang Luo, Xiaowei Guo, Guangming Lu, Xing Sun
Learning to Mutate with Hypergradient Guided Population Zhiqiang Tao, Yaliang Li, Bolin Ding, Ce Zhang, Jingren Zhou, Yun Fu
A convex optimization formulation for multivariate regression Yunzhang Zhu
Online Meta-Critic Learning for Off-Policy Actor-Critic Methods Wei Zhou, Yiying Li, Yongxin Yang, Huaimin Wang, Timothy Hospedales
The All-or-Nothing Phenomenon in Sparse Tensor PCA Jonathan Niles-Weed, Ilias Zadik
Synthesize, Execute and Debug: Learning to Repair for Neural Program Synthesis Kavi Gupta, Peter Ebert Christensen, Xinyun Chen, Dawn Song
ARMA Nets: Expanding Receptive Field for Dense Prediction Jiahao Su, Shiqi Wang, Furong Huang
Diversity-Guided Multi-Objective Bayesian Optimization With Batch Evaluations Mina Konakovic Lukovic, Yunsheng Tian, Wojciech Matusik
SOLOv2: Dynamic and Fast Instance Segmentation Xinlong Wang, Rufeng Zhang, Tao Kong, Lei Li, Chunhua Shen
Robust Recovery via Implicit Bias of Discrepant Learning Rates for Double Over-parameterization Chong You, Zhihui Zhu, Qing Qu, Yi Ma
Axioms for Learning from Pairwise Comparisons Ritesh Noothigattu, Dominik Peters, Ariel D. Procaccia
Continuous Regularized Wasserstein Barycenters Lingxiao Li, Aude Genevay, Mikhail Yurochkin, Justin M. Solomon
Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting Defu Cao, Yujing Wang, Juanyong Duan, Ce Zhang, Xia Zhu, Congrui Huang, Yunhai Tong, Bixiong Xu, Jing Bai, Jie Tong, Qi Zhang
Online Multitask Learning with Long-Term Memory Mark Herbster, Stephen Pasteris, Lisa Tse
Fewer is More: A Deep Graph Metric Learning Perspective Using Fewer Proxies Yuehua Zhu, Muli Yang, Cheng Deng, Wei Liu
Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting LEI BAI, Lina Yao, Can Li, Xianzhi Wang, Can Wang
On Reward-Free Reinforcement Learning with Linear Function Approximation Ruosong Wang, Simon S. Du, Lin Yang, Russ R. Salakhutdinov
Robustness of Community Detection to Random Geometric Perturbations Sandrine Peche, Vianney Perchet
Learning outside the Black-Box: The pursuit of interpretable models Jonathan Crabbe, Yao Zhang, William Zame, Mihaela van der Schaar
Breaking Reversibility Accelerates Langevin Dynamics for Non-Convex Optimization Xuefeng GAO, Mert Gurbuzbalaban, Lingjiong Zhu
Robust large-margin learning in hyperbolic space Melanie Weber, Manzil Zaheer, Ankit Singh Rawat, Aditya K. Menon, Sanjiv Kumar
Replica-Exchange Nos\’e-Hoover Dynamics for Bayesian Learning on Large Datasets Rui Luo, Qiang Zhang, Yaodong Yang, Jun Wang
Adversarially Robust Few-Shot Learning: A Meta-Learning Approach Micah Goldblum, Liam Fowl, Tom Goldstein
Neural Anisotropy Directions Guillermo Ortiz-Jimenez, Apostolos Modas, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard
Digraph Inception Convolutional Networks Zekun Tong, Yuxuan Liang, Changsheng Sun, Xinke Li, David Rosenblum, Andrew Lim
PAC-Bayesian Bound for the Conditional Value at Risk Zakaria Mhammedi, Benjamin Guedj, Robert C. Williamson
Stochastic Stein Discrepancies Jackson Gorham, Anant Raj, Lester Mackey
On the Role of Sparsity and DAG Constraints for Learning Linear DAGs Ignavier Ng, AmirEmad Ghassami, Kun Zhang
Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Search Houwen Peng, Hao Du, Hongyuan Yu, QI LI, Jing Liao, Jianlong Fu
Fair Multiple Decision Making Through Soft Interventions Yaowei Hu, Yongkai Wu, Lu Zhang, Xintao Wu
Representation Learning for Integrating Multi-domain Outcomes to Optimize Individualized Treatment Yuan Chen, Donglin Zeng, Tianchen Xu, Yuanjia Wang
Learning to Play No-Press Diplomacy with Best Response Policy Iteration Thomas Anthony, Tom Eccles, Andrea Tacchetti, János Kramár, Ian Gemp, Thomas Hudson, Nicolas Porcel, Marc Lanctot, Julien Perolat, Richard Everett, Satinder Singh, Thore Graepel, Yoram Bachrach
Inverse Learning of Symmetries Mario Wieser, Sonali Parbhoo, Aleksander Wieczorek, Volker Roth
DiffGCN: Graph Convolutional Networks via Differential Operators and Algebraic Multigrid Pooling Moshe Eliasof, Eran Treister
Distributed Newton Can Communicate Less and Resist Byzantine Workers Avishek Ghosh, Raj Kumar Maity, Arya Mazumdar
Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees Shali Jiang, Daniel Jiang, Maximilian Balandat, Brian Karrer, Jacob Gardner, Roman Garnett
Effective Diversity in Population Based Reinforcement Learning Jack Parker-Holder, Aldo Pacchiano, Krzysztof M. Choromanski, Stephen J. Roberts
Elastic-InfoGAN: Unsupervised Disentangled Representation Learning in Class-Imbalanced Data Utkarsh Ojha, Krishna Kumar Singh, Cho-Jui Hsieh, Yong Jae Lee
Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces Guy Lorberbom, Chris J. Maddison, Nicolas Heess, Tamir Hazan, Daniel Tarlow
Hybrid Models for Learning to Branch Prateek Gupta, Maxime Gasse, Elias Khalil, Pawan Mudigonda, Andrea Lodi, Yoshua Bengio
WoodFisher: Efficient Second-Order Approximation for Neural Network Compression Sidak Pal Singh, Dan Alistarh
Bi-level Score Matching for Learning Energy-based Latent Variable Models Fan Bao, Chongxuan LI, Taufik Xu, Hang Su, Jun Zhu, Bo Zhang
Counterfactual Contrastive Learning for Weakly-Supervised Vision-Language Grounding Zhu Zhang, Zhou Zhao, Zhijie Lin, jieming zhu, Xiuqiang He
Decision trees as partitioning machines to characterize their generalization properties Jean-Samuel Leboeuf, Frédéric LeBlanc, Mario Marchand
Learning to Prove Theorems by Learning to Generate Theorems Mingzhe Wang, Jia Deng
3D Self-Supervised Methods for Medical Imaging Aiham Taleb, Winfried Loetzsch, Noel Danz, Julius Severin, Thomas Gaertner, Benjamin Bergner, Christoph Lippert
Bayesian filtering unifies adaptive and non-adaptive neural network optimization methods Laurence Aitchison
Worst-Case Analysis for Randomly Collected Data Justin Chen, Gregory Valiant, Paul Valiant
Truthful Data Acquisition via Peer Prediction Yiling Chen, Yiheng Shen, Shuran Zheng
Learning Robust Decision Policies from Observational Data Muhammad Osama, Dave Zachariah, Peter Stoica
Byzantine Resilient Distributed Multi-Task Learning Jiani Li, Waseem Abbas, Xenofon Koutsoukos
Reinforcement Learning in Factored MDPs: Oracle-Efficient Algorithms and Tighter Regret Bounds for the Non-Episodic Setting Ziping Xu, Ambuj Tewari
Improving model calibration with accuracy versus uncertainty optimization Ranganath Krishnan, Omesh Tickoo
The Convolution Exponential and Generalized Sylvester Flows Emiel Hoogeboom, Victor Garcia Satorras, Jakub Tomczak, Max Welling
An Improved Analysis of Stochastic Gradient Descent with Momentum Yanli Liu, Yuan Gao, Wotao Yin
Precise expressions for random projections: Low-rank approximation and randomized Newton Michal Derezinski, Feynman T. Liang, Zhenyu Liao, Michael W. Mahoney
The MAGICAL Benchmark for Robust Imitation Sam Toyer, Rohin Shah, Andrew Critch, Stuart Russell
X-CAL: Explicit Calibration for Survival Analysis Mark Goldstein, Xintian Han, Aahlad Puli, Adler Perotte , Rajesh Ranganath
Decentralized Accelerated Proximal Gradient Descent Haishan Ye, Ziang Zhou, Luo Luo, Tong Zhang
Making Non-Stochastic Control (Almost) as Easy as Stochastic Max Simchowitz
BERT Loses Patience: Fast and Robust Inference with Early Exit Wangchunshu Zhou, Canwen Xu, Tao Ge, Julian McAuley, Ke Xu, Furu Wei
Optimal and Practical Algorithms for Smooth and Strongly Convex Decentralized Optimization Dmitry Kovalev, Adil SALIM, Peter Richtarik
BAIL: Best-Action Imitation Learning for Batch Deep Reinforcement Learning Xinyue Chen, Zijian Zhou, Zheng Wang, Che Wang, Yanqiu Wu, Keith Ross
Regularizing Towards Permutation Invariance In Recurrent Models Edo Cohen-Karlik, Avichai Ben David, Amir Globerson
What Did You Think Would Happen? Explaining Agent Behaviour through Intended Outcomes Herman Ho-Man Yau, Chris Russell, Simon Hadfield
Batch normalization provably avoids ranks collapse for randomly initialised deep networks Hadi Daneshmand, Jonas Kohler, Francis Bach, Thomas Hofmann, Aurelien Lucchi
Choice Bandits Arpit Agarwal, Nicholas Johnson, Shivani Agarwal
What if Neural Networks had SVDs? Alexander Mathiasen, Frederik Hvilshøj, Jakob Rødsgaard Jørgensen, Anshul Nasery, Davide Mottin
A Matrix Chernoff Bound for Markov Chains and Its Application to Co-occurrence Matrices Jiezhong Qiu, Chi Wang, Ben Liao, Richard Peng, Jie Tang
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Ensuring Fairness Beyond the Training Data Debmalya Mandal, Samuel Deng, Suman Jana, Jeannette Wing, Daniel J. Hsu
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Pre-training via Paraphrasing Mike Lewis, Marjan Ghazvininejad, Gargi Ghosh, Armen Aghajanyan, Sida Wang, Luke Zettlemoyer
GCN meets GPU: Decoupling “When to Sample” from “How to Sample” Morteza Ramezani, Weilin Cong, Mehrdad Mahdavi, Anand Sivasubramaniam, Mahmut Kandemir
Continual Learning of a Mixed Sequence of Similar and Dissimilar Tasks Zixuan Ke, Bing Liu, Xingchang Huang
All your loss are belong to Bayes Christian Walder, Richard Nock
HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks Zhen Dong, Zhewei Yao, Daiyaan Arfeen, Amir Gholami, Michael W. Mahoney, Kurt Keutzer
Sample-Efficient Reinforcement Learning of Undercomplete POMDPs Chi Jin, Sham Kakade, Akshay Krishnamurthy, Qinghua Liu
Non-Convex SGD Learns Halfspaces with Adversarial Label Noise Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis
A Tight Lower Bound and Efficient Reduction for Swap Regret Shinji Ito
DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction Aviral Kumar, Abhishek Gupta, Sergey Levine
OTLDA: A Geometry-aware Optimal Transport Approach for Topic Modeling Viet Huynh, He Zhao, Dinh Phung
Measuring Robustness to Natural Distribution Shifts in Image Classification Rohan Taori, Achal Dave, Vaishaal Shankar, Nicholas Carlini, Benjamin Recht, Ludwig Schmidt
Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data and Bayesian Inference Disi Ji, Padhraic Smyth, Mark Steyvers
RandAugment: Practical Automated Data Augmentation with a Reduced Search Space Ekin Dogus Cubuk, Barret Zoph, Jon Shlens, Quoc Le
Asymptotic normality and confidence intervals for derivatives of 2-layers neural network in the random features model Yiwei Shen, Pierre C Bellec
DisARM: An Antithetic Gradient Estimator for Binary Latent Variables Zhe Dong, Andriy Mnih, George Tucker
Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings Pantelis Elinas, Edwin V. Bonilla, Louis Tiao
Supervised Contrastive Learning Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, Dilip Krishnan
Learning Optimal Representations with the Decodable Information Bottleneck Yann Dubois, Douwe Kiela, David J. Schwab, Ramakrishna Vedantam
Meta-trained agents implement Bayes-optimal agents Vladimir Mikulik, Grégoire Delétang, Tom McGrath, Tim Genewein, Miljan Martic, Shane Legg, Pedro Ortega
Learning Agent Representations for Ice Hockey Guiliang Liu, Oliver Schulte, Pascal Poupart, Mike Rudd, Mehrsan Javan
Weak Form Generalized Hamiltonian Learning Kevin Course, Trefor Evans, Prasanth Nair
Neural Non-Rigid Tracking Aljaz Bozic, Pablo Palafox, Michael Zollhöfer, Angela Dai, Justus Thies, Matthias Niessner
Collegial Ensembles Etai Littwin, Ben Myara, Sima Sabah, Joshua Susskind, Shuangfei Zhai, Oren Golan
ICNet: Intra-saliency Correlation Network for Co-Saliency Detection Wen-Da Jin, Jun Xu, Ming-Ming Cheng, Yi Zhang, Wei Guo
Improved Variational Bayesian Phylogenetic Inference with Normalizing Flows Cheng Zhang
Deep Metric Learning with Spherical Embedding Dingyi Zhang, Yingming Li, Zhongfei Zhang
Preference-based Reinforcement Learning with Finite-Time Guarantees Yichong Xu, Ruosong Wang, Lin Yang, Aarti Singh, Artur Dubrawski
AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients Juntang Zhuang, Tommy Tang, Yifan Ding, Sekhar C. Tatikonda, Nicha Dvornek, Xenophon Papademetris, James Duncan
Interpretable Sequence Learning for Covid-19 Forecasting Sercan Arik, Chun-Liang Li, Jinsung Yoon, Rajarishi Sinha, Arkady Epshteyn, Long Le, Vikas Menon, Shashank Singh, Leyou Zhang, Martin Nikoltchev, Yash Sonthalia, Hootan Nakhost, Elli Kanal, Tomas Pfister
Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding Hongseok Namkoong, Ramtin Keramati, Steve Yadlowsky, Emma Brunskill
Modern Hopfield Networks and Attention for Immune Repertoire Classification Michael Widrich, Bernhard Schäfl, Milena Pavlović, Hubert Ramsauer, Lukas Gruber, Markus Holzleitner, Johannes Brandstetter, Geir Kjetil Sandve, Victor Greiff, Sepp Hochreiter, Günter Klambauer
One Ring to Rule Them All: Certifiably Robust Geometric Perception with Outliers Heng Yang, Luca Carlone
Task-Robust Model-Agnostic Meta-Learning Liam Collins, Aryan Mokhtari, Sanjay Shakkottai
R-learning in actor-critic model offers a biologically relevant mechanism for sequential decision-making Sergey Shuvaev, Sarah Starosta, Duda Kvitsiani, Adam Kepecs, Alexei Koulakov
Revisiting Frank-Wolfe for Polytopes: Strict Complementarity and Sparsity Dan Garber
Fast Convergence of Langevin Dynamics on Manifold: Geodesics meet Log-Sobolev Xiao Wang, Qi Lei, Ioannis Panageas
Tensor Completion Made Practical Allen Liu, Ankur Moitra
Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks Kenta Oono, Taiji Suzuki
Content Provider Dynamics and Coordination in Recommendation Ecosystems Omer Ben-Porat, Itay Rosenberg, Moshe Tennenholtz
Almost Surely Stable Deep Dynamics Nathan Lawrence, Philip Loewen, Michael Forbes, Johan Backstrom, Bhushan Gopaluni
Experimental design for MRI by greedy policy search Tim Bakker, Herke van Hoof, Max Welling
Expert-Supervised Reinforcement Learning for Offline Policy Learning and Evaluation Aaron Sonabend, Junwei Lu, Leo Anthony Celi, Tianxi Cai, Peter Szolovits
ColdGANs: Taming Language GANs with Cautious Sampling Strategies Thomas Scialom, Paul-Alexis Dray, Sylvain Lamprier, Benjamin Piwowarski, Jacopo Staiano
Hedging in games: Faster convergence of external and swap regrets Xi Chen, Binghui Peng
The Origins and Prevalence of Texture Bias in Convolutional Neural Networks Katherine Hermann, Ting Chen, Simon Kornblith
Time-Reversal Symmetric ODE Network In Huh, Eunho Yang, Sung Ju Hwang, Jinwoo Shin
Provable Overlapping Community Detection in Weighted Graphs Jimit Majmudar, Stephen Vavasis
Fast Unbalanced Optimal Transport on a Tree Ryoma Sato, Makoto Yamada, Hisashi Kashima
Acceleration with a Ball Optimization Oracle Yair Carmon, Arun Jambulapati, Qijia Jiang, Yujia Jin, Yin Tat Lee, Aaron Sidford, Kevin Tian
Avoiding Side Effects By Considering Future Tasks Victoria Krakovna, Laurent Orseau, Richard Ngo, Miljan Martic, Shane Legg
Handling Missing Data with Graph Representation Learning Jiaxuan You, Xiaobai Ma, Yi Ding, Mykel J. Kochenderfer, Jure Leskovec
Improving Auto-Augment via Augmentation-Wise Weight Sharing Keyu Tian, Chen Lin, Ming Sun, Luping Zhou, Junjie Yan, Wanli Ouyang
MMA Regularization: Decorrelating Weights of Neural Networks by Maximizing the Minimal Angles Zhennan Wang, Canqun Xiang, Wenbin Zou, Chen Xu
HRN: A Holistic Approach to One Class Learning Wenpeng Hu, Mengyu Wang, Qi Qin, Jinwen Ma, Bing Liu
The Generalized Lasso with Nonlinear Observations and Generative Priors Zhaoqiang Liu, Jonathan Scarlett
Fair regression via plug-in estimator and recalibration with statistical guarantees Evgenii Chzhen, Christophe Denis, Mohamed Hebiri, Luca Oneto, Massimiliano Pontil
Modeling Shared responses in Neuroimaging Studies through MultiView ICA Hugo Richard, Luigi Gresele, Aapo Hyvarinen, Bertrand Thirion, Alexandre Gramfort, Pierre Ablin
Efficient Planning in Large MDPs with Weak Linear Function Approximation Roshan Shariff, Csaba Szepesvari
Efficient Learning of Generative Models via Finite-Difference Score Matching Tianyu Pang, Taufik Xu, Chongxuan LI, Yang Song, Stefano Ermon, Jun Zhu
Semialgebraic Optimization for Lipschitz Constants of ReLU Networks Tong Chen, Jean B. Lasserre, Victor Magron, Edouard Pauwels
Linear-Sample Learning of Low-Rank Distributions Ayush Jain, Alon Orlitsky
Transferable Calibration with Lower Bias and Variance in Domain Adaptation Ximei Wang, Mingsheng Long, Jianmin Wang, Michael Jordan
Generalization bound of globally optimal non-convex neural network training: Transportation map estimation by infinite dimensional Langevin dynamics Taiji Suzuki
Online Bayesian Goal Inference for Boundedly Rational Planning Agents Tan Zhi-Xuan, Jordyn Mann, Tom Silver, Josh Tenenbaum, Vikash Mansinghka
BayReL: Bayesian Relational Learning for Multi-omics Data Integration Ehsan Hajiramezanali, Arman Hasanzadeh, Nick Duffield, Krishna Narayanan, Xiaoning Qian
Weakly Supervised Deep Functional Maps for Shape Matching Abhishek Sharma, Maks Ovsjanikov
Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift Remi Tachet des Combes, Han Zhao, Yu-Xiang Wang, Geoffrey J. Gordon
Rethinking the Value of Labels for Improving Class-Imbalanced Learning Yuzhe Yang, Zhi Xu
Provably Robust Metric Learning Lu Wang, Xuanqing Liu, Jinfeng Yi, Yuan Jiang, Cho-Jui Hsieh
Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings Yu Chen, Lingfei Wu, Mohammed Zaki
COPT: Coordinated Optimal Transport on Graphs Yihe Dong, Will Sawin
No Subclass Left Behind: Fine-Grained Robustness in Coarse-Grained Classification Problems Nimit Sohoni, Jared Dunnmon, Geoffrey Angus, Albert Gu, Christopher Ré
Model Rubik’s Cube: Twisting Resolution, Depth and Width for TinyNets Kai Han, Yunhe Wang, Qiulin Zhang, Wei Zhang, Chunjing XU, Tong Zhang
Self-Adaptive Training: beyond Empirical Risk Minimization Lang Huang, Chao Zhang, Hongyang Zhang
Effective Dimension Adaptive Sketching Methods for Faster Regularized Least-Squares Optimization Jonathan Lacotte, Mert Pilanci
Near-Optimal Comparison Based Clustering Michaël Perrot, Pascal Esser, Debarghya Ghoshdastidar
Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement Xin Liu, Josh Fromm, Shwetak Patel, Daniel McDuff
A new convergent variant of Q-learning with linear function approximation Diogo Carvalho, Francisco S. Melo, Pedro Santos
TaylorGAN: Neighbor-Augmented Policy Update Towards Sample-Efficient Natural Language Generation Chun-Hsing Lin, Siang-Ruei Wu, Hung-yi Lee, Yun-Nung Chen
Neural Networks with Small Weights and Depth-Separation Barriers Gal Vardi, Ohad Shamir
Untangling tradeoffs between recurrence and self-attention in artificial neural networks Giancarlo Kerg, Bhargav Kanuparthi, Anirudh Goyal ALIAS PARTH GOYAL, Kyle Goyette, Yoshua Bengio, Guillaume Lajoie
Dual-Free Stochastic Decentralized Optimization with Variance Reduction Hadrien Hendrikx, Francis Bach, Laurent Massoulié
Online Learning in Contextual Bandits using Gated Linear Networks Eren Sezener, Marcus Hutter, David Budden, Jianan Wang, Joel Veness
Throughput-Optimal Topology Design for Cross-Silo Federated Learning Othmane MARFOQ, CHUAN XU, Giovanni Neglia, Richard Vidal
Quantized Variational Inference Amir Dib
Asymptotically Optimal Exact Minibatch Metropolis-Hastings Ruqi Zhang, A. Feder Cooper, Christopher M. De Sa
Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search Linnan Wang, Rodrigo Fonseca, Yuandong Tian
Feature Shift Detection: Localizing Which Features Have Shifted via Conditional Distribution Tests Sean Kulinski, Saurabh Bagchi, David I. Inouye
Unifying Activation- and Timing-based Learning Rules for Spiking Neural Networks Jinseok Kim, Kyungsu Kim, Jae-Joon Kim
Space-Time Correspondence as a Contrastive Random Walk Allan Jabri, Andrew Owens, Alexei Efros
The Flajolet-Martin Sketch Itself Preserves Differential Privacy: Private Counting with Minimal Space Adam Smith, Shuang Song, Abhradeep Thakurta
Exponential ergodicity of mirror-Langevin diffusions Sinho Chewi, Thibaut Le Gouic, Chen Lu, Tyler Maunu, Philippe Rigollet, Austin Stromme
An Efficient Framework for Clustered Federated Learning Avishek Ghosh, Jichan Chung, Dong Yin, Kannan Ramchandran
Autoencoders that don’t overfit towards the Identity Harald Steck
Polynomial-Time Computation of Optimal Correlated Equilibria in Two-Player Extensive-Form Games with Public Chance Moves and Beyond Gabriele Farina, Tuomas Sandholm
Parameterized Explainer for Graph Neural Network Dongsheng Luo, Wei Cheng, Dongkuan Xu, Wenchao Yu, Bo Zong, Haifeng Chen, Xiang Zhang
Recursive Inference for Variational Autoencoders Minyoung Kim, Vladimir Pavlovic
Flexible mean field variational inference using mixtures of non-overlapping exponential families Jeffrey Spence
HYDRA: Pruning Adversarially Robust Neural Networks Vikash Sehwag, Shiqi Wang, Prateek Mittal, Suman Jana
NVAE: A Deep Hierarchical Variational Autoencoder Arash Vahdat, Jan Kautz
Can Temporal-Diﬀerence and Q-Learning Learn Representation? A Mean-Field Theory Yufeng Zhang, Qi Cai, Zhuoran Yang, Yongxin Chen, Zhaoran Wang
What Do Neural Networks Learn When Trained With Random Labels? Hartmut Maennel, Ibrahim M. Alabdulmohsin, Ilya O. Tolstikhin, Robert Baldock, Olivier Bousquet, Sylvain Gelly, Daniel Keysers
Counterfactual Prediction for Bundle Treatment Hao Zou, Peng Cui, Bo Li, Zheyan Shen, Jianxin Ma, Hongxia Yang, Yue He
Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs Hongyu Ren, Jure Leskovec
Learning Disentangled Representations and Group Structure of Dynamical Environments Robin Quessard, Thomas Barrett, William Clements
Learning Linear Programs from Optimal Decisions Yingcong Tan, Daria Terekhov, Andrew Delong
Wisdom of the Ensemble: Improving Consistency of Deep Learning Models Lijing Wang, Dipanjan Ghosh, Maria Gonzalez Diaz, Ahmed Farahat, Mahbubul Alam, Chetan Gupta, Jiangzhuo Chen, Madhav Marathe
Universal Function Approximation on Graphs Rickard Gabrielsson
Accelerating Reinforcement Learning through GPU Atari Emulation Steven Dalton, iuri frosio
EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning Jiachen Li, Fan Yang, Masayoshi Tomizuka, Chiho Choi
Comparator-Adaptive Convex Bandits Dirk van der Hoeven, Ashok Cutkosky, Haipeng Luo
Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs Jianzhun Du, Joseph Futoma, Finale Doshi-Velez
The Adaptive Complexity of Maximizing a Gross Substitutes Valuation Ron Kupfer, Sharon Qian, Eric Balkanski, Yaron Singer
A Robust Functional EM Algorithm for Incomplete Panel Count Data Alexander Moreno, Zhenke Wu, Jamie Roslyn Yap, Cho Lam, David Wetter, Inbal Nahum-Shani, Walter Dempsey, James M. Rehg
Graph Stochastic Neural Networks for Semi-supervised Learning Haibo Wang, Chuan Zhou, Xin Chen, Jia Wu, Shirui Pan, Jilong Wang
Compositional Zero-Shot Learning via Fine-Grained Dense Feature Composition Dat Huynh, Ehsan Elhamifar
A Benchmark for Systematic Generalization in Grounded Language Understanding Laura Ruis, Jacob Andreas, Marco Baroni, Diane Bouchacourt, Brenden M. Lake
Weston-Watkins Hinge Loss and Ordered Partitions Yutong Wang, Clayton Scott
Reinforcement Learning with Augmented Data Misha Laskin, Kimin Lee, Adam Stooke, Lerrel Pinto, Pieter Abbeel, Aravind Srinivas
Towards Minimax Optimal Reinforcement Learning in Factored Markov Decision Processes Yi Tian, Jian Qian, Suvrit Sra
Graduated Assignment for Joint Multi-Graph Matching and Clustering with Application to Unsupervised Graph Matching Network Learning Runzhong Wang, Junchi Yan, Xiaokang Yang
Estimating Training Data Influence by Tracing Gradient Descent Garima Pruthi, Frederick Liu, Satyen Kale, Mukund Sundararajan
Joint Policy Search for Multi-agent Collaboration with Imperfect Information Yuandong Tian, Qucheng Gong, Yu Jiang
Adversarial Bandits with Corruptions: Regret Lower Bound and No-regret Algorithm lin yang, Mohammad Hajiesmaili, Mohammad Sadegh Talebi, John C. S. Lui, Wing Shing Wong
Beta R-CNN: Looking into Pedestrian Detection from Another Perspective Zixuan Xu, Banghuai Li, Ye Yuan, Anhong Dang
Batch Normalization Biases Residual Blocks Towards the Identity Function in Deep Networks Soham De, Sam Smith
Learning Retrospective Knowledge with Reverse Reinforcement Learning Shangtong Zhang, Vivek Veeriah, Shimon Whiteson
Dialog without Dialog Data: Learning Visual Dialog Agents from VQA Data Michael Cogswell, Jiasen Lu, Rishabh Jain, Stefan Lee, Devi Parikh, Dhruv Batra
GCOMB: Learning Budget-constrained Combinatorial Algorithms over Billion-sized Graphs Sahil Manchanda, AKASH MITTAL, Anuj Dhawan, Sourav Medya, Sayan Ranu, Ambuj Singh
A General Large Neighborhood Search Framework for Solving Integer Linear Programs Jialin Song, ravi lanka, Yisong Yue, Bistra Dilkina
A Theoretical Framework for Target Propagation Alexander Meulemans, Francesco Carzaniga, Johan Suykens, João Sacramento, Benjamin F. Grewe
OrganITE: Optimal transplant donor organ offering using an individual treatment effect Jeroen Berrevoets, James Jordon, Ioana Bica, alexander gimson, Mihaela van der Schaar
The Complete Lasso Tradeoff Diagram Hua Wang, Yachong Yang, Zhiqi Bu, Weijie Su
On the universality of deep learning Emmanuel Abbe, Colin Sandon
Regression with reject option and application to kNN Ahmed Zaoui, Christophe Denis, Mohamed Hebiri
The Primal-Dual method for Learning Augmented Algorithms Etienne Bamas, Andreas Maggiori, Ola Svensson
FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs Alekh Agarwal, Sham Kakade, Akshay Krishnamurthy, Wen Sun
A Class of Algorithms for General Instrumental Variable Models Niki Kilbertus, Matt J. Kusner, Ricardo Silva
Black-Box Ripper: Copying black-box models using generative evolutionary algorithms Antonio Barbalau, Adrian Cosma, Radu Tudor Ionescu, Marius Popescu
Bayesian Optimization of Risk Measures Sait Cakmak, Raul Astudillo Marban, Peter Frazier, Enlu Zhou
TorsionNet: A Reinforcement Learning Approach to Sequential Conformer Search Tarun Gogineni, Ziping Xu, Exequiel Punzalan, Runxuan Jiang, Joshua Kammeraad, Ambuj Tewari, Paul Zimmerman
GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis Katja Schwarz, Yiyi Liao, Michael Niemeyer, Andreas Geiger
PIE-NET: Parametric Inference of Point Cloud Edges Xiaogang Wang, Yuelang Xu, Kai Xu, Andrea Tagliasacchi, Bin Zhou, Ali Mahdavi-Amiri, Hao Zhang
A Simple Language Model for Task-Oriented Dialogue Ehsan Hosseini-Asl, Bryan McCann, Chien-Sheng Wu, Semih Yavuz, Richard Socher
A Continuous-Time Mirror Descent Approach to Sparse Phase Retrieval Fan Wu, Patrick Rebeschini
Confidence sequences for sampling without replacement Ian Waudby-Smith, Aaditya Ramdas
A mean-field analysis of two-player zero-sum games Carles Domingo-Enrich, Samy Jelassi, Arthur Mensch, Grant Rotskoff, Joan Bruna
Leap-Of-Thought: Teaching Pre-Trained Models to Systematically Reason Over Implicit Knowledge Alon Talmor, Oyvind Tafjord, Peter Clark, Yoav Goldberg, Jonathan Berant
Pipeline PSRO: A Scalable Approach for Finding Approximate Nash Equilibria in Large Games Stephen Mcaleer, J.B. Lanier, Roy Fox, Pierre Baldi
Improving Sparse Vector Technique with Renyi Differential Privacy Yuqing Zhu, Yu-Xiang Wang
Latent Template Induction with Gumbel-CRFs Yao Fu, Chuanqi Tan, Bin Bi, Mosha Chen, Yansong Feng, Alexander Rush
Instance Based Approximations to Profile Maximum Likelihood Nima Anari, Moses Charikar, Kirankumar Shiragur, Aaron Sidford
Factorizable Graph Convolutional Networks Yiding Yang, Zunlei Feng, Mingli Song, Xinchao Wang
Guided Adversarial Attack for Evaluating and Enhancing Adversarial Defenses Gaurang Sriramanan, Sravanti Addepalli, Arya Baburaj, Venkatesh Babu R
A Study on Encodings for Neural Architecture Search Colin White, Willie Neiswanger, Sam Nolen, Yash Savani
Noise2Same: Optimizing A Self-Supervised Bound for Image Denoising Yaochen Xie, Zhengyang Wang, Shuiwang Ji
Early-Learning Regularization Prevents Memorization of Noisy Labels Sheng Liu, Jonathan Niles-Weed, Narges Razavian, Carlos Fernandez-Granda
LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-resolution and Beyond Wenbo Li, Kun Zhou, Lu Qi, Nianjuan Jiang, Jiangbo Lu, Jiaya Jia
Learning Parities with Neural Networks Amit Daniely, Eran Malach
Consistent Plug-in Classifiers for Complex Objectives and Constraints Shiv Kumar Tavker, Harish Guruprasad Ramaswamy, Harikrishna Narasimhan
Movement Pruning: Adaptive Sparsity by Fine-Tuning Victor Sanh, Thomas Wolf, Alexander Rush
Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot Jingtong Su, Yihang Chen, Tianle Cai, Tianhao Wu, Ruiqi Gao, Liwei Wang, Jason D. Lee
Online Matrix Completion with Side Information Mark Herbster, Stephen Pasteris, Lisa Tse
Position-based Scaled Gradient for Model Quantization and Pruning Jangho Kim, KiYoon Yoo, Nojun Kwak
Online Learning with Primary and Secondary Losses Avrim Blum, Han Shao
Graph Information Bottleneck Tailin Wu, Hongyu Ren, Pan Li, Jure Leskovec
The Complexity of Adversarially Robust Proper Learning of Halfspaces with Agnostic Noise Ilias Diakonikolas, Daniel M. Kane, Pasin Manurangsi
Adaptive Online Estimation of Piecewise Polynomial Trends Dheeraj Baby, Yu-Xiang Wang
RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference Oindrila Saha, Aditya Kusupati, Harsha Vardhan Simhadri, Manik Varma, Prateek Jain
Agnostic Learning with Multiple Objectives Corinna Cortes, Mehryar Mohri, Javier Gonzalvo, Dmitry Storcheus
3D Multi-bodies: Fitting Sets of Plausible 3D Human Models to Ambiguous Image Data Benjamin Biggs, David Novotny, Sebastien Ehrhardt, Hanbyul Joo, Ben Graham, Andrea Vedaldi
Auto-Panoptic: Cooperative Multi-Component Architecture Search for Panoptic Segmentation Yangxin Wu, Gengwei Zhang, Hang Xu, Xiaodan Liang, Liang Lin
Differentiable Top-k with Optimal Transport Yujia Xie, Hanjun Dai, Minshuo Chen, Bo Dai, Tuo Zhao, Hongyuan Zha, Wei Wei, Tomas Pfister
Information-theoretic Task Selection for Meta-Reinforcement Learning Ricardo Luna Gutierrez, Matteo Leonetti
A Limitation of the PAC-Bayes Framework Roi Livni, Shay Moran
On Completeness-aware Concept-Based Explanations in Deep Neural Networks Chih-Kuan Yeh, Been Kim, Sercan Arik, Chun-Liang Li, Tomas Pfister, Pradeep Ravikumar
Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problems Luo Luo, Haishan Ye, Zhichao Huang, Tong Zhang
Why Normalizing Flows Fail to Detect Out-of-Distribution Data Polina Kirichenko, Pavel Izmailov, Andrew G. Wilson
Explaining Naive Bayes and Other Linear Classifiers with Polynomial Time and Delay Joao Marques-Silva, Thomas Gerspacher, Martin Cooper, Alexey Ignatiev, Nina Narodytska
Unsupervised Translation of Programming Languages Baptiste Roziere, Marie-Anne Lachaux, Lowik Chanussot, Guillaume Lample
Adversarial Style Mining for One-Shot Unsupervised Domain Adaptation Yawei Luo, Ping Liu, Tao Guan, Junqing Yu, Yi Yang
Optimally Deceiving a Learning Leader in Stackelberg Games Georgios Birmpas, Jiarui Gan, Alexandros Hollender, Francisco Marmolejo, Ninad Rajgopal, Alexandros Voudouris
Online Optimization with Memory and Competitive Control Guanya Shi, Yiheng Lin, Soon-Jo Chung, Yisong Yue, Adam Wierman
IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method Yossi Arjevani, Joan Bruna, Bugra Can, Mert Gurbuzbalaban, Stefanie Jegelka, Hongzhou Lin
Evolving Graphical Planner: Contextual Global Planning for Vision-and-Language Navigation Zhiwei Deng, Karthik Narasimhan, Olga Russakovsky
Learning from Failure: De-biasing Classifier from Biased Classifier Junhyun Nam, Hyuntak Cha, Sung-Soo Ahn, Jaeho Lee, Jinwoo Shin
Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder Zhisheng Xiao, Qing Yan, Yali Amit
Deep Diffusion-Invariant Wasserstein Distributional Classification Sung Woo Park+, Dong Wook Shu, Junseok Kwon
Finding All $ϵ$ -Good Arms in Stochastic Bandits Blake Mason, Lalit Jain, Ardhendu Tripathy, Robert Nowak
Meta-Learning through Hebbian Plasticity in Random Networks Elias Najarro, Sebastian Risi
A Computational Separation between Private Learning and Online Learning Mark Bun
Top-KAST: Top-K Always Sparse Training Siddhant Jayakumar, Razvan Pascanu, Jack Rae, Simon Osindero, Erich Elsen
Meta-Learning with Adaptive Hyperparameters Sungyong Baik, Myungsub Choi, Janghoon Choi, Heewon Kim, Kyoung Mu Lee
Tight last-iterate convergence rates for no-regret learning in multi-player games Noah Golowich, Sarath Pattathil, Constantinos Daskalakis
Curvature Regularization to Prevent Distortion in Graph Embedding Hongbin Pei, Bingzhe Wei, Kevin Chang, Chunxu Zhang, Bo Yang
Perturbing Across the Feature Hierarchy to Improve Standard and Strict Blackbox Attack Transferability Nathan Inkawhich, Kevin Liang, Binghui Wang, Matthew Inkawhich, Lawrence Carin, Yiran Chen
Statistical and Topological Properties of Sliced Probability Divergences Kimia Nadjahi, Alain Durmus, Lénaïc Chizat, Soheil Kolouri, Shahin Shahrampour, Umut Simsekli
Probabilistic Active Meta-Learning Jean Kaddour, Steindor Saemundsson, Marc Deisenroth
Knowledge Distillation in Wide Neural Networks: Risk Bound, Data Efficiency and Imperfect Teacher Guangda Ji, Zhanxing Zhu
Adversarial Attacks on Deep Graph Matching Zijie Zhang, Zeru Zhang, Yang Zhou, Yelong Shen, Ruoming Jin, Dejing Dou
The Generalization-Stability Tradeoff In Neural Network Pruning Brian Bartoldson, Ari Morcos, Adrian Barbu, Gordon Erlebacher
Gradient-EM Bayesian Meta-Learning Yayi Zou, Xiaoqi Lu
Logarithmic Regret Bound in Partially Observable Linear Dynamical Systems Ali Sahin Lale, Kamyar Azizzadenesheli, Babak Hassibi, Anima Anandkumar
Linearly Converging Error Compensated SGD Eduard Gorbunov, Dmitry Kovalev, Dmitry Makarenko, Peter Richtarik
Canonical 3D Deformer Maps: Unifying parametric and non-parametric methods for dense weakly-supervised category reconstruction David Novotny, Roman Shapovalov, Andrea Vedaldi
A Self-Tuning Actor-Critic Algorithm Tom Zahavy, Zhongwen Xu, Vivek Veeriah, Matteo Hessel, Junhyuk Oh, Hado P. van Hasselt, David Silver, Satinder Singh
The Cone of Silence: Speech Separation by Localization Teerapat Jenrungrot, Vivek Jayaram, Steve Seitz, Ira Kemelmacher-Shlizerman
High-Dimensional Bayesian Optimization via Nested Riemannian Manifolds Noémie Jaquier, Leonel Rozo
Train-by-Reconnect: Decoupling Locations of Weights from Their Values Yushi Qiu, Reiji Suda
Learning discrete distributions: user vs item-level privacy Yuhan Liu, Ananda Theertha Suresh, Felix Xinnan X. Yu, Sanjiv Kumar, Michael Riley
Matrix Completion with Quantified Uncertainty through Low Rank Gaussian Copula Yuxuan Zhao, Madeleine Udell
Sparse and Continuous Attention Mechanisms André Martins, António Farinhas, Marcos Treviso, Vlad Niculae, Pedro Aguiar, Mario Figueiredo
Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection Xiang Li, Wenhai Wang, Lijun Wu, Shuo Chen, Xiaolin Hu, Jun Li, Jinhui Tang, Jian Yang
Learning by Minimizing the Sum of Ranked Range Shu Hu, Yiming Ying, xin wang, Siwei Lyu
Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations Huan Zhang, Hongge Chen, Chaowei Xiao, Bo Li, mingyan liu, Duane Boning, Cho-Jui Hsieh
Understanding Anomaly Detection with Deep Invertible Networks through Hierarchies of Distributions and Features Robin Schirrmeister, Yuxuan Zhou, Tonio Ball, Dan Zhang
Fair Hierarchical Clustering Sara Ahmadian, Alessandro Epasto, Marina Knittel, Ravi Kumar, Mohammad Mahdian, Benjamin Moseley, Philip Pham, Sergei Vassilvitskii, Yuyan Wang
Self-training Avoids Using Spurious Features Under Domain Shift Yining Chen, Colin Wei, Ananya Kumar, Tengyu Ma
Improving Online Rent-or-Buy Algorithms with Sequential Decision Making and ML Predictions Soumya Banerjee
CircleGAN: Generative Adversarial Learning across Spherical Circles Woohyeon Shim, Minsu Cho
WOR and $p$ ‘s: Sketches for $ℓ_{p}$ -Sampling Without Replacement Edith Cohen, Rasmus Pagh, David Woodruff
Hypersolvers: Toward Fast Continuous-Depth Models Michael Poli, Stefano Massaroli, Atsushi Yamashita, Hajime Asama, Jinkyoo Park
Log-Likelihood Ratio Minimizing Flows: Towards Robust and Quantifiable Neural Distribution Alignment Ben Usman, Avneesh Sud, Nick Dufour, Kate Saenko
Escaping the Gravitational Pull of Softmax Jincheng Mei, Chenjun Xiao, Bo Dai, Lihong Li, Csaba Szepesvari, Dale Schuurmans
Regret in Online Recommendation Systems Kaito Ariu, Narae Ryu, Se-Young Yun, Alexandre Proutiere
On Convergence and Generalization of Dropout Training Poorya Mianjy, Raman Arora
Second Order Optimality in Decentralized Non-Convex Optimization via Perturbed Gradient Tracking Isidoros Tziotis, Constantine Caramanis, Aryan Mokhtari
Implicit Regularization in Deep Learning May Not Be Explainable by Norms Noam Razin, Nadav Cohen
POMO: Policy Optimization with Multiple Optima for Reinforcement Learning Yeong-Dae Kwon, Jinho Choo, Byoungjip Kim, Iljoo Yoon, Youngjune Gwon, Seungjai Min
Uncertainty-aware Self-training for Few-shot Text Classification Subhabrata Mukherjee, Ahmed Awadallah
Learning to Learn with Feedback and Local Plasticity Jack Lindsey, Ashok Litwin-Kumar
Every View Counts: Cross-View Consistency in 3D Object Detection with Hybrid-Cylindrical-Spherical Voxelization Qi Chen, Lin Sun, Ernest Cheung, Alan L. Yuille
Sharper Generalization Bounds for Pairwise Learning Yunwen Lei, Antoine Ledent, Marius Kloft
A Measure-Theoretic Approach to Kernel Conditional Mean Embeddings Junhyung Park, Krikamol Muandet
Quantifying the Empirical Wasserstein Distance to a Set of Measures: Beating the Curse of Dimensionality Nian Si, Jose Blanchet, Soumyadip Ghosh, Mark Squillante
Bootstrap Your Own Latent – A New Approach to Self-Supervised Learning Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, Bilal Piot, koray kavukcuoglu, Remi Munos, Michal Valko
Towards Theoretically Understanding Why Sgd Generalizes Better Than Adam in Deep Learning Pan Zhou, Jiashi Feng, Chao Ma, Caiming Xiong, Steven Chu Hong Hoi, Weinan E
RSKDD-Net: Random Sample-based Keypoint Detector and Descriptor Fan Lu, Guang Chen, Yinlong Liu, Zhongnan Qu, Alois Knoll
Efficient Clustering for Stretched Mixtures: Landscape and Optimality Kaizheng Wang, Yuling Yan, Mateo Diaz
A Group-Theoretic Framework for Data Augmentation Shuxiao Chen, Edgar Dobriban, Jane Lee
The Statistical Cost of Robust Kernel Hyperparameter Turning Raphael Meyer, Christopher Musco
How does Weight Correlation Affect Generalisation Ability of Deep Neural Networks? Gaojie Jin, Xinping Yi, Liang Zhang, Lijun Zhang, Sven Schewe, Xiaowei Huang
ContraGAN: Contrastive Learning for Conditional Image Generation Minguk Kang, Jaesik Park
On the distance between two neural networks and the stability of learning Jeremy Bernstein, Arash Vahdat, Yisong Yue, Ming-Yu Liu
A Topological Filter for Learning with Label Noise Pengxiang Wu, Songzhu Zheng, Mayank Goswami, Dimitris Metaxas, Chao Chen
Personalized Federated Learning with Moreau Envelopes Canh T. Dinh, Nguyen Tran, Tuan Dung Nguyen
Avoiding Side Effects in Complex Environments Alex Turner, Neale Ratzlaff, Prasad Tadepalli
No-regret Learning in Price Competitions under Consumer Reference Effects Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang
Geometric Dataset Distances via Optimal Transport David Alvarez Melis, Nicolo Fusi
Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters Sulin Liu, Xingyuan Sun, Peter J. Ramadge, Ryan P. Adams
A novel variational form of the Schatten- $p$ quasi-norm Paris Giampouras, Rene Vidal, Athanasios Rontogiannis, Benjamin Haeffele
Energy-based Out-of-distribution Detection Weitang Liu, Xiaoyun Wang, John Owens, Yixuan Li
On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them Chen Liu, Mathieu Salzmann, Tao Lin, Ryota Tomioka, Sabine Süsstrunk
User-Dependent Neural Sequence Models for Continuous-Time Event Data Alex Boyd, Robert Bamler, Stephan Mandt, Padhraic Smyth
Active Structure Learning of Causal DAGs via Directed Clique Trees Chandler Squires, Sara Magliacane, Kristjan Greenewald, Dmitriy Katz, Murat Kocaoglu, Karthikeyan Shanmugam
Convergence and Stability of Graph Convolutional Networks on Large Random Graphs Nicolas Keriven, Alberto Bietti, Samuel Vaiter
BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization Maximilian Balandat, Brian Karrer, Daniel Jiang, Samuel Daulton, Ben Letham, Andrew G. Wilson, Eytan Bakshy
Reconsidering Generative Objectives For Counterfactual Reasoning Danni Lu, Chenyang Tao, Junya Chen, Fan Li, Feng Guo, Lawrence Carin
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Quantile Propagation for Wasserstein-Approximate Gaussian Processes Rui Zhang, Christian Walder, Edwin V. Bonilla, Marian-Andrei Rizoiu, Lexing Xie
Generating Adjacency-Constrained Subgoals in Hierarchical Reinforcement Learning Tianren Zhang, Shangqi Guo, Tian Tan, Xiaolin Hu, Feng Chen
High-contrast “gaudy” images improve the training of deep neural network models of visual cortex Benjamin Cowley, Jonathan W. Pillow
Duality-Induced Regularizer for Tensor Factorization Based Knowledge Graph Completion Zhanqiu Zhang, Jianyu Cai, Jie Wang
Distributed Training with Heterogeneous Data: Bridging Median- and Mean-Based Algorithms Xiangyi Chen, Tiancong Chen, Haoran Sun, Steven Z. Wu, Mingyi Hong
H-Mem: Harnessing synaptic plasticity with Hebbian Memory Networks Thomas Limbacher, Robert Legenstein
Neural Unsigned Distance Fields for Implicit Function Learning Julian Chibane, Mohamad Aymen mir, Gerard Pons-Moll
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Fast Transformers with Clustered Attention Apoorv Vyas, Angelos Katharopoulos, François Fleuret
The Convex Relaxation Barrier, Revisited: Tightened Single-Neuron Relaxations for Neural Network Verification Christian Tjandraatmadja, Ross Anderson, Joey Huchette, Will Ma, KRUNAL KISHOR PATEL, Juan Pablo Vielma
Strongly Incremental Constituency Parsing with Graph Neural Networks Kaiyu Yang, Jia Deng
AOT: Appearance Optimal Transport Based Identity Swapping for Forgery Detection Hao Zhu, Chaoyou Fu, Qianyi Wu, Wayne Wu, Chen Qian, Ran He
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Minimax Optimal Nonparametric Estimation of Heterogeneous Treatment Effects Zijun Gao, Yanjun Han
Residual Force Control for Agile Human Behavior Imitation and Extended Motion Synthesis Ye Yuan, Kris Kitani
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Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning Zhongzheng Ren, Raymond Yeh, Alexander Schwing
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Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings Christopher Morris, Gaurav Rattan, Petra Mutzel
Adversarial Crowdsourcing Through Robust Rank-One Matrix Completion Qianqian Ma, Alex Olshevsky
Learning Semantic-aware Normalization for Generative Adversarial Networks Heliang Zheng, Jianlong Fu, Yanhong Zeng, Jiebo Luo, Zheng-Jun Zha
Differentiable Causal Discovery from Interventional Data Philippe Brouillard, Sébastien Lachapelle, Alexandre Lacoste, Simon Lacoste-Julien, Alexandre Drouin
One-sample Guided Object Representation Disassembling Zunlei Feng, Yongming He, Xinchao Wang, Xin Gao, Jie Lei, Cheng Jin, Mingli Song
Extrapolation Towards Imaginary 0-Nearest Neighbour and Its Improved Convergence Rate Akifumi Okuno, Hidetoshi Shimodaira
Robust Persistence Diagrams using Reproducing Kernels Siddharth Vishwanath, Kenji Fukumizu, Satoshi Kuriki, Bharath K. Sriperumbudur
Contextual Games: Multi-Agent Learning with Side Information Pier Giuseppe Sessa, Ilija Bogunovic, Andreas Krause, Maryam Kamgarpour
Goal-directed Generation of Discrete Structures with Conditional Generative Models Amina Mollaysa, Brooks Paige, Alexandros Kalousis
Beyond Lazy Training for Over-parameterized Tensor Decomposition Xiang Wang, Chenwei Wu, Jason D. Lee, Tengyu Ma, Rong Ge
Denoised Smoothing: A Provable Defense for Pretrained Classifiers Hadi Salman, Mingjie Sun, Greg Yang, Ashish Kapoor, J. Zico Kolter
Minibatch Stochastic Approximate Proximal Point Methods Hilal Asi, Karan Chadha, Gary Cheng, John C. Duchi
Attribute Prototype Network for Zero-Shot Learning Wenjia Xu, Yongqin Xian, Jiuniu Wang, Bernt Schiele, Zeynep Akata
CrossTransformers: spatially-aware few-shot transfer Carl Doersch, Ankush Gupta, Andrew Zisserman
Learning Latent Space Energy-Based Prior Model Bo Pang, Tian Han, Erik Nijkamp, Song-Chun Zhu, Ying Nian Wu
SEVIR : A Storm Event Imagery Dataset for Deep Learning Applications in Radar and Satellite Meteorology Mark Veillette, Siddharth Samsi, Chris Mattioli
Lightweight Generative Adversarial Networks for Text-Guided Image Manipulation Bowen Li, Xiaojuan Qi, Philip Torr, Thomas Lukasiewicz
High-Dimensional Contextual Policy Search with Unknown Context Rewards using Bayesian Optimization Qing Feng , Ben Letham, Hongzi Mao, Eytan Bakshy
Model Fusion via Optimal Transport Sidak Pal Singh, Martin Jaggi
On the Stability and Convergence of Robust Adversarial Reinforcement Learning: A Case Study on Linear Quadratic Systems Kaiqing Zhang, Bin Hu, Tamer Basar
Learning Individually Inferred Communication for Multi-Agent Cooperation Ziluo Ding, Tiejun Huang, Zongqing Lu
Set2Graph: Learning Graphs From Sets Hadar Serviansky, Nimrod Segol, Jonathan Shlomi, Kyle Cranmer, Eilam Gross, Haggai Maron, Yaron Lipman
Graph Random Neural Networks for Semi-Supervised Learning on Graphs Wenzheng Feng, Jie Zhang, Yuxiao Dong, Yu Han, Huanbo Luan, Qian Xu, Qiang Yang, Evgeny Kharlamov, Jie Tang
Gradient Boosted Normalizing Flows Robert Giaquinto, Arindam Banerjee
Open Graph Benchmark: Datasets for Machine Learning on Graphs Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, Jure Leskovec
Towards Understanding Hierarchical Learning: Benefits of Neural Representations Minshuo Chen, Yu Bai, Jason D. Lee, Tuo Zhao, Huan Wang, Caiming Xiong, Richard Socher
Texture Interpolation for Probing Visual Perception Jonathan Vacher, Aida Davila, Adam Kohn, Ruben Coen-Cagli
Hierarchical Neural Architecture Search for Deep Stereo Matching Xuelian Cheng, Yiran Zhong, Mehrtash Harandi, Yuchao Dai, Xiaojun Chang, Hongdong Li, Tom Drummond, Zongyuan Ge
MuSCLE: Multi Sweep Compression of LiDAR using Deep Entropy Models Sourav Biswas, Jerry Liu, Kelvin Wong, Shenlong Wang, Raquel Urtasun
Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy Edward Moroshko, Blake E. Woodworth, Suriya Gunasekar, Jason D. Lee, Nati Srebro, Daniel Soudry
Focus of Attention Improves Information Transfer in Visual Features Matteo Tiezzi, Stefano Melacci, Alessandro Betti, Marco Maggini, Marco Gori
Auditing Differentially Private Machine Learning: How Private is Private SGD? Matthew Jagielski, Jonathan Ullman, Alina Oprea
A Dynamical Central Limit Theorem for Shallow Neural Networks Zhengdao Chen, Grant Rotskoff, Joan Bruna, Eric Vanden-Eijnden
Measuring Systematic Generalization in Neural Proof Generation with Transformers Nicolas Gontier, Koustuv Sinha, Siva Reddy, Chris Pal
Big Self-Supervised Models are Strong Semi-Supervised Learners Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, Geoffrey E. Hinton
Learning from Label Proportions: A Mutual Contamination Framework Clayton Scott, Jianxin Zhang
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization Geoff Pleiss, Martin Jankowiak, David Eriksson, Anil Damle, Jacob Gardner
Self-Adaptively Learning to Demoiré from Focused and Defocused Image Pairs Lin Liu, Shanxin Yuan, Jianzhuang Liu, Liping Bao, Gregory Slabaugh, Qi Tian
Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning Nathan Kallus, Angela Zhou
Model Class Reliance for Random Forests Gavin Smith, Roberto Mansilla, James Goulding
Follow the Perturbed Leader: Optimism and Fast Parallel Algorithms for Smooth Minimax Games Arun Suggala, Praneeth Netrapalli
Agnostic $Q$ -learning with Function Approximation in Deterministic Systems: Near-Optimal Bounds on Approximation Error and Sample Complexity Simon S. Du, Jason D. Lee, Gaurav Mahajan, Ruosong Wang
Learning to Adapt to Evolving Domains Hong Liu, Mingsheng Long, Jianmin Wang, Yu Wang
Synthesizing Tasks for Block-based Programming Umair Ahmed, Maria Christakis, Aleksandr Efremov, Nigel Fernandez, Ahana Ghosh, Abhik Roychoudhury, Adish Singla
Scalable Belief Propagation via Relaxed Scheduling Vitalii Aksenov, Dan Alistarh, Janne H. Korhonen
Firefly Neural Architecture Descent: a General Approach for Growing Neural Networks Lemeng Wu, Bo Liu, Peter Stone, Qiang Liu
Risk-Sensitive Reinforcement Learning: Near-Optimal Risk-Sample Tradeoff in Regret Yingjie Fei, Zhuoran Yang, Yudong Chen, Zhaoran Wang, Qiaomin Xie
Learning to Decode: Reinforcement Learning for Decoding of Sparse Graph-Based Channel Codes Salman Habib, Allison Beemer, Joerg Kliewer
Faster DBSCAN via subsampled similarity queries Heinrich Jiang, Jennifer Jang, Jakub Lacki
De-Anonymizing Text by Fingerprinting Language Generation Zhen Sun, Roei Schuster, Vitaly Shmatikov
Multiparameter Persistence Image for Topological Machine Learning Mathieu Carrière, Andrew Blumberg
PLANS: Neuro-Symbolic Program Learning from Videos Raphaël Dang-Nhu
Matrix Inference and Estimation in Multi-Layer Models Parthe Pandit, Mojtaba Sahraee Ardakan, Sundeep Rangan, Philip Schniter, Alyson K. Fletcher
MeshSDF: Differentiable Iso-Surface Extraction Edoardo Remelli, Artem Lukoianov, Stephan Richter, Benoit Guillard, Timur Bagautdinov, Pierre Baque, Pascal Fua
Variational Interaction Information Maximization for Cross-domain Disentanglement HyeongJoo Hwang, Geon-Hyeong Kim, Seunghoon Hong, Kee-Eung Kim
Provably Efficient Exploration for Reinforcement Learning Using Unsupervised Learning Fei Feng, Ruosong Wang, Wotao Yin, Simon S. Du, Lin Yang
Faithful Embeddings for Knowledge Base Queries Haitian Sun, Andrew Arnold, Tania Bedrax Weiss, Fernando Pereira, William W. Cohen
Wasserstein Distances for Stereo Disparity Estimation Divyansh Garg, Yan Wang, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger, Wei-Lun Chao
Multi-agent Trajectory Prediction with Fuzzy Query Attention Nitin Kamra, Hao Zhu, Dweep Kumarbhai Trivedi, Ming Zhang, Yan Liu
Multilabel Classification by Hierarchical Partitioning and Data-dependent Grouping Shashanka Ubaru, Sanjeeb Dash, Arya Mazumdar, Oktay Gunluk
An Analysis of SVD for Deep Rotation Estimation Jake Levinson, Carlos Esteves, Kefan Chen, Noah Snavely, Angjoo Kanazawa, Afshin Rostamizadeh, Ameesh Makadia
Can the Brain Do Backpropagation? — Exact Implementation of Backpropagation in Predictive Coding Networks Yuhang Song, Thomas Lukasiewicz, Zhenghua Xu, Rafal Bogacz
Manifold GPLVMs for discovering non-Euclidean latent structure in neural data Kristopher Jensen, Ta-Chu Kao, Marco Tripodi, Guillaume Hennequin
Distributed Distillation for On-Device Learning Ilai Bistritz, Ariana Mann, Nicholas Bambos
COOT: Cooperative Hierarchical Transformer for Video-Text Representation Learning Simon Ging, Mohammadreza Zolfaghari, Hamed Pirsiavash, Thomas Brox
Passport-aware Normalization for Deep Model Protection Jie Zhang, Dongdong Chen, Jing Liao, Weiming Zhang, Gang Hua, Nenghai Yu
Sampling-Decomposable Generative Adversarial Recommender Binbin Jin, Defu Lian, Zheng Liu, Qi Liu, Jianhui Ma, Xing Xie, Enhong Chen
Limits to Depth Efficiencies of Self-Attention Yoav Levine, Noam Wies, Or Sharir, Hofit Bata, Amnon Shashua

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