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ORCID34th NeurIPS 2020
- Hugo Larochelle, Marc'Aurelio Ranzato, Raia Hadsell, Maria-Florina Balcan, Hsuan-Tien Lin:
Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual. 2020 - Seongmin Ok:
A graph similarity for deep learning. - Sangnie Bhardwaj, Ian Fischer, Johannes Ballé, Troy T. Chinen:
An Unsupervised Information-Theoretic Perceptual Quality Metric. - Jean-Baptiste Alayrac, Adrià Recasens, Rosalia Schneider, Relja Arandjelovic, Jason Ramapuram, Jeffrey De Fauw, Lucas Smaira, Sander Dieleman, Andrew Zisserman:
Self-Supervised MultiModal Versatile Networks. - Simiao Ren, Willie Padilla, Jordan M. Malof:
Benchmarking Deep Inverse Models over time, and the Neural-Adjoint method. - Masatoshi Uehara, Masahiro Kato, Shota Yasui:
Off-Policy Evaluation and Learning for External Validity under a Covariate Shift. - Yao-Hung Hubert Tsai, Han Zhao, Makoto Yamada, Louis-Philippe Morency, Ruslan Salakhutdinov:
Neural Methods for Point-wise Dependency Estimation. - Oleksandr Shchur, Nicholas Gao, Marin Bilos, Stephan Günnemann:
Fast and Flexible Temporal Point Processes with Triangular Maps. - Yiwen Guo, Qizhang Li, Hao Chen:
Backpropagating Linearly Improves Transferability of Adversarial Examples. - Daiyi Peng, Xuanyi Dong, Esteban Real, Mingxing Tan, Yifeng Lu, Gabriel Bender, Hanxiao Liu, Adam Kraft, Chen Liang, Quoc Le:
PyGlove: Symbolic Programming for Automated Machine Learning. - Tamás Erdélyi, Cameron Musco, Christopher Musco:
Fourier Sparse Leverage Scores and Approximate Kernel Learning. - Nicholas J. A. Harvey, Christopher Liaw, Tasuku Soma:
Improved Algorithms for Online Submodular Maximization via First-order Regret Bounds. - Alexandre Lacoste, Pau Rodríguez López, Frederic Branchaud-Charron, Parmida Atighehchian, Massimo Caccia, Issam Hadj Laradji, Alexandre Drouin, Matt Craddock, Laurent Charlin, David Vázquez:
Synbols: Probing Learning Algorithms with Synthetic Datasets. - Avinatan Hassidim, Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer:
Adversarially Robust Streaming Algorithms via Differential Privacy. - Long Chen, Yuan Yao, Feng Xu, Miao Xu, Hanghang Tong:
Trading Personalization for Accuracy: Data Debugging in Collaborative Filtering. - Yuntian Deng, Alexander M. Rush
:
Cascaded Text Generation with Markov Transformers. - Shib Sankar Dasgupta, Michael Boratko, Dongxu Zhang, Luke Vilnis, Xiang Li, Andrew McCallum:
Improving Local Identifiability in Probabilistic Box Embeddings. - Ryan McKenna, Daniel Sheldon:
Permute-and-Flip: A new mechanism for differentially private selection. - William Gilpin:
Deep reconstruction of strange attractors from time series. - Shengxi Li, Zeyang Yu, Min Xiang, Danilo P. Mandic:
Reciprocal Adversarial Learning via Characteristic Functions. - Jiexin Duan, Xingye Qiao, Guang Cheng:
Statistical Guarantees of Distributed Nearest Neighbor Classification. - Mao Ye, Tongzheng Ren, Qiang Liu:
Stein Self-Repulsive Dynamics: Benefits From Past Samples. - Tomas Vaskevicius, Varun Kanade, Patrick Rebeschini:
The Statistical Complexity of Early-Stopped Mirror Descent. - Amir-Hossein Karimi, Bodo Julius von Kügelgen, Bernhard Schölkopf, Isabel Valera:
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach. - Valentin De Bortoli, Alain Durmus, Xavier Fontaine, Umut Simsekli:
Quantitative Propagation of Chaos for SGD in Wide Neural Networks. - Cheng Zhang, Kun Zhang, Yingzhen Li:
A Causal View on Robustness of Neural Networks. - Santiago Mazuelas, Andrea Zanoni, Aritz Pérez:
Minimax Classification with 0-1 Loss and Performance Guarantees. - Pierluca D'Oro, Wojciech Jaskowski:
How to Learn a Useful Critic? Model-based Action-Gradient-Estimator Policy Optimization. - Lingxiao Huang, K. Sudhir, Nisheeth K. Vishnoi:
Coresets for Regressions with Panel Data. - Alex Beatson, Jordan T. Ash, Geoffrey Roeder, Tianju Xue, Ryan P. Adams:
Learning Composable Energy Surrogates for PDE Order Reduction. - Maryam Majzoubi, Chicheng Zhang, Rajan Chari, Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins:
Efficient Contextual Bandits with Continuous Actions. - Yaniv Romano, Stephen Bates, Emmanuel J. Candès:
Achieving Equalized Odds by Resampling Sensitive Attributes. - Wenhao Luo, Wen Sun, Ashish Kapoor:
Multi-Robot Collision Avoidance under Uncertainty with Probabilistic Safety Barrier Certificates. - Pierre-Cyril Aubin-Frankowski, Zoltán Szabó:
Hard Shape-Constrained Kernel Machines. - Jiaxin Chen, Xiao-Ming Wu, Yanke Li, Qimai Li, Li-Ming Zhan, Fu-Lai Chung:
A Closer Look at the Training Strategy for Modern Meta-Learning. - Damien Teney, Ehsan Abbasnejad, Kushal Kafle, Robik Shrestha, Christopher Kanan, Anton van den Hengel:
On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law. - Ayman Boustati, Ömer Deniz Akyildiz, Theodoros Damoulas, Adam M. Johansen:
Generalised Bayesian Filtering via Sequential Monte Carlo. - Kai Han, Zongmai Cao, Shuang Cui, Benwei Wu:
Deterministic Approximation for Submodular Maximization over a Matroid in Nearly Linear Time. - Johann Brehmer, Kyle Cranmer:
Flows for simultaneous manifold learning and density estimation. - Austin Xu, Mark A. Davenport:
Simultaneous Preference and Metric Learning from Paired Comparisons. - Jincheng Bai, Qifan Song, Guang Cheng:
Efficient Variational Inference for Sparse Deep Learning with Theoretical Guarantee. - Yufan Zhou, Changyou Chen, Jinhui Xu:
Learning Manifold Implicitly via Explicit Heat-Kernel Learning. - Chaojie Wang, Hao Zhang, Bo Chen, Dongsheng Wang, Zhengjue Wang, Mingyuan Zhou:
Deep Relational Topic Modeling via Graph Poisson Gamma Belief Network. - Hengtong Hu, Lingxi Xie, Zewei Du, Richang Hong, Qi Tian:
One-bit Supervision for Image Classification. - Behnam Neyshabur, Hanie Sedghi, Chiyuan Zhang:
What is being transferred in transfer learning? - Ashwinkumar Badanidiyuru, Amin Karbasi, Ehsan Kazemi, Jan Vondrák:
Submodular Maximization Through Barrier Functions. - Yujia Huang, James Gornet, Sihui Dai, Zhiding Yu, Tan M. Nguyen, Doris Y. Tsao, Anima Anandkumar:
Neural Networks with Recurrent Generative Feedback. - Jinheon Baek, Dong Bok Lee, Sung Ju Hwang:
Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction. - Kaustav Kundu, Joseph Tighe:
Exploiting weakly supervised visual patterns to learn from partial annotations. - Yibo Yang, Robert Bamler, Stephan Mandt:
Improving Inference for Neural Image Compression. - Woojeong Kim, Suhyun Kim, Mincheol Park, Geunseok Jeon:
Neuron Merging: Compensating for Pruned Neurons. - Kihyuk Sohn, David Berthelot, Nicholas Carlini, Zizhao Zhang, Han Zhang, Colin Raffel, Ekin Dogus Cubuk, Alexey Kurakin, Chun-Liang Li:
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence. - Arthur Delarue, Ross Anderson, Christian Tjandraatmadja:
Reinforcement Learning with Combinatorial Actions: An Application to Vehicle Routing. - 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:
Towards Playing Full MOBA Games with Deep Reinforcement Learning. - Weiwei Kong, Walid Krichene, Nicolas Mayoraz, Steffen Rendle, Li Zhang:
Rankmax: An Adaptive Projection Alternative to the Softmax Function. - Nataly Brukhim, Xinyi Chen, Elad Hazan, Shay Moran:
Online Agnostic Boosting via Regret Minimization. - Dong Zhang, Hanwang Zhang, Jinhui Tang, Xian-Sheng Hua, Qianru Sun:
Causal Intervention for Weakly-Supervised Semantic Segmentation. - Jonathan Kuck, Shuvam Chakraborty, Hao Tang, Rachel Luo, Jiaming Song, Ashish Sabharwal, Stefano Ermon:
Belief Propagation Neural Networks. - Yi Zhang, Orestis Plevrakis, Simon S. Du, Xingguo Li, Zhao Song, Sanjeev Arora:
Over-parameterized Adversarial Training: An Analysis Overcoming the Curse of Dimensionality. - Adil Khan, Khadija Fraz:
Post-training Iterative Hierarchical Data Augmentation for Deep Networks. - Julius Adebayo, Michael Muelly, Ilaria Liccardi, Been Kim:
Debugging Tests for Model Explanations. - Ajil Jalal, Liu Liu, Alexandros G. Dimakis, Constantine Caramanis:
Robust compressed sensing using generative models. - Preethi Lahoti, Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, Ed H. Chi:
Fairness without Demographics through Adversarially Reweighted Learning. - Alex X. Lee, Anusha Nagabandi, Pieter Abbeel, Sergey Levine:
Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model. - Jack Parker-Holder, Luke Metz, Cinjon Resnick, Hengyuan Hu, Adam Lerer, Alistair Letcher, Alexander Peysakhovich, Aldo Pacchiano, Jakob N. Foerster:
Ridge Rider: Finding Diverse Solutions by Following Eigenvectors of the Hessian. - Thiparat Chotibut, Fryderyk Falniowski, Michal Misiurewicz, Georgios Piliouras:
The route to chaos in routing games: When is price of anarchy too optimistic? - Arun Verma, Manjesh Kumar Hanawal, Csaba Szepesvári, Venkatesh Saligrama:
Online Algorithm for Unsupervised Sequential Selection with Contextual Information. - Yanxi Li, Zhaohui Yang, Yunhe Wang, Chang Xu:
Adapting Neural Architectures Between Domains. - Sana Tonekaboni, Shalmali Joshi, Kieran Campbell, David Duvenaud, Anna Goldenberg:
What went wrong and when? Instance-wise feature importance for time-series black-box models. - Yingxue Zhou, Belhal Karimi, Jinxing Yu, Zhiqiang Xu, Ping Li:
Towards Better Generalization of Adaptive Gradient Methods. - Tanmay Gangwani, Yuan Zhou, Jian Peng:
Learning Guidance Rewards with Trajectory-space Smoothing. - Chaobing Song, Yong Jiang, Yi Ma:
Variance Reduction via Accelerated Dual Averaging for Finite-Sum Optimization. - Rishi Sonthalia, Anna C. Gilbert:
Tree! I am no Tree! I am a low dimensional Hyperbolic Embedding. - Nick Pawlowski, Daniel Coelho de Castro, Ben Glocker:
Deep Structural Causal Models for Tractable Counterfactual Inference. - Dario Pavllo, Graham Spinks, Thomas Hofmann, Marie-Francine Moens, Aurélien Lucchi:
Convolutional Generation of Textured 3D Meshes. - Jianfei Chen, Yu Gai, Zhewei Yao, Michael W. Mahoney, Joseph E. Gonzalez:
A Statistical Framework for Low-bitwidth Training of Deep Neural Networks. - Qian Huang, Horace He, Abhay Singh, Yan Zhang, Ser-Nam Lim, Austin R. Benson:
Better Set Representations For Relational Reasoning. - Hao Zhang, Yuan Li, Zhijie Deng, Xiaodan Liang, Lawrence Carin, Eric P. Xing:
AutoSync: Learning to Synchronize for Data-Parallel Distributed Deep Learning. - Jianan Wang, Eren Sezener, David Budden, Marcus Hutter, Joel Veness:
A Combinatorial Perspective on Transfer Learning. - Amit Daniely, Gal Vardi:
Hardness of Learning Neural Networks with Natural Weights. - Steinar Laenen, He Sun:
Higher-Order Spectral Clustering of Directed Graphs. - Francesco Milano, Antonio Loquercio, Antoni Rosinol, Davide Scaramuzza, Luca Carlone:
Primal-Dual Mesh Convolutional Neural Networks. - Giulia Denevi, Massimiliano Pontil, Carlo Ciliberto:
The Advantage of Conditional Meta-Learning for Biased Regularization and Fine Tuning. - Qing Guo, Felix Juefei-Xu, Xiaofei Xie, Lei Ma, Jian Wang, Bing Yu, Wei Feng, Yang Liu:
Watch out! Motion is Blurring the Vision of Your Deep Neural Networks. - Zebang Shen, Zhenfu Wang, Alejandro Ribeiro, Hamed Hassani:
Sinkhorn Barycenter via Functional Gradient Descent. - Murad Tukan, Alaa Maalouf, Dan Feldman:
Coresets for Near-Convex Functions. - Bobby He, Balaji Lakshminarayanan, Yee Whye Teh:
Bayesian Deep Ensembles via the Neural Tangent Kernel. - Yunhui Guo, Mingrui Liu, Tianbao Yang, Tajana Rosing:
Improved Schemes for Episodic Memory-based Lifelong Learning. - Sebastian Curi, Kfir Y. Levy, Stefanie Jegelka, Andreas Krause:
Adaptive Sampling for Stochastic Risk-Averse Learning. - Jiangxin Dong, Stefan Roth, Bernt Schiele:
Deep Wiener Deconvolution: Wiener Meets Deep Learning for Image Deblurring. - Junhyuk Oh, Matteo Hessel, Wojciech M. Czarnecki, Zhongwen Xu, Hado van Hasselt, Satinder Singh, David Silver:
Discovering Reinforcement Learning Algorithms. - Jeffrey M. Dudek, Dror Fried, Kuldeep S. Meel:
Taming Discrete Integration via the Boon of Dimensionality. - Chenyang Lei, Yazhou Xing, Qifeng Chen:
Blind Video Temporal Consistency via Deep Video Prior. - Jingtao Ding, Yuhan Quan, Quanming Yao, Yong Li, Depeng Jin:
Simplify and Robustify Negative Sampling for Implicit Collaborative Filtering. - Zhenwen Dai, Praveen Chandar, Ghazal Fazelnia, Benjamin A. Carterette, Mounia Lalmas:
Model Selection for Production System via Automated Online Experiments. - Panayotis Mertikopoulos, Nadav Hallak, Ali Kavis, Volkan Cevher:
On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems. - Kaidi Xu, Zhouxing Shi, Huan Zhang, Yihan Wang, Kai-Wei Chang, Minlie Huang, Bhavya Kailkhura, Xue Lin, Cho-Jui Hsieh:
Automatic Perturbation Analysis for Scalable Certified Robustness and Beyond. - Luke I. Rast, Jan Drugowitsch:
Adaptation Properties Allow Identification of Optimized Neural Codes. - Junchi Yang, Negar Kiyavash, Niao He:
Global Convergence and Variance Reduction for a Class of Nonconvex-Nonconcave Minimax Problems. - Kaiqing Zhang, Sham M. Kakade, Tamer Basar, Lin F. Yang:
Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity. - Aviral Kumar, Aurick Zhou, George Tucker, Sergey Levine:
Conservative Q-Learning for Offline Reinforcement Learning. - Shuai Li, Fang Kong, Kejie Tang, Qizhi Li, Wei Chen:
Online Influence Maximization under Linear Threshold Model. - Ushnish Sengupta, Matt Amos, J. Scott Hosking, Carl Edward Rasmussen, Matthew P. Juniper, Paul J. Young:
Ensembling geophysical models with Bayesian Neural Networks. - Yuxi Li, Ning Xu, Jinlong Peng, John See, Weiyao Lin:
Delving into the Cyclic Mechanism in Semi-supervised Video Object Segmentation. - Christopher Frye, Colin Rowat, Ilya Feige:
Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability. - Xinshi Chen, Yufei Zhang, Christoph Reisinger, Le Song:
Understanding Deep Architecture with Reasoning Layer. - Anders Jonsson, Emilie Kaufmann, Pierre Ménard, Omar Darwiche Domingues, Edouard Leurent, Michal Valko:
Planning in Markov Decision Processes with Gap-Dependent Sample Complexity. - Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill:
Provably Good Batch Off-Policy Reinforcement Learning Without Great Exploration. - Ping-yeh Chiang, Michael J. Curry, Ahmed Abdelkader, Aounon Kumar, John Dickerson, Tom Goldstein:
Detection as Regression: Certified Object Detection with Median Smoothing. - Joey Huchette, Haihao Lu, Hossein Esfandiari, Vahab S. Mirrokni:
Contextual Reserve Price Optimization in Auctions via Mixed Integer Programming. - Shuxuan Guo, José M. Álvarez, Mathieu Salzmann:
ExpandNets: Linear Over-parameterization to Train Compact Convolutional Networks. - Dongsoo Lee, Se Jung Kwon, Byeongwook Kim, Yongkweon Jeon, Baeseong Park, Jeongin Yun:
FleXOR: Trainable Fractional Quantization. - Eran Malach, Shai Shalev-Shwartz:
The Implications of Local Correlation on Learning Some Deep Functions. - Samuel Håkansson, Viktor Lindblom, Omer Gottesman, Fredrik D. Johansson:
Learning to search efficiently for causally near-optimal treatments. - Ambar Pal, René Vidal:
A Game Theoretic Analysis of Additive Adversarial Attacks and Defenses. - Bertrand Charpentier, Daniel Zügner, Stephan Günnemann:
Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts. - Johannes Bausch:
Recurrent Quantum Neural Networks. - Emmanouil V. Vlatakis-Gkaragkounis, Lampros Flokas, Thanasis Lianeas, Panayotis Mertikopoulos, Georgios Piliouras:
No-Regret Learning and Mixed Nash Equilibria: They Do Not Mix. - Gergely Neu, Ciara Pike-Burke:
A Unifying View of Optimism in Episodic Reinforcement Learning. - Moran Feldman, Amin Karbasi:
Continuous Submodular Maximization: Beyond DR-Submodularity. - Andrea Tirinzoni, Matteo Pirotta, Marcello Restelli, Alessandro Lazaric:
An Asymptotically Optimal Primal-Dual Incremental Algorithm for Contextual Linear Bandits. - Oscar Chang, Lampros Flokas, Hod Lipson, Michael Spranger:
Assessing SATNet's Ability to Solve the Symbol Grounding Problem. - Michal Jamroz, Marcin Kurdziel, Mateusz Opala:
A Bayesian Nonparametrics View into Deep Representations. - Amnon Geifman, Abhay Kumar Yadav, Yoni Kasten, Meirav Galun, David W. Jacobs, Ronen Basri:
On the Similarity between the Laplace and Neural Tangent Kernels. - Yuval Atzmon, Felix Kreuk, Uri Shalit, Gal Chechik:
A causal view of compositional zero-shot recognition. - Albert Gu, Tri Dao, Stefano Ermon, Atri Rudra, Christopher Ré:
HiPPO: Recurrent Memory with Optimal Polynomial Projections. - Benteng Ma, Jing Zhang, Yong Xia, Dacheng Tao:
Auto Learning Attention. - Trent Kyono, Yao Zhang, Mihaela van der Schaar:
CASTLE: Regularization via Auxiliary Causal Graph Discovery. - Kaihua Tang, Jianqiang Huang, Hanwang Zhang:
Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect. - Dominik Peters, Ariel D. Procaccia, Alexandros Psomas, Zixin Zhou:
Explainable Voting. - Chun Kai Ling, Fei Fang, J. Zico Kolter:
Deep Archimedean Copulas. - Benjamin Letham, Roberto Calandra, Akshara Rai, Eytan Bakshy:
Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization. - Chu Zhou, Hang Zhao, Jin Han, Chang Xu, Chao Xu, Tiejun Huang, Boxin Shi:
UnModNet: Learning to Unwrap a Modulo Image for High Dynamic Range Imaging. - Shaogang Ren, Weijie Zhao, Ping Li:
Thunder: a Fast Coordinate Selection Solver for Sparse Learning. - Ziyin Liu, Tilman Hartwig, Masahito Ueda:
Neural Networks Fail to Learn Periodic Functions and How to Fix It. - Boyu Wang, Huidong Liu, Dimitris Samaras, Minh Hoai Nguyen:
Distribution Matching for Crowd Counting. - Riccardo Marin, Marie-Julie Rakotosaona, Simone Melzi, Maks Ovsjanikov:
Correspondence learning via linearly-invariant embedding. - Cong Zhang, Wen Song, Zhiguang Cao, Jie Zhang, Puay Siew Tan, Chi Xu:
Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning. - Florian Tramèr, Nicholas Carlini, Wieland Brendel, Aleksander Madry:
On Adaptive Attacks to Adversarial Example Defenses. - Zebang Shen, Zhenfu Wang, Alejandro Ribeiro, Hamed Hassani:
Sinkhorn Natural Gradient for Generative Models. - Arthur Mensch, Gabriel Peyré:
Online Sinkhorn: Optimal Transport distances from sample streams. - Marc T. Law, Jos Stam:
Ultrahyperbolic Representation Learning. - Ilja Kuzborskij, Nicolò Cesa-Bianchi:
Locally-Adaptive Nonparametric Online Learning. - Xinyun Chen, Chen Liang, Adams Wei Yu, Dawn Song, Denny Zhou:
Compositional Generalization via Neural-Symbolic Stack Machines. - Luana Ruiz, Luiz F. O. Chamon, Alejandro Ribeiro:
Graphon Neural Networks and the Transferability of Graph Neural Networks. - Mohsen Bayati, Nima Hamidi, Ramesh Johari, Khashayar Khosravi:
Unreasonable Effectiveness of Greedy Algorithms in Multi-Armed Bandit with Many Arms. - Michael Janner, Igor Mordatch, Sergey Levine:
Gamma-Models: Generative Temporal Difference Learning for Infinite-Horizon Prediction. - Xian Li, Asa Cooper Stickland, Yuqing Tang, Xiang Kong:
Deep Transformers with Latent Depth. - Kunal Gupta, Manmohan Chandraker:
Neural Mesh Flow: 3D Manifold Mesh Generation via Diffeomorphic Flows. - Jérôme-Alexis Chevalier, Joseph Salmon, Alexandre Gramfort, Bertrand Thirion:
Statistical control for spatio-temporal MEG/EEG source imaging with desparsified mutli-task Lasso. - Haoran Zhu, Pavankumar Murali, Dzung T. Phan, Lam M. Nguyen, Jayant Kalagnanam:
A Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees. - Kai Jia, Martin C. Rinard:
Efficient Exact Verification of Binarized Neural Networks. - Xiao Sun, Naigang Wang, Chia-Yu Chen, Jiamin Ni, Ankur Agrawal, Xiaodong Cui, Swagath Venkataramani, Kaoutar El Maghraoui, Vijayalakshmi Srinivasan, Kailash Gopalakrishnan:
Ultra-Low Precision 4-bit Training of Deep Neural Networks. - Han Shi, Renjie Pi, Hang Xu, Zhenguo Li, James T. Kwok, Tong Zhang:
Bridging the Gap between Sample-based and One-shot Neural Architecture Search with BONAS. - Xi Zhang, Xiaolin Wu:
On Numerosity of Deep Neural Networks. - Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia:
Outlier Robust Mean Estimation with Subgaussian Rates via Stability. - Jiuxiang Gu, Jason Kuen, Shafiq R. Joty, Jianfei Cai, Vlad I. Morariu, Handong Zhao, Tong Sun:
Self-Supervised Relationship Probing. - Zifeng Wang, Xi Chen, Rui Wen, Shao-Lun Huang, Ercan E. Kuruoglu, Yefeng Zheng:
Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback. - Fenglin Liu, Xuancheng Ren, Xian Wu, Shen Ge, Wei Fan, Yuexian Zou, Xu Sun:
Prophet Attention: Predicting Attention with Future Attention. - Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared 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 M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, Dario Amodei:
Language Models are Few-Shot Learners. - Allan Grønlund, Lior Kamma, Kasper Green Larsen:
Margins are Insufficient for Explaining Gradient Boosting. - Alex Tseng, Avanti Shrikumar, Anshul Kundaje:
Fourier-transform-based attribution priors improve the interpretability and stability of deep learning models for genomics. - Tan M. Nguyen, Richard G. Baraniuk, Andrea L. Bertozzi, Stanley J. Osher, Bao Wang:
MomentumRNN: Integrating Momentum into Recurrent Neural Networks. - Zaheen Farraz Ahmad, Levi Lelis, Michael Bowling:
Marginal Utility for Planning in Continuous or Large Discrete Action Spaces. - Peng Chen, Omar Ghattas:
Projected Stein Variational Gradient Descent. - Seyed Mohammadreza Mousavi Kalan, Zalan Fabian, Salman Avestimehr, Mahdi Soltanolkotabi:
Minimax Lower Bounds for Transfer Learning with Linear and One-hidden Layer Neural Networks. - Fabian Fuchs, Daniel E. Worrall, Volker Fischer, Max Welling:
SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks. - Masashi Tsubaki, Teruyasu Mizoguchi:
On the equivalence of molecular graph convolution and molecular wave function with poor basis set. - Chenkai Yu, Guanya Shi, Soon-Jo Chung, Yisong Yue, Adam Wierman:
The Power of Predictions in Online Control. - Tushar Nagarajan, Kristen Grauman:
Learning Affordance Landscapes for Interaction Exploration in 3D Environments. - Ilai Bistritz, Nicholas Bambos:
Cooperative Multi-player Bandit Optimization. - Shinji Ito, Shuichi Hirahara, Tasuku Soma, Yuichi Yoshida:
Tight First- and Second-Order Regret Bounds for Adversarial Linear Bandits. - Zhao Chen, Jiquan Ngiam, Yanping Huang, Thang Luong, Henrik Kretzschmar, Yuning Chai, Dragomir Anguelov:
Just Pick a Sign: Optimizing Deep Multitask Models with Gradient Sign Dropout. - Steffen Czolbe, Oswin Krause, Ingemar J. Cox, Christian Igel:
A Loss Function for Generative Neural Networks Based on Watson's Perceptual Model. - Ervine Zheng, Qi Yu, Rui Li, Pengcheng Shi, Anne R. Haake:
Dynamic Fusion of Eye Movement Data and Verbal Narrations in Knowledge-rich Domains. - Guannan Qu, Yiheng Lin, Adam Wierman, Na Li:
Scalable Multi-Agent Reinforcement Learning for Networked Systems with Average Reward. - Akshunna S. Dogra, William T. Redman:
Optimizing Neural Networks via Koopman Operator Theory. - Sinho Chewi, Thibaut Le Gouic, Chen Lu, Tyler Maunu, Philippe Rigollet:
SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergence. - Jeremias Sulam, Ramchandran Muthukumar, Raman Arora:
Adversarial Robustness of Supervised Sparse Coding. - Craig Boutilier, Chih-Wei Hsu, Branislav Kveton, Martin Mladenov, Csaba Szepesvári, Manzil Zaheer:
Differentiable Meta-Learning of Bandit Policies. - Manish V. Reddy, Andrzej Banburski, Nishka Pant, Tomaso A. Poggio:
Biologically Inspired Mechanisms for Adversarial Robustness. - Surbhi Goel, Aravind Gollakota, Adam R. Klivans:
Statistical-Query Lower Bounds via Functional Gradients. - Yu Bai, Chi Jin, Tiancheng Yu:
Near-Optimal Reinforcement Learning with Self-Play. - Shushan He, Hongyuan Zha, Xiaojing Ye:
Network Diffusions via Neural Mean-Field Dynamics. - Zhilu Zhang, Mert R. Sabuncu:
Self-Distillation as Instance-Specific Label Smoothing. - Yunbei Xu, Assaf Zeevi:
Towards Problem-dependent Optimal Learning Rates. - Chau Tran, Yuqing Tang, Xian Li, Jiatao Gu:
Cross-lingual Retrieval for Iterative Self-Supervised Training. - Diego Mesquita, Amauri H. Souza Jr., Samuel Kaski:
Rethinking pooling in graph neural networks. - Petar Velickovic, Lars Buesing, Matthew C. Overlan, Razvan Pascanu, Oriol Vinyals, Charles Blundell:
Pointer Graph Networks. - Yao Zhang, Mihaela van der Schaar:
Gradient Regularized V-Learning for Dynamic Treatment Regimes. - Lénaïc Chizat, Pierre Roussillon, Flavien Léger, François-Xavier Vialard, Gabriel Peyré:
Faster Wasserstein Distance Estimation with the Sinkhorn Divergence. - Veronica Chelu, Doina Precup, Hado van Hasselt:
Forethought and Hindsight in Credit Assignment. - Hyun-Suk Lee, Yao Zhang, William R. Zame, Cong Shen, Jang-Won Lee, Mihaela van der Schaar:
Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification. - Diego M. Arribas, Yuan Zhao, Il Memming Park:
Rescuing neural spike train models from bad MLE. - Filip Hanzely, Slavomír Hanzely, Samuel Horváth, Peter Richtárik:
Lower Bounds and Optimal Algorithms for Personalized Federated Learning. - Dinghuai Zhang, Mao Ye, Chengyue Gong, Zhanxing Zhu, Qiang Liu:
Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework. - Fan Xie, Alexander Chowdhury, M. Clara De Paolis Kaluza, Linfeng Zhao, Lawson L. S. Wong, Rose Yu:
Deep Imitation Learning for Bimanual Robotic Manipulation. - Lassi Meronen, Christabella Irwanto, Arno Solin:
Stationary Activations for Uncertainty Calibration in Deep Learning. - Tao Lin, Lingjing Kong, Sebastian U. Stich, Martin Jaggi:
Ensemble Distillation for Robust Model Fusion in Federated Learning. - Qian Lou, Wen-jie Lu, Cheng Hong, Lei Jiang:
Falcon: Fast Spectral Inference on Encrypted Data. - Ekaterina Lobacheva, Nadezhda Chirkova, Maxim Kodryan, Dmitry P. Vetrov:
On Power Laws in Deep Ensembles. - Donald Goldfarb, Yi Ren, Achraf Bahamou:
Practical Quasi-Newton Methods for Training Deep Neural Networks. - Tomas Geffner, Justin Domke:
Approximation Based Variance Reduction for Reparameterization Gradients. - Jianfeng Zhang, Xuecheng Nie, Jiashi Feng:
Inference Stage Optimization for Cross-scenario 3D Human Pose Estimation. - Vu C. Dinh, Lam Si Tung Ho:
Consistent feature selection for analytic deep neural networks. - Yulin Wang, Kangchen Lv, Rui Huang, Shiji Song, Le Yang, Gao Huang:
Glance and Focus: a Dynamic Approach to Reducing Spatial Redundancy in Image Classification. - Malik Boudiaf, Imtiaz Masud Ziko, Jérôme Rony, Jose Dolz, Pablo Piantanida, Ismail Ben Ayed:
Information Maximization for Few-Shot Learning. - Giorgia Ramponi, Gianluca Drappo, Marcello Restelli:
Inverse Reinforcement Learning from a Gradient-based Learner. - Fan Yang, Alina Vereshchaka, Changyou Chen, Wen Dong:
Bayesian Multi-type Mean Field Multi-agent Imitation Learning. - Daniel S. Brown, Scott Niekum, Marek Petrik:
Bayesian Robust Optimization for Imitation Learning. - Lior Yariv, Yoni Kasten, Dror Moran, Meirav Galun, Matan Atzmon, Ronen Basri, Yaron Lipman:
Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance. - Emile Mathieu, Maximilian Nickel:
Riemannian Continuous Normalizing Flows. - Isabella Pozzi, Sander M. Bohté, Pieter R. Roelfsema:
Attention-Gated Brain Propagation: How the brain can implement reward-based error backpropagation. - Ziv Goldfeld, Kristjan H. Greenewald, Kengo Kato:
Asymptotic Guarantees for Generative Modeling Based on the Smooth Wasserstein Distance. - Scott Pesme, Nicolas Flammarion:
Online Robust Regression via SGD on the l1 loss. - Yuriy Biktairov, Maxim Stebelev, Irina Rudenko, Oleh Shliazhko, Boris Yangel:
PRANK: motion Prediction based on RANKing. - Chuan Wen, Jierui Lin, Trevor Darrell, Dinesh Jayaraman, Yang Gao:
Fighting Copycat Agents in Behavioral Cloning from Observation Histories. - Raphaël Berthier, Francis R. Bach, Pierre Gaillard:
Tight Nonparametric Convergence Rates for Stochastic Gradient Descent under the Noiseless Linear Model. - Ruohan Wang, Yiannis Demiris, Carlo Ciliberto:
Structured Prediction for Conditional Meta-Learning. - Ankit Pensia, Shashank Rajput, Alliot Nagle, Harit Vishwakarma, Dimitris S. Papailiopoulos:
Optimal Lottery Tickets via Subset Sum: Logarithmic Over-Parameterization is Sufficient. - Douwe Kiela, Hamed Firooz, Aravind Mohan, Vedanuj Goswami, Amanpreet Singh, Pratik Ringshia, Davide Testuggine:
The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes. - Lingkai Kong, Molei Tao:
Stochasticity of Deterministic Gradient Descent: Large Learning Rate for Multiscale Objective Function. - Aran Nayebi, Sanjana Srivastava, Surya Ganguli, Daniel L. K. Yamins:
Identifying Learning Rules From Neural Network Observables. - Alessandro Epasto, Mohammad Mahdian, Vahab S. Mirrokni, Emmanouil Zampetakis:
Optimal Approximation - Smoothness Tradeoffs for Soft-Max Functions. - Lisa Lee, Ben Eysenbach, Ruslan Salakhutdinov, Shixiang Shane Gu, Chelsea Finn:
Weakly-Supervised Reinforcement Learning for Controllable Behavior. - Duncan C. McElfresh, Michael J. Curry, Tuomas Sandholm, John Dickerson:
Improving Policy-Constrained Kidney Exchange via Pre-Screening. - Lucas Yanan Tian, Kevin Ellis, Marta Kryven, Josh Tenenbaum:
Learning abstract structure for drawing by efficient motor program induction. - Kaixuan Huang, Yuqing Wang, Molei Tao, Tuo Zhao:
Why Do Deep Residual Networks Generalize Better than Deep Feedforward Networks? - A Neural Tangent Kernel Perspective. - Krikamol Muandet, Arash Mehrjou, Si Kai Lee, Anant Raj:
Dual Instrumental Variable Regression. - Hao Chen, Lili Zheng, Raed Al Kontar, Garvesh Raskutti:
Stochastic Gradient Descent in Correlated Settings: A Study on Gaussian Processes. - Zhongqi Yue, Hanwang Zhang, Qianru Sun, Xian-Sheng Hua:
Interventional Few-Shot Learning. - Nan Jiang, Jiawei Huang:
Minimax Value Interval for Off-Policy Evaluation and Policy Optimization. - Yifan Hu, Siqi Zhang, Xin Chen, Niao He:
Biased Stochastic First-Order Methods for Conditional Stochastic Optimization and Applications in Meta Learning. - Haoran You, Xiaohan Chen, Yongan Zhang, Chaojian Li, Sicheng Li, Zihao Liu, Zhangyang Wang, Yingyan Lin:
ShiftAddNet: A Hardware-Inspired Deep Network. - Robin Rombach, Patrick Esser, Björn Ommer:
Network-to-Network Translation with Conditional Invertible Neural Networks. - Yash Savani, Colin White, Naveen Sundar Govindarajulu:
Intra-Processing Methods for Debiasing Neural Networks. - Songtao Lu, Meisam Razaviyayn, Bo Yang, Kejun Huang, Mingyi Hong:
Finding Second-Order Stationary Points Efficiently in Smooth Nonconvex Linearly Constrained Optimization Problems. - Jian Shen, Han Zhao, Weinan Zhang, Yong Yu:
Model-based Policy Optimization with Unsupervised Model Adaptation. - Xiaoxia Wu, Edgar Dobriban, Tongzheng Ren, Shanshan Wu, Zhiyuan Li, Suriya Gunasekar, Rachel A. Ward, Qiang Liu:
Implicit Regularization and Convergence for Weight Normalization. - Changlin Wan, Wennan Chang, Tong Zhao, Sha Cao, Chi Zhang:
Geometric All-way Boolean Tensor Decomposition. - Yutian Chen, Abram L. Friesen, Feryal M. P. Behbahani, Arnaud Doucet, David Budden, Matthew Hoffman, Nando de Freitas:
Modular Meta-Learning with Shrinkage. - Preetam Nandy, Kinjal Basu, Shaunak Chatterjee, Ye Tu:
A/B Testing in Dense Large-Scale Networks: Design and Inference. - Vitaly Feldman, Chiyuan Zhang:
What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation. - Zhenyu Huang, Peng Hu, Joey Tianyi Zhou, Jiancheng Lv, Xi Peng:
Partially View-aligned Clustering. - Laetitia Chapel, Mokhtar Z. Alaya, Gilles Gasso:
Partial Optimal Tranport with applications on Positive-Unlabeled Learning. - Nived Rajaraman, Lin F. Yang, Jiantao Jiao, Kannan Ramchandran:
Toward the Fundamental Limits of Imitation Learning. - Laurent Orseau, Marcus Hutter, Omar Rivasplata:
Logarithmic Pruning is All You Need. - Guillermo Ortiz-Jiménez, Apostolos Modas, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard:
Hold me tight! Influence of discriminative features on deep network boundaries. - Raef Bassily, Shay Moran, Anupama Nandi:
Learning from Mixtures of Private and Public Populations. - Dongxian Wu, Shu-Tao Xia, Yisen Wang:
Adversarial Weight Perturbation Helps Robust Generalization. - Yuval Emek, Ron Lavi, Rad Niazadeh, Yangguang Shi:
Stateful Posted Pricing with Vanishing Regret via Dynamic Deterministic Markov Decision Processes. - Minseon Kim, Jihoon Tack, Sung Ju Hwang:
Adversarial Self-Supervised Contrastive Learning. - Emmanuel de Bézenac, Syama Sundar Rangapuram, Konstantinos Benidis, Michael Bohlke-Schneider, Richard Kurle, Lorenzo Stella, Hilaf Hasson, Patrick Gallinari, Tim Januschowski:
Normalizing Kalman Filters for Multivariate Time Series Analysis. - Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel M. Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, Paul F. Christiano:
Learning to summarize with human feedback. - Tarik Dzanic, Karan Shah, Freddie D. Witherden:
Fourier Spectrum Discrepancies in Deep Network Generated Images. - Dongqi Han, Erik De Schutter, Sungho Hong:
Lamina-specific neuronal properties promote robust, stable signal propagation in feedforward networks. - Ashutosh Adhikari, Xingdi Yuan, Marc-Alexandre Côté, Mikulas Zelinka, Marc-Antoine Rondeau, Romain Laroche, Pascal Poupart, Jian Tang, Adam Trischler, William L. Hamilton:
Learning Dynamic Belief Graphs to Generalize on Text-Based Games. - Stéphane d'Ascoli, Levent Sagun, Giulio Biroli:
Triple descent and the two kinds of overfitting: where & why do they appear? - Raeid Saqur, Karthik Narasimhan:
Multimodal Graph Networks for Compositional Generalization in Visual Question Answering. - Daniel D. Johnson, Hugo Larochelle, Daniel Tarlow:
Learning Graph Structure With A Finite-State Automaton Layer. - Yulong Lu, Jianfeng Lu:
A Universal Approximation Theorem of Deep Neural Networks for Expressing Probability Distributions. - Paul Henderson, Christoph H. Lampert:
Unsupervised object-centric video generation and decomposition in 3D. - Haoliang Li, Yufei Wang, Renjie Wan, Shiqi Wang, Tie-Qiang Li, Alex C. Kot:
Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization. - Guoqiang Wu, Jun Zhu:
Multi-label classification: do Hamming loss and subset accuracy really conflict with each other? - Dieqiao Feng, Carla P. Gomes, Bart Selman:
A Novel Automated Curriculum Strategy to Solve Hard Sokoban Planning Instances. - Atalanti-Anastasia Mastakouri, Bernhard Schölkopf:
Causal analysis of Covid-19 Spread in Germany. - Thomas Berrett, Cristina Butucea:
Locally private non-asymptotic testing of discrete distributions is faster using interactive mechanisms. - Fartash Faghri, Iman Tabrizian, Ilia Markov, Dan Alistarh, Daniel M. Roy, Ali Ramezani-Kebrya:
Adaptive Gradient Quantization for Data-Parallel SGD. - Solenne Gaucher:
Finite Continuum-Armed Bandits. - Itai Gat, Idan Schwartz, Alexander G. Schwing, Tamir Hazan:
Removing Bias in Multi-modal Classifiers: Regularization by Maximizing Functional Entropies. - Severin Berger, Christian K. Machens:
Compact task representations as a normative model for higher-order brain activity. - Edouard Leurent, Odalric-Ambrym Maillard, Denis V. Efimov:
Robust-Adaptive Control of Linear Systems: beyond Quadratic Costs. - Sijing Tu, Çigdem Aslay, Aristides Gionis:
Co-exposure Maximization in Online Social Networks. - Benoît Guillard, Edoardo Remelli, Pascal Fua:
UCLID-Net: Single View Reconstruction in Object Space. - Jongmin Lee, Byung-Jun Lee, Kee-Eung Kim:
Reinforcement Learning for Control with Multiple Frequencies. - Stefano Sarao Mannelli, Giulio Biroli, Chiara Cammarota, Florent Krzakala, Pierfrancesco Urbani, Lenka Zdeborová:
Complex Dynamics in Simple Neural Networks: Understanding Gradient Flow in Phase Retrieval. - Naganand Yadati:
Neural Message Passing for Multi-Relational Ordered and Recursive Hypergraphs. - Saurabh Garg, Yifan Wu, Sivaraman Balakrishnan, Zachary C. Lipton:
A Unified View of Label Shift Estimation. - Christos Tzamos, Emmanouil V. Vlatakis-Gkaragkounis, Ilias Zadik:
Optimal Private Median Estimation under Minimal Distributional Assumptions. - Wei-Ning Chen, Peter Kairouz, Ayfer Özgür:
Breaking the Communication-Privacy-Accuracy Trilemma. - Kun Su, Xiulong Liu, Eli Shlizerman:
Audeo: Audio Generation for a Silent Performance Video. - Krzysztof Marcin Choromanski, Jared Quincy Davis, Valerii Likhosherstov, Xingyou Song, Jean-Jacques E. Slotine, Jacob Varley, Honglak Lee, Adrian Weller, Vikas Sindhwani:
Ode to an ODE. - Hossein Mobahi, Mehrdad Farajtabar, Peter L. Bartlett:
Self-Distillation Amplifies Regularization in Hilbert Space. - Takeshi Teshima, Isao Ishikawa, Koichi Tojo, Kenta Oono, Masahiro Ikeda, Masashi Sugiyama:
Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators. - Po-Wei Wang, J. Zico Kolter:
Community detection using fast low-cardinality semidefinite programming . - Jia Wan, Antoni B. Chan:
Modeling Noisy Annotations for Crowd Counting. - Dibya Ghosh, Marlos C. Machado, Nicolas Le Roux:
An operator view of policy gradient methods. - Senthil Purushwalkam, Abhinav Gupta:
Demystifying Contrastive Self-Supervised Learning: Invariances, Augmentations and Dataset Biases. - Aditya Bhaskara, Amin Karbasi, Silvio Lattanzi, Morteza Zadimoghaddam:
Online MAP Inference of Determinantal Point Processes. - Yongqing Liang, Xin Li, Navid H. Jafari, Jim Chen:
Video Object Segmentation with Adaptive Feature Bank and Uncertain-Region Refinement. - Zoe Ashwood, Nicholas A. Roy, Ji Hyun Bak, Jonathan W. Pillow:
Inferring learning rules from animal decision-making. - Tuan Anh Nguyen, Anh Tuan Tran:
Input-Aware Dynamic Backdoor Attack. - Andreas Loukas:
How hard is to distinguish graphs with graph neural networks? - Lin Chen, Qian Yu, Hannah Lawrence, Amin Karbasi:
Minimax Regret of Switching-Constrained Online Convex Optimization: No Phase Transition. - Wei-An Lin, Chun Pong Lau, Alexander Levine, Rama Chellappa, Soheil Feizi:
Dual Manifold Adversarial Robustness: Defense against Lp and non-Lp Adversarial Attacks. - Shangchen Zhou, Jiawei Zhang, Wangmeng Zuo, Chen Change Loy:
Cross-Scale Internal Graph Neural Network for Image Super-Resolution. - Feng Wang, Huaping Liu, Di Guo, Fuchun Sun:
Unsupervised Representation Learning by Invariance Propagation. - Yukuan Yang, Fangyun Wei, Miaojing Shi, Guoqi Li:
Restoring Negative Information in Few-Shot Object Detection. - Hadi Salman, Andrew Ilyas, Logan Engstrom, Ashish Kapoor, Aleksander Madry:
Do Adversarially Robust ImageNet Models Transfer Better? - Bijan Mazaheri, Siddharth Jain, Jehoshua Bruck:
Robust Correction of Sampling Bias using Cumulative Distribution Functions. - Alireza Fallah, Aryan Mokhtari, Asuman E. Ozdaglar:
Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach. - Guoliang Kang, Yunchao Wei, Yi Yang, Yueting Zhuang, Alexander G. Hauptmann:
Pixel-Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation. - Yaniv Romano, Matteo Sesia, Emmanuel J. Candès:
Classification with Valid and Adaptive Coverage. - Tejaswini Pedapati, Avinash Balakrishnan, Karthikeyan Shanmugam, Amit Dhurandhar:
Learning Global Transparent Models consistent with Local Contrastive Explanations. - Ali Siahkamari, Xide Xia, Venkatesh Saligrama, David A. Castañón, Brian Kulis:
Learning to Approximate a Bregman Divergence. - Shweta Mahajan, Stefan Roth:
Diverse Image Captioning with Context-Object Split Latent Spaces. - Armand Comas Massague, Chi Zhang, Zlatan Feric, Octavia I. Camps, Rose Yu:
Learning Disentangled Representations of Videos with Missing Data. - Pim de Haan, Taco S. Cohen, Max Welling:
Natural Graph Networks. - Sangwon Jung, Hongjoon Ahn, Sungmin Cha, Taesup Moon:
Continual Learning with Node-Importance based Adaptive Group Sparse Regularization. - Max Ryabinin, Anton Gusev:
Towards Crowdsourced Training of Large Neural Networks using Decentralized Mixture-of-Experts. - Wenchao Chen, Chaojie Wang, Bo Chen, Yicheng Liu, Hao Zhang, Mingyuan Zhou:
Bidirectional Convolutional Poisson Gamma Dynamical Systems. - Bogdan Mazoure, Remi Tachet des Combes, Thang Doan, Philip Bachman, R. Devon Hjelm:
Deep Reinforcement and InfoMax Learning. - Clément Calauzènes, Nicolas Usunier:
On ranking via sorting by estimated expected utility. - Chirag Gupta, Aleksandr Podkopaev, Aaditya Ramdas:
Distribution-free binary classification: prediction sets, confidence intervals and calibration. - Didrik Nielsen, Ole Winther:
Closing the Dequantization Gap: PixelCNN as a Single-Layer Flow. - Jing Shi, Xuankai Chang, Pengcheng Guo, Shinji Watanabe, Yusuke Fujita, Jiaming Xu, Bo Xu, Lei Xie:
Sequence to Multi-Sequence Learning via Conditional Chain Mapping for Mixture Signals. - Zhiyan Ding, Qin Li:
Variance reduction for Random Coordinate Descent-Langevin Monte Carlo. - Cédric Colas, Tristan Karch, Nicolas Lair, Jean-Michel Dussoux, Clément Moulin-Frier, Peter F. Dominey, Pierre-Yves Oudeyer:
Language as a Cognitive Tool to Imagine Goals in Curiosity Driven Exploration. - Sho Takase, Sosuke Kobayashi:
All Word Embeddings from One Embedding. - Adil Salim, Peter Richtárik:
Primal Dual Interpretation of the Proximal Stochastic Gradient Langevin Algorithm. - Yihong Gu, Weizhong Zhang, Cong Fang, Jason D. Lee, Tong Zhang:
How to Characterize The Landscape of Overparameterized Convolutional Neural Networks. - Richard Y. Zhang:
On the Tightness of Semidefinite Relaxations for Certifying Robustness to Adversarial Examples. - Arman Adibi, Aryan Mokhtari, Hamed Hassani:
Submodular Meta-Learning. - Barret Zoph, Golnaz Ghiasi, Tsung-Yi Lin, Yin Cui, Hanxiao Liu, Ekin Dogus Cubuk, Quoc Le:
Rethinking Pre-training and Self-training. - Scott Wisdom, Efthymios Tzinis, Hakan Erdogan, Ron J. Weiss, Kevin W. Wilson, John R. Hershey:
Unsupervised Sound Separation Using Mixture Invariant Training. - Sean R. Sinclair, Tianyu Wang, Gauri Jain, Siddhartha Banerjee, Christina Lee Yu:
Adaptive Discretization for Model-Based Reinforcement Learning. - Zeping Yu, Wenxin Zheng, Jiaqi Wang, Qiyi Tang, Sen Nie, Shi Wu:
CodeCMR: Cross-Modal Retrieval For Function-Level Binary Source Code Matching. - Jordan T. Ash, Ryan P. Adams:
On Warm-Starting Neural Network Training. - Dennis Wei, Tian Gao, Yue Yu:
DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networks. - Taewon Jeong, Heeyoung Kim:
OOD-MAML: Meta-Learning for Few-Shot Out-of-Distribution Detection and Classification. - Siddharth Desai, Ishan Durugkar, Haresh Karnan, Garrett Warnell, Josiah Hanna, Peter Stone:
An Imitation from Observation Approach to Transfer Learning with Dynamics Mismatch. - Martin Lohmann, Jordi Salvador, Aniruddha Kembhavi, Roozbeh Mottaghi:
Learning About Objects by Learning to Interact with Them. - Doron Cohen, Aryeh Kontorovich, Geoffrey Wolfer:
Learning discrete distributions with infinite support. - Stefano Massaroli, Michael Poli, Jinkyoo Park, Atsushi Yamashita, Hajime Asama:
Dissecting Neural ODEs. - Siddarth Asokan, Chandra Sekhar Seelamantula:
Teaching a GAN What Not to Learn. - Silviu Pitis, Elliot Creager, Animesh Garg:
Counterfactual Data Augmentation using Locally Factored Dynamics. - Lin Song, Yanwei Li, Zhengkai Jiang, Zeming Li, Xiangyu Zhang, Hongbin Sun, Jian Sun, Nanning Zheng:
Rethinking Learnable Tree Filter for Generic Feature Transform. - Massimiliano Patacchiola, Amos J. Storkey:
Self-Supervised Relational Reasoning for Representation Learning. - Cheng Meng, Jun Yu, Jingyi Zhang, Ping Ma, Wenxuan Zhong:
Sufficient dimension reduction for classification using principal optimal transport direction. - Jiajin Li, Caihua Chen, Anthony Man-Cho So:
Fast Epigraphical Projection-based Incremental Algorithms for Wasserstein Distributionally Robust Support Vector Machine. - Badih Ghazi, Ravi Kumar, Pasin Manurangsi:
Differentially Private Clustering: Tight Approximation Ratios. - Vincent Cohen-Addad, Adrian Kosowski, Frederik Mallmann-Trenn, David Saulpic:
On the Power of Louvain in the Stochastic Block Model. - Forest Yang, Mouhamadou Cisse, Oluwasanmi Koyejo:
Fairness with Overlapping Groups; a Probabilistic Perspective. - Afshin Oroojlooy, MohammadReza Nazari, Davood Hajinezhad, Jorge Silva:
AttendLight: Universal Attention-Based Reinforcement Learning Model for Traffic Signal Control. - Zhaohui Yang, Yunhe Wang, Kai Han, Chunjing Xu, Chao Xu, Dacheng Tao, Chang Xu:
Searching for Low-Bit Weights in Quantized Neural Networks. - Qiong Wu, Felix Ming Fai Wong, Yanhua Li, Zhenming Liu, Varun Kanade:
Adaptive Reduced Rank Regression. - Nir Rosenfeld, Sophie Hilgard, Sai Srivatsa Ravindranath, David C. Parkes:
From Predictions to Decisions: Using Lookahead Regularization. - Sue Zheng, David S. Hayden, Jason Pacheco, John W. Fisher III:
Sequential Bayesian Experimental Design with Variable Cost Structure. - Byol Kim, Chen Xu, Rina Foygel Barber:
Predictive inference is free with the jackknife+-after-bootstrap. - Amanda Coston, Edward H. Kennedy, Alexandra Chouldechova:
Counterfactual Predictions under Runtime Confounding. - Ildoo Kim, Younghoon Kim, Sungwoong Kim:
Learning Loss for Test-Time Augmentation. - Jiawei Ren, Cunjun Yu, Shunan Sheng, Xiao Ma, Haiyu Zhao, Shuai Yi, Hongsheng Li:
Balanced Meta-Softmax for Long-Tailed Visual Recognition. - Sreejith Balakrishnan, Quoc Phong Nguyen, Bryan Kian Hsiang Low, Harold Soh:
Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization. - Elise van der Pol, Daniel E. Worrall, Herke van Hoof, Frans A. Oliehoek, Max Welling:
MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning. - Jeya Vikranth Jeyakumar, Joseph Noor, Yu-Hsi Cheng, Luis Garcia, Mani B. Srivastava:
How Can I Explain This to You? An Empirical Study of Deep Neural Network Explanation Methods. - Kunal Talwar:
On the Error Resistance of Hinge-Loss Minimization. - Nino Vieillard, Olivier Pietquin, Matthieu Geist:
Munchausen Reinforcement Learning. - Devendra Singh Chaplot, Dhiraj Gandhi, Abhinav Gupta, Ruslan Salakhutdinov:
Object Goal Navigation using Goal-Oriented Semantic Exploration. - Chirag Pabbaraju, Po-Wei Wang, J. Zico Kolter:
Efficient semidefinite-programming-based inference for binary and multi-class MRFs. - Zihang Dai, Guokun Lai, Yiming Yang, Quoc Le:
Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing. - Matthew Chang, Arjun Gupta, Saurabh Gupta:
Semantic Visual Navigation by Watching YouTube Videos. - Hamid Jalalzai, Pierre Colombo, Chloé Clavel, Éric Gaussier, Giovanna Varni, Emmanuel Vignon, Anne Sabourin:
Heavy-tailed Representations, Text Polarity Classification & Data Augmentation. - Thibault Castells, Philippe Weinzaepfel, Jérôme Revaud:
SuperLoss: A Generic Loss for Robust Curriculum Learning. - Vijil Chenthamarakshan, Payel Das, Samuel C. Hoffman, Hendrik Strobelt, Inkit Padhi, Kar Wai Lim, Benjamin Hoover, Matteo Manica, Jannis Born, Teodoro Laino, Aleksandra Mojsilovic:
CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models. - Yijie Guo, Jongwook Choi, Marcin Moczulski, Shengyu Feng, Samy Bengio, Mohammad Norouzi, Honglak Lee:
Memory Based Trajectory-conditioned Policies for Learning from Sparse Rewards. - Sebastian Farquhar, Lewis Smith, Yarin Gal:
Liberty or Depth: Deep Bayesian Neural Nets Do Not Need Complex Weight Posterior Approximations. - Tengyu Xu, Zhe Wang, Yingbin Liang:
Improving Sample Complexity Bounds for (Natural) Actor-Critic Algorithms. - Jacob Kelly, Jesse Bettencourt, Matthew J. Johnson, David Duvenaud:
Learning Differential Equations that are Easy to Solve. - Raef Bassily, Vitaly Feldman, Cristóbal Guzmán, Kunal Talwar:
Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses. - Jinke He, Miguel Suau, Frans A. Oliehoek:
Influence-Augmented Online Planning for Complex Environments. - Pranjal Awasthi, Satyen Kale, Stefani Karp, Mehryar Mohri:
PAC-Bayes Learning Bounds for Sample-Dependent Priors. - Hong Jun Jeon, Smitha Milli, Anca D. Dragan:
Reward-rational (implicit) choice: A unifying formalism for reward learning. - Vincent Le Guen, Nicolas Thome:
Probabilistic Time Series Forecasting with Shape and Temporal Diversity. - Sarah Jane Hong, Martín Arjovsky, Darryl Barnhart, Ian Thompson:
Low Distortion Block-Resampling with Spatially Stochastic Networks. - Pingbo Pan, Siddharth Swaroop, Alexander Immer, Runa Eschenhagen, Richard E. Turner, Mohammad Emtiyaz Khan:
Continual Deep Learning by Functional Regularisation of Memorable Past. - Pan Li, Yanbang Wang, Hongwei Wang, Jure Leskovec:
Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation Learning. - Lu Chi, Borui Jiang, Yadong Mu:
Fast Fourier Convolution. - Pedro O. Pinheiro, Amjad Almahairi, Ryan Y. Benmalek, Florian Golemo, Aaron C. Courville:
Unsupervised Learning of Dense Visual Representations. - Jeet Mohapatra, Ching-Yun Ko, Tsui-Wei Weng, Pin-Yu Chen, Sijia Liu, Luca Daniel:
Higher-Order Certification For Randomized Smoothing. - Sitan Chen, Jerry Li, Ankur Moitra:
Learning Structured Distributions From Untrusted Batches: Faster and Simpler. - Will Williams, Sam Ringer, Tom Ash, David MacLeod, Jamie Dougherty, John Hughes:
Hierarchical Quantized Autoencoders. - Yusuke Tashiro, Yang Song, Stefano Ermon:
Diversity can be Transferred: Output Diversification for White- and Black-box Attacks. - Weichao Mao, Kaiqing Zhang, Qiaomin Xie, Tamer Basar:
POLY-HOOT: Monte-Carlo Planning in Continuous Space MDPs with Non-Asymptotic Analysis. - Yuqing Du, Stas Tiomkin, Emre Kiciman, Daniel Polani, Pieter Abbeel, Anca D. Dragan:
AvE: Assistance via Empowerment. - Junyu Zhang, Alec Koppel, Amrit Singh Bedi, Csaba Szepesvári, Mengdi Wang:
Variational Policy Gradient Method for Reinforcement Learning with General Utilities. - Rylan Schaeffer, Mikail Khona, Leenoy Meshulam, International Brain Laboratory, Ila Fiete:
Reverse-engineering recurrent neural network solutions to a hierarchical inference task for mice. - Uchenna Akujuobi, Jun Chen, Mohamed Elhoseiny, Michael Spranger, Xiangliang Zhang:
Temporal Positive-unlabeled Learning for Biomedical Hypothesis Generation via Risk Estimation. - Marcin Tomczak, Siddharth Swaroop, Richard E. Turner:
Efficient Low Rank Gaussian Variational Inference for Neural Networks. - Borja Balle, Peter Kairouz, Brendan McMahan, Om Dipakbhai Thakkar, Abhradeep Thakurta:
Privacy Amplification via Random Check-Ins. - Andy Shih, Stefano Ermon:
Probabilistic Circuits for Variational Inference in Discrete Graphical Models. - Naveen Venkat, Jogendra Nath Kundu, Durgesh Kumar Singh, Ambareesh Revanur, Venkatesh Babu R.:
Your Classifier can Secretly Suffice Multi-Source Domain Adaptation. - Yuki Markus Asano, Mandela Patrick, Christian Rupprecht, Andrea Vedaldi:
Labelling unlabelled videos from scratch with multi-modal self-supervision. - Anna Korba, Adil Salim, Michael Arbel, Giulia Luise, Arthur Gretton:
A Non-Asymptotic Analysis for Stein Variational Gradient Descent. - Weihao Kong, Raghav Somani, Sham M. Kakade, Sewoong Oh:
Robust Meta-learning for Mixed Linear Regression with Small Batches. - Andrew Gordon Wilson, Pavel Izmailov:
Bayesian Deep Learning and a Probabilistic Perspective of Generalization. - Dimitrios Mallis, Enrique Sanchez, Matthew Bell, Georgios Tzimiropoulos:
Unsupervised Learning of Object Landmarks via Self-Training Correspondence. - Yue Li, Ilmun Kim, Yuting Wei:
Randomized tests for high-dimensional regression: A more efficient and powerful solution. - Pedro Morgado, Yi Li, Nuno Vasconcelos:
Learning Representations from Audio-Visual Spatial Alignment. - Tewodros Amberbir Habtegebrial, Varun Jampani, Orazio Gallo, Didier Stricker:
Generative View Synthesis: From Single-view Semantics to Novel-view Images. - Jiaqi Ma, Shuangrui Ding, Qiaozhu Mei:
Towards More Practical Adversarial Attacks on Graph Neural Networks. - Ruihan Yang, Huazhe Xu, Yi Wu, Xiaolong Wang:
Multi-Task Reinforcement Learning with Soft Modularization. - Tom Heskes, Evi Sijben, Ioan Gabriel Bucur, Tom Claassen:
Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models. - Aitor Lewkowycz, Guy Gur-Ari:
On the training dynamics of deep networks with $L_2$ regularization. - Yuanhao Wang, Jian Li:
Improved Algorithms for Convex-Concave Minimax Optimization. - Jialin Yuan, Chao Chen, Fuxin Li:
Deep Variational Instance Segmentation. - Feng Liu, Xiaoming Liu:
Learning Implicit Functions for Topology-Varying Dense 3D Shape Correspondence. - Yikai Wang, Wenbing Huang, Fuchun Sun, Tingyang Xu, Yu Rong, Junzhou Huang:
Deep Multimodal Fusion by Channel Exchanging. - Mayalen Etcheverry, Clément Moulin-Frier, Pierre-Yves Oudeyer:
Hierarchically Organized Latent Modules for Exploratory Search in Morphogenetic Systems. - Silviu-Marian Udrescu, Andrew K. Tan, Jiahai Feng, Orisvaldo Neto, Tailin Wu, Max Tegmark:
AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity. - Shinji Ito, Daisuke Hatano, Hanna Sumita, Kei Takemura, Takuro Fukunaga, Naonori Kakimura, Ken-ichi Kawarabayashi:
Delay and Cooperation in Nonstochastic Linear Bandits. - David Mohlin, Josephine Sullivan, Gérald Bianchi:
Probabilistic Orientation Estimation with Matrix Fisher Distributions. - Qianyi Li, Cengiz Pehlevan:
Minimax Dynamics of Optimally Balanced Spiking Networks of Excitatory and Inhibitory Neurons. - Benjamin Rhodes, Kai Xu, Michael U. Gutmann:
Telescoping Density-Ratio Estimation. - Kaixiong Zhou, Xiao Huang, Yuening Li, Daochen Zha, Rui Chen, Xia Hu:
Towards Deeper Graph Neural Networks with Differentiable Group Normalization. - Celestine Mendler-Dünner, Juan C. Perdomo, Tijana Zrnic, Moritz Hardt:
Stochastic Optimization for Performative Prediction. - Ameesh Shah, Eric Zhan, Jennifer J. Sun, Abhinav Verma, Yisong Yue, Swarat Chaudhuri:
Learning Differentiable Programs with Admissible Neural Heuristics. - Michal Derezinski, Rajiv Khanna, Michael W. Mahoney:
Improved guarantees and a multiple-descent curve for Column Subset Selection and the Nystrom method. - Kun Zhang, Mingming Gong, Petar Stojanov, Biwei Huang, Qingsong Liu, Clark Glymour:
Domain Adaptation as a Problem of Inference on Graphical Models. - Sébastien Bubeck, Ronen Eldan, Yin Tat Lee, Dan Mikulincer:
Network size and size of the weights in memorization with two-layers neural networks. - Michael J. Curry, Ping-Yeh Chiang, Tom Goldstein, John Dickerson:
Certifying Strategyproof Auction Networks. - Kelvin Xu, Siddharth Verma, Chelsea Finn, Sergey Levine:
Continual Learning of Control Primitives : Skill Discovery via Reset-Games. - Yong-Lu Li, Xinpeng Liu, Xiaoqian Wu, Yizhuo Li, Cewu Lu:
HOI Analysis: Integrating and Decomposing Human-Object Interaction. - Meng Liu, David F. Gleich:
Strongly local p-norm-cut algorithms for semi-supervised learning and local graph clustering. - Mukund Sudarshan, Wesley Tansey, Rajesh Ranganath:
Deep Direct Likelihood Knockoffs. - Siyuan Shan, Yang Li, Junier B. Oliva:
Meta-Neighborhoods. - Shikhar Bahl, Mustafa Mukadam, Abhinav Gupta, Deepak Pathak:
Neural Dynamic Policies for End-to-End Sensorimotor Learning. - Gabriel Mahuas, Giulio Isacchini, Olivier Marre, Ulisse Ferrari, Thierry Mora:
A new inference approach for training shallow and deep generalized linear models of noisy interacting neurons. - Romain Lopez, Pierre Boyeau, Nir Yosef, Michael I. Jordan, Jeffrey Regier:
Decision-Making with Auto-Encoding Variational Bayes. - Geondo Park, June Yong Yang, Sung Ju Hwang, Eunho Yang:
Attribution Preservation in Network Compression for Reliable Network Interpretation. - Maksymilian Wojtas, Ke Chen:
Feature Importance Ranking for Deep Learning. - Aahlad Manas Puli, Adler J. Perotte, Rajesh Ranganath:
Causal Estimation with Functional Confounders. - Aviral Kumar, Sergey Levine:
Model Inversion Networks for Model-Based Optimization. - Umut Simsekli, Ozan Sener, George Deligiannidis, Murat A. Erdogdu:
Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks. - Michal Derezinski, Feynman T. Liang, Michael W. Mahoney:
Exact expressions for double descent and implicit regularization via surrogate random design. - Aounon Kumar, Alexander Levine, Soheil Feizi, Tom Goldstein:
Certifying Confidence via Randomized Smoothing. - Shuqi Yang, Xingzhe He, Bo Zhu:
Learning Physical Constraints with Neural Projections. - Serena Lutong Wang, Wenshuo Guo, Harikrishna Narasimhan, Andrew Cotter, Maya R. Gupta, Michael I. Jordan:
Robust Optimization for Fairness with Noisy Protected Groups. - Hongyuan Mei, Tom Wan, Jason Eisner:
Noise-Contrastive Estimation for Multivariate Point Processes. - Xiaotie Deng, Ron Lavi, Tao Lin, Qi Qi, Wenwei Wang, Xiang Yan:
A Game-Theoretic Analysis of the Empirical Revenue Maximization Algorithm with Endogenous Sampling. - Chandrashekar Lakshminarayanan, Amit Vikram Singh:
Neural Path Features and Neural Path Kernel : Understanding the role of gates in deep learning. - Shaojie Bai, Vladlen Koltun, J. Zico Kolter:
Multiscale Deep Equilibrium Models. - Scott Emmons, Ajay Jain, Michael Laskin, Thanard Kurutach, Pieter Abbeel, Deepak Pathak:
Sparse Graphical Memory for Robust Planning. - Andrés R. Masegosa, Stephan Sloth Lorenzen, Christian Igel, Yevgeny Seldin:
Second Order PAC-Bayesian Bounds for the Weighted Majority Vote. - Jia Li, Jianwei Yu, Jiajin Li, Honglei Zhang, Kangfei Zhao, Yu Rong, Hong Cheng, Junzhou Huang:
Dirichlet Graph Variational Autoencoder. - Mariya Toneva, Otilia Stretcu, Barnabás Póczos, Leila Wehbe, Tom M. Mitchell:
Modeling Task Effects on Meaning Representation in the Brain via Zero-Shot MEG Prediction. - Amin Parvaneh, Ehsan Abbasnejad, Damien Teney, Qinfeng Shi, Anton van den Hengel:
Counterfactual Vision-and-Language Navigation: Unravelling the Unseen. - Moran Shkolnik, Brian Chmiel, Ron Banner, Gil Shomron, Yury Nahshan, Alex M. Bronstein, Uri C. Weiser:
Robust Quantization: One Model to Rule Them All. - Sumanth Dathathri, Krishnamurthy Dvijotham, Alexey Kurakin, Aditi Raghunathan, Jonathan Uesato, Rudy Bunel, Shreya Shankar, Jacob Steinhardt, Ian J. Goodfellow, Percy Liang, Pushmeet Kohli:
Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming. - Honglin Yuan, Tengyu Ma:
Federated Accelerated Stochastic Gradient Descent. - Ananya Uppal, Shashank Singh, Barnabás Póczos:
Robust Density Estimation under Besov IPM Losses. - Franco Pellegrini, Giulio Biroli:
An analytic theory of shallow networks dynamics for hinge loss classification. - Tianyi Lin, Nhat Ho, Xi Chen, Marco Cuturi, Michael I. Jordan:
Fixed-Support Wasserstein Barycenters: Computational Hardness and Fast Algorithm. - Riccardo Spezialetti, Federico Stella, Marlon Marcon, Luciano Silva, Samuele Salti, Luigi Di Stefano:
Learning to Orient Surfaces by Self-supervised Spherical CNNs. - Rui Liu, Tianyi Wu, Barzan Mozafari:
Adam with Bandit Sampling for Deep Learning. - Maximus Mutschler, Andreas Zell:
Parabolic Approximation Line Search for DNNs. - Spencer Frei, Yuan Cao, Quanquan Gu:
Agnostic Learning of a Single Neuron with Gradient Descent. - Pierre Perrault, Etienne Boursier, Michal Valko, Vianney Perchet:
Statistical Efficiency of Thompson Sampling for Combinatorial Semi-Bandits. - Yossi Arjevani, Michael Field:
Analytic Characterization of the Hessian in Shallow ReLU Models: A Tale of Symmetry. - Matthew R. O'Shaughnessy, Gregory Canal, Marissa Connor, Christopher Rozell, Mark A. Davenport:
Generative causal explanations of black-box classifiers. - Dorian Baudry, Emilie Kaufmann, Odalric-Ambrym Maillard:
Sub-sampling for Efficient Non-Parametric Bandit Exploration. - Andrés R. Masegosa:
Learning under Model Misspecification: Applications to Variational and Ensemble methods. - Alex Tamkin, Dan Jurafsky, Noah D. Goodman:
Language Through a Prism: A Spectral Approach for Multiscale Language Representations. - Huanrui Yang, Jingyang Zhang, Hongliang Dong, Nathan Inkawhich, Andrew Gardner, Andrew Touchet, Wesley Wilkes, Heath Berry, Hai Li:
DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensembles. - Lun Wang, Qi Pang, Dawn Song:
Towards practical differentially private causal graph discovery. - Constantinos Daskalakis, Dylan J. Foster, Noah Golowich:
Independent Policy Gradient Methods for Competitive Reinforcement Learning. - Christopher Grimm, André Barreto, Satinder Singh, David Silver:
The Value Equivalence Principle for Model-Based Reinforcement Learning. - Yash Bhalgat, Yizhe Zhang, Jamie Menjay Lin, Fatih Porikli:
Structured Convolutions for Efficient Neural Network Design. - Aleksandr Ermolov, Nicu Sebe:
Latent World Models For Intrinsically Motivated Exploration. - Jingqiu Ding, Samuel B. Hopkins, David Steurer:
Estimating Rank-One Spikes from Heavy-Tailed Noise via Self-Avoiding Walks. - Ching-An Cheng, Andrey Kolobov, Alekh Agarwal:
Policy Improvement via Imitation of Multiple Oracles. - Chongli Qin, Yan Wu, Jost Tobias Springenberg, Andy Brock, Jeff Donahue, Timothy P. Lillicrap, Pushmeet Kohli:
Training Generative Adversarial Networks by Solving Ordinary Differential Equations. - Abhijith Jayakumar, Andrey Y. Lokhov, Sidhant Misra, Marc Vuffray:
Learning of Discrete Graphical Models with Neural Networks. - Yihong Chen, Zheng Zhang, Yue Cao, Liwei Wang, Stephen Lin, Han Hu:
RepPoints v2: Verification Meets Regression for Object Detection. - Zhengxiong Luo, Yan Huang, Shang Li, Liang Wang, Tieniu Tan:
Unfolding the Alternating Optimization for Blind Super Resolution. - Vasileios Charisopoulos, Austin R. Benson, Anil Damle:
Entrywise convergence of iterative methods for eigenproblems. - Nanbo Li, Cian Eastwood, Robert B. Fisher:
Learning Object-Centric Representations of Multi-Object Scenes from Multiple Views. - Junchi Yang, Siqi Zhang, Negar Kiyavash, Niao He:
A Catalyst Framework for Minimax Optimization. - Tengda Han, Weidi Xie, Andrew Zisserman:
Self-supervised Co-Training for Video Representation Learning. - Max B. Paulus, Dami Choi, Daniel Tarlow, Andreas Krause, Chris J. Maddison:
Gradient Estimation with Stochastic Softmax Tricks. - Janarthanan Rajendran, Alexander Irpan, Eric Jang:
Meta-Learning Requires Meta-Augmentation. - Paria Rashidinejad, Jiantao Jiao, Stuart Russell:
SLIP: Learning to predict in unknown dynamical systems with long-term memory. - Yue Wu, Pan Zhou, Andrew Gordon Wilson, Eric P. Xing, Zhiting Hu:
Improving GAN Training with Probability Ratio Clipping and Sample Reweighting. - Mart van Baalen, Christos Louizos, Markus Nagel, Rana Ali Amjad, Ying Wang, Tijmen Blankevoort, Max Welling:
Bayesian Bits: Unifying Quantization and Pruning. - Kuldeep S. Meel, Yash Pote, Sourav Chakraborty:
On Testing of Samplers. - Vu Nguyen, Vaden Masrani, Rob Brekelmans, Michael A. Osborne, Frank Wood:
Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective. - Wenhui Wang, Furu Wei, Li Dong, Hangbo Bao, Nan Yang, Ming Zhou:
MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers. - Yan Yan, Yi Xu, Qihang Lin, Wei Liu, Tianbao Yang:
Optimal Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization. - You Lu, Bert Huang:
Woodbury Transformations for Deep Generative Flows. - Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, Yang Shen:
Graph Contrastive Learning with Augmentations. - Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, Chelsea Finn:
Gradient Surgery for Multi-Task Learning. - Harrison Zhu, Xing Liu, Ruya Kang, Zhichao Shen, Seth R. Flaxman, François-Xavier Briol:
Bayesian Probabilistic Numerical Integration with Tree-Based Models. - Stanislav Fort, Gintare Karolina Dziugaite, Mansheej Paul, Sepideh Kharaghani, Daniel M. Roy, Surya Ganguli:
Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel. - Kexin Huang, Marinka Zitnik:
Graph Meta Learning via Local Subgraphs. - Naiqi Li, Wenjie Li, Jifeng Sun, Yinghua Gao, Yong Jiang, Shu-Tao Xia:
Stochastic Deep Gaussian Processes over Graphs. - Junsouk Choi, Robert S. Chapkin, Yang Ni:
Bayesian Causal Structural Learning with Zero-Inflated Poisson Bayesian Networks. - Benjamín Sánchez-Lengeling, Jennifer N. Wei, Brian K. Lee, Emily Reif, Peter Wang, Wesley Wei Qian, Kevin McCloskey, Lucy J. Colwell, Alexander B. Wiltschko:
Evaluating Attribution for Graph Neural Networks. - Alexander Norcliffe, Cristian Bodnar, Ben Day, Nikola Simidjievski, Pietro Lió:
On Second Order Behaviour in Augmented Neural ODEs. - Amirata Ghorbani, James Y. Zou:
Neuron Shapley: Discovering the Responsible Neurons. - Hao Wu, Jonas Köhler, Frank Noé:
Stochastic Normalizing Flows. - John T. Halloran, David M. Rocke:
GPU-Accelerated Primal Learning for Extremely Fast Large-Scale Classification. - Christopher De Sa:
Random Reshuffling is Not Always Better. - Karthikeyan Natesan Ramamurthy, Bhanukiran Vinzamuri, Yunfeng Zhang, Amit Dhurandhar:
Model Agnostic Multilevel Explanations. - Marine Le Morvan, Julie Josse, Thomas Moreau, Erwan Scornet, Gaël Varoquaux:
NeuMiss networks: differentiable programming for supervised learning with missing values. - Jiaxing Wang, Haoli Bai, Jiaxiang Wu, Xupeng Shi, Junzhou Huang, Irwin King, Michael R. Lyu, Jian Cheng:
Revisiting Parameter Sharing for Automatic Neural Channel Number Search. - Abhimanyu Dubey, Alex 'Sandy' Pentland:
Differentially-Private Federated Linear Bandits. - Qiwen Cui, Lin F. Yang:
Is Plug-in Solver Sample-Efficient for Feature-based Reinforcement Learning? - Daniel Bear, Chaofei Fan, Damian Mrowca, Yunzhu Li, Seth Alter, Aran Nayebi, Jeremy Schwartz, Li Fei-Fei, Jiajun Wu, Josh Tenenbaum, Daniel L. K. Yamins:
Learning Physical Graph Representations from Visual Scenes. - Anqi Wu, Estefany Kelly Buchanan, Matthew R. Whiteway, Michael Schartner, Guido Meijer, Jean-Paul Noel, Erica Rodriguez, Claire Everett, Amy Norovich, Evan Schaffer, Neeli Mishra, C. Daniel Salzman, Dora E. Angelaki, Andrés Bendesky, International Brain Laboratory, John P. Cunningham, Liam Paninski:
Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking. - Tomoharu Iwata, Atsutoshi Kumagai:
Meta-learning from Tasks with Heterogeneous Attribute Spaces. - Guy Blanc, Neha Gupta, Jane Lange, Li-Yang Tan:
Estimating decision tree learnability with polylogarithmic sample complexity. - Daniel M. DiPietro, Shiying Xiong, Bo Zhu:
Sparse Symplectically Integrated Neural Networks. - Nicolai Häni, Selim Engin, Jun-Jee Chao, Volkan Isler:
Continuous Object Representation Networks: Novel View Synthesis without Target View Supervision. - Thomas M. Sutter, Imant Daunhawer, Julia E. Vogt:
Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence. - Kiwon Um, Robert Brand, Yun (Raymond) Fei, Philipp Holl, Nils Thuerey:
Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers. - Ruosong Wang, Ruslan Salakhutdinov, Lin F. Yang:
Reinforcement Learning with General Value Function Approximation: Provably Efficient Approach via Bounded Eluder Dimension. - Luca Zancato, Alessandro Achille, Avinash Ravichandran, Rahul Bhotika, Stefano Soatto:
Predicting Training Time Without Training. - Michael Tsang, Sirisha Rambhatla, Yan Liu:
How does This Interaction Affect Me? Interpretable Attribution for Feature Interactions. - John S. Choi, Krishan Kumar, Mohammad Khazali, Katie Wingel, Mahdi Choudhury, Adam S. Charles, Bijan Pesaran:
Optimal Adaptive Electrode Selection to Maximize Simultaneously Recorded Neuron Yield. - Greg Anderson, Abhinav Verma, Isil Dillig, Swarat Chaudhuri:
Neurosymbolic Reinforcement Learning with Formally Verified Exploration. - Jason J. Yu, Konstantinos G. Derpanis, Marcus A. Brubaker:
Wavelet Flow: Fast Training of High Resolution Normalizing Flows. - Jiachen Li, Quan Vuong, Shuang Liu, Minghua Liu, Kamil Ciosek, Henrik I. Christensen, Hao Su:
Multi-task Batch Reinforcement Learning with Metric Learning. - Josue Nassar, Piotr A. Sokól, SueYeon Chung, Kenneth D. Harris, Il Memming Park:
On 1/n neural representation and robustness. - Yaoqing Yang, Rajiv Khanna, Yaodong Yu, Amir Gholami, Kurt Keutzer, Joseph E. Gonzalez, Kannan Ramchandran, Michael W. Mahoney:
Boundary thickness and robustness in learning models. - Yu Takagi, Steven W. Kennerley, Jun-ichiro Hirayama, Laurence T. Hunt:
Demixed shared component analysis of neural population data from multiple brain areas. - Jonas M. Kübler, Wittawat Jitkrittum, Bernhard Schölkopf, Krikamol Muandet:
Learning Kernel Tests Without Data Splitting. - Qizhe Xie, Zihang Dai, Eduard H. Hovy, Thang Luong, Quoc Le:
Unsupervised Data Augmentation for Consistency Training. - Yueming Lyu, Yuan Yuan, Ivor W. Tsang:
Subgroup-based Rank-1 Lattice Quasi-Monte Carlo. - Blake E. Woodworth, Kumar Kshitij Patel, Nati Srebro:
Minibatch vs Local SGD for Heterogeneous Distributed Learning. - Virginia Aglietti, Theodoros Damoulas, Mauricio A. Álvarez, Javier González:
Multi-task Causal Learning with Gaussian Processes. - Joong-Ho Won:
Proximity Operator of the Matrix Perspective Function and its Applications. - Guanqi Zhan, Qingnan Fan, Kaichun Mo, Lin Shao, Baoquan Chen, Leonidas J. Guibas, Hao Dong:
Generative 3D Part Assembly via Dynamic Graph Learning. - Ekta Sood, Simon Tannert, Philipp Müller, Andreas Bulling:
Improving Natural Language Processing Tasks with Human Gaze-Guided Neural Attention. - Max Hopkins, Daniel Kane, Shachar Lovett:
The Power of Comparisons for Actively Learning Linear Classifiers. - Surbhi Goel, Adam R. Klivans, Frederic Koehler:
From Boltzmann Machines to Neural Networks and Back Again. - Kwang-Sung Jun, Chicheng Zhang:
Crush Optimism with Pessimism: Structured Bandits Beyond Asymptotic Optimality. - Hidenori Tanaka, Daniel Kunin, Daniel L. K. Yamins, Surya Ganguli:
Pruning neural networks without any data by iteratively conserving synaptic flow. - Zirui Liu, Qingquan Song, Kaixiong Zhou, Ting-Hsiang Wang, Ying Shan, Xia Hu:
Detecting Interactions from Neural Networks via Topological Analysis. - Aman Sinha, Matthew O'Kelly, Russ Tedrake, John C. Duchi:
Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systems. - Rohan R. Paleja, Andrew Silva, Letian Chen, Matthew C. Gombolay:
Interpretable and Personalized Apprenticeship Scheduling: Learning Interpretable Scheduling Policies from Heterogeneous User Demonstrations. - Mengdi Xu, Wenhao Ding, Jiacheng Zhu, Zuxin Liu, Baiming Chen, Ding Zhao:
Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes. - Aya Abdelsalam Ismail, Mohamed K. Gunady, Héctor Corrada Bravo, Soheil Feizi:
Benchmarking Deep Learning Interpretability in Time Series Predictions. - Andreas Grammenos, Rodrigo Mendoza-Smith, Jon Crowcroft, Cecilia Mascolo:
Federated Principal Component Analysis. - Alexander Levine, Soheil Feizi:
(De)Randomized Smoothing for Certifiable Defense against Patch Attacks. - Giannis Daras, Nikita Kitaev, Augustus Odena, Alexandros G. Dimakis:
SMYRF - Efficient Attention using Asymmetric Clustering. - Fabio De Sousa Ribeiro, Georgios Leontidis, Stefanos D. Kollias:
Introducing Routing Uncertainty in Capsule Networks. - Kevin Scaman, Ludovic Dos Santos, Merwan Barlier, Igor Colin:
A Simple and Efficient Smoothing Method for Faster Optimization and Local Exploration. - Florian Wenzel, Jasper Snoek, Dustin Tran, Rodolphe Jenatton:
Hyperparameter Ensembles for Robustness and Uncertainty Quantification. - Bailey Flanigan, Paul Gölz, Anupam Gupta, Ariel D. Procaccia:
Neutralizing Self-Selection Bias in Sampling for Sortition. - Elena Smirnova, Elvis Dohmatob:
On the Convergence of Smooth Regularized Approximate Value Iteration Schemes. - Mengjiao Yang, Ofir Nachum, Bo Dai, Lihong Li, Dale Schuurmans:
Off-Policy Evaluation via the Regularized Lagrangian. - Harm van Seijen, Hadi Nekoei, Evan Racah, Sarath Chandar:
The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learning. - Niklas Heim, Tomás Pevný, Václav Smídl:
Neural Power Units. - Jussi Viinikka, Antti Hyttinen, Johan Pensar, Mikko Koivisto:
Towards Scalable Bayesian Learning of Causal DAGs. - Ze Wang, Xiuyuan Cheng, Guillermo Sapiro, Qiang Qiu:
A Dictionary Approach to Domain-Invariant Learning in Deep Networks. - Juho Lee, Yoonho Lee, Jungtaek Kim, Eunho Yang, Sung Ju Hwang, Yee Whye Teh:
Bootstrapping neural processes. - Zhe Gan, Yen-Chun Chen, Linjie Li, Chen Zhu, Yu Cheng, Jingjing Liu:
Large-Scale Adversarial Training for Vision-and-Language Representation Learning. - Amit Daniely, Hadas Shacham:
Most ReLU Networks Suffer from $\ell^2$ Adversarial Perturbations. - Yilun Du, Shuang Li, Igor Mordatch:
Compositional Visual Generation with Energy Based Models. - David Chiang, Darcey Riley:
Factor Graph Grammars. - Nikolaos Karalias, Andreas Loukas:
Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs. - Chenlin Meng, Lantao Yu, Yang Song, Jiaming Song, Stefano Ermon:
Autoregressive Score Matching. - Michal Derezinski, Burak Bartan, Mert Pilanci, Michael W. Mahoney:
Debiasing Distributed Second Order Optimization with Surrogate Sketching and Scaled Regularization. - Patrick Kidger, James Morrill, James Foster, Terry J. Lyons:
Neural Controlled Differential Equations for Irregular Time Series. - Zheng Wen, Doina Precup, Morteza Ibrahimi, André Barreto, Benjamin Van Roy, Satinder Singh:
On Efficiency in Hierarchical Reinforcement Learning. - Wonyeol Lee, Hangyeol Yu, Xavier Rival, Hongseok Yang:
On Correctness of Automatic Differentiation for Non-Differentiable Functions. - Jonathan Wenger, Philipp Hennig:
Probabilistic Linear Solvers for Machine Learning. - Yingjie Fei, Zhuoran Yang, Zhaoran Wang, Qiaomin Xie:
Dynamic Regret of Policy Optimization in Non-Stationary Environments. - Zongyi Li, Nikola B. Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Andrew M. Stuart, Kaushik Bhattacharya, Anima Anandkumar:
Multipole Graph Neural Operator for Parametric Partial Differential Equations. - Thu Nguyen-Phuoc, Christian Richardt, Long Mai, Yong-Liang Yang, Niloy J. Mitra:
BlockGAN: Learning 3D Object-aware Scene Representations from Unlabelled Images. - Huaxiu Yao, Yingbo Zhou, Mehrdad Mahdavi, Zhenhui Li, Richard Socher, Caiming Xiong:
Online Structured Meta-learning. - Rakshit Trivedi, Hongyuan Zha:
Learning Strategic Network Emergence Games. - Wangchunshu Zhou, Jinyi Hu, Hanlin Zhang, Xiaodan Liang, Maosong Sun, Chenyan Xiong, Jian Tang:
Towards Interpretable Natural Language Understanding with Explanations as Latent Variables. - Wentao Weng, Harsh Gupta, Niao He, Lei Ying, R. Srikant:
The Mean-Squared Error of Double Q-Learning. - Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, Phillip Isola:
What Makes for Good Views for Contrastive Learning? - Jonathan Ho, Ajay Jain, Pieter Abbeel:
Denoising Diffusion Probabilistic Models. - John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato:
Barking up the right tree: an approach to search over molecule synthesis DAGs. - Lijia Zhou, Danica J. Sutherland, Nati Srebro:
On Uniform Convergence and Low-Norm Interpolation Learning. - Ziqi Liu, Zhengwei Wu, Zhiqiang Zhang, Jun Zhou, Shuang Yang, Le Song, Yuan Qi:
Bandit Samplers for Training Graph Neural Networks. - Daniele Calandriello, Michal Derezinski, Michal Valko:
Sampling from a k-DPP without looking at all items. - Bastian Rieck, Tristan Yates, Christian Bock, Karsten M. Borgwardt, Guy Wolf, Nicholas B. Turk-Browne, Smita Krishnaswamy:
Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence. - Yinuo Guo, Zeqi Lin, Jian-Guang Lou, Dongmei Zhang:
Hierarchical Poset Decoding for Compositional Generalization in Language. - Tom Yan, Christian Kroer, Alexander Peysakhovich:
Evaluating and Rewarding Teamwork Using Cooperative Game Abstractions. - Yang Li, Haidong Yi, Christopher M. Bender, Siyuan Shan, Junier B. Oliva:
Exchangeable Neural ODE for Set Modeling. - Yi Hao, Alon Orlitsky:
Profile Entropy: A Fundamental Measure for the Learnability and Compressibility of Distributions. - Qijian Zhang, Runmin Cong, Junhui Hou, Chongyi Li, Yao Zhao:
CoADNet: Collaborative Aggregation-and-Distribution Networks for Co-Salient Object Detection. - Xuchan Bao, James Lucas, Sushant Sachdeva, Roger B. Grosse:
Regularized linear autoencoders recover the principal components, eventually. - Wei Wang, Min-Ling Zhang:
Semi-Supervised Partial Label Learning via Confidence-Rated Margin Maximization. - Tiziano Portenier, Siavash Arjomand Bigdeli, Orcun Goksel:
GramGAN: Deep 3D Texture Synthesis From 2D Exemplars. - Yunhang Shen, Rongrong Ji, Zhiwei Chen, Yongjian Wu, Feiyue Huang:
UWSOD: Toward Fully-Supervised-Level Capacity Weakly Supervised Object Detection. - Guy Bresler, Rares-Darius Buhai:
Learning Restricted Boltzmann Machines with Sparse Latent Variables. - Gen Li, Yuting Wei, Yuejie Chi, Yuantao Gu, Yuxin Chen:
Sample Complexity of Asynchronous Q-Learning: Sharper Analysis and Variance Reduction. - Adel Nabli, Margarida Carvalho:
Curriculum learning for multilevel budgeted combinatorial problems. - Reese Pathak, Martin J. Wainwright:
FedSplit: an algorithmic framework for fast federated optimization. - Aude Sportisse, Claire Boyer, Julie Josse:
Estimation and Imputation in Probabilistic Principal Component Analysis with Missing Not At Random Data. - Louis Chen, Divya Padmanabhan, Chee Chin Lim, Karthik Natarajan:
Correlation Robust Influence Maximization. - Johannes Friedrich:
Neuronal Gaussian Process Regression. - Jiaxi Ying, José Vinícius de Miranda Cardoso, Daniel P. Palomar:
Nonconvex Sparse Graph Learning under Laplacian Constrained Graphical Model. - Olivier Bousquet, Roi Livni, Shay Moran:
Synthetic Data Generators - Sequential and Private. - Haoyun Wang, Liyan Xie, Alex Cuozzo, Simon Mak, Yao Xie:
Uncertainty Quantification for Inferring Hawkes Networks. - Yuguang Yue, Zhendong Wang, Mingyuan Zhou:
Implicit Distributional Reinforcement Learning. - Baifeng Shi, Judy Hoffman, Kate Saenko, Trevor Darrell, Huijuan Xu:
Auxiliary Task Reweighting for Minimum-data Learning. - Brian Hu Zhang, Tuomas Sandholm:
Small Nash Equilibrium Certificates in Very Large Games. - Arash Ardakani, Amir Ardakani, Warren J. Gross:
Training Linear Finite-State Machines. - Chicheng Zhang, Jie Shen, Pranjal Awasthi:
Efficient active learning of sparse halfspaces with arbitrary bounded noise. - Taesung Park, Jun-Yan Zhu, Oliver Wang, Jingwan Lu, Eli Shechtman, Alexei A. Efros, Richard Zhang:
Swapping Autoencoder for Deep Image Manipulation. - Charu Sharma, Manohar Kaul:
Self-Supervised Few-Shot Learning on Point Clouds. - Arun Ganesh, Kunal Talwar:
Faster Differentially Private Samplers via Rényi Divergence Analysis of Discretized Langevin MCMC. - Ding Zhou, Xue-Xin Wei:
Learning identifiable and interpretable latent models of high-dimensional neural activity using pi-VAE. - Çaglar Gülçehre, Ziyu Wang, Alexander Novikov, Thomas Paine, Sergio Gómez Colmenarejo, Konrad Zolna, Rishabh Agarwal, Josh Merel, Daniel J. Mankowitz, Cosmin Paduraru, Gabriel Dulac-Arnold, Jerry Li, Mohammad Norouzi, Matthew Hoffman, Nicolas Heess, Nando de Freitas:
RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning. - Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Jiankang Deng, Gang Niu, Masashi Sugiyama:
Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning. - Jayanta Mandi, Tias Guns:
Interior Point Solving for LP-based prediction+optimisation. - Siavash Golkar, David Lipshutz, Yanis Bahroun, Anirvan M. Sengupta, Dmitri B. Chklovskii:
A simple normative network approximates local non-Hebbian learning in the cortex. - Roman Pogodin, Peter E. Latham:
Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networks. - Seyed-Iman Mirzadeh, Mehrdad Farajtabar, Razvan Pascanu, Hassan Ghasemzadeh:
Understanding the Role of Training Regimes in Continual Learning. - Evgenii Chzhen, Christophe Denis, Mohamed Hebiri, Luca Oneto, Massimiliano Pontil:
Fair regression with Wasserstein barycenters. - Tianlong Chen, Weiyi Zhang, Jingyang Zhou, Shiyu Chang, Sijia Liu, Lisa Amini, Zhangyang Wang:
Training Stronger Baselines for Learning to Optimize. - Matt Jordan, Alexandros G. Dimakis:
Exactly Computing the Local Lipschitz Constant of ReLU Networks. - Daniel Jarrett, Ioana Bica, Mihaela van der Schaar:
Strictly Batch Imitation Learning by Energy-based Distribution Matching. - Ye He, Krishnakumar Balasubramanian, Murat A. Erdogdu:
On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Sampling Method. - Jiawei Zhang, Peijun Xiao, Ruoyu Sun, Zhi-Quan Luo:
A Single-Loop Smoothed Gradient Descent-Ascent Algorithm for Nonconvex-Concave Min-Max Problems. - Niv Pekar, Yaniv Benny, Lior Wolf:
Generating Correct Answers for Progressive Matrices Intelligence Tests. - Yurun Tian, Axel Barroso Laguna, Tony Ng, Vassileios Balntas, Krystian Mikolajczyk:
HyNet: Learning Local Descriptor with Hybrid Similarity Measure and Triplet Loss. - Kush Bhatia, Ashwin Pananjady, Peter L. Bartlett, Anca D. Dragan, Martin J. Wainwright:
Preference learning along multiple criteria: A game-theoretic perspective. - Yikai Li, Jiayuan Mao, Xiuming Zhang, Bill Freeman, Josh Tenenbaum, Noah Snavely, Jiajun Wu:
Multi-Plane Program Induction with 3D Box Priors. - Anne Draelos, John M. Pearson:
Online Neural Connectivity Estimation with Noisy Group Testing. - Haotao Wang, Tianlong Chen, Shupeng Gui, Ting-Kuei Hu, Ji Liu, Zhangyang Wang:
Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free. - Vincent Sitzmann, Julien N. P. Martel, Alexander W. Bergman, David B. Lindell, Gordon Wetzstein:
Implicit Neural Representations with Periodic Activation Functions. - Mingbao Lin, Rongrong Ji, Zihan Xu, Baochang Zhang, Yan Wang, Yongjian Wu, Feiyue Huang, Chia-Wen Lin:
Rotated Binary Neural Network. - Lorenzo Dall'Amico, Romain Couillet, Nicolas Tremblay:
Community detection in sparse time-evolving graphs with a dynamical Bethe-Hessian. - Jeremiah Z. Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax-Weiss, Balaji Lakshminarayanan:
Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness. - Govinda M. Kamath, Tavor Z. Baharav, Ilan Shomorony:
Adaptive Learning of Rank-One Models for Efficient Pairwise Sequence Alignment. - Diego Doimo, Aldo Glielmo, Alessio Ansuini, Alessandro Laio:
Hierarchical nucleation in deep neural networks. - Matthew Tancik, Pratul P. Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan T. Barron, Ren Ng:
Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains. - Shichao Zhu, Shirui Pan, Chuan Zhou, Jia Wu, Yanan Cao, Bin Wang:
Graph Geometry Interaction Learning. - Shengyu Zhao, Zhijian Liu, Ji Lin, Jun-Yan Zhu, Song Han:
Differentiable Augmentation for Data-Efficient GAN Training. - Shuhao Cui, Xuan Jin, Shuhui Wang, Yuan He, Qingming Huang:
Heuristic Domain Adaptation. - Anian Ruoss, Mislav Balunovic, Marc Fischer, Martin T. Vechev:
Learning Certified Individually Fair Representations. - Xiaobo Xia, Tongliang Liu, Bo Han, Nannan Wang, Mingming Gong, Haifeng Liu, Gang Niu, Dacheng Tao, Masashi Sugiyama:
Part-dependent Label Noise: Towards Instance-dependent Label Noise. - Jianyu Wang, Qinghua Liu, Hao Liang, Gauri Joshi, H. Vincent Poor:
Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization. - Yanli Liu, Kaiqing Zhang, Tamer Basar, Wotao Yin:
An Improved Analysis of (Variance-Reduced) Policy Gradient and Natural Policy Gradient Methods. - Orestis Plevrakis, Elad Hazan:
Geometric Exploration for Online Control. - Yunzhi Zhang, Pieter Abbeel, Lerrel Pinto:
Automatic Curriculum Learning through Value Disagreement. - Aaron Defazio, Tullie Murrell, Michael P. Recht:
MRI Banding Removal via Adversarial Training. - Heinrich Küttler, Nantas Nardelli, Alexander H. Miller, Roberta Raileanu, Marco Selvatici, Edward Grefenstette, Tim Rocktäschel:
The NetHack Learning Environment. - Yicong Hong, Cristian Rodriguez Opazo, Yuankai Qi, Qi Wu, Stephen Gould:
Language and Visual Entity Relationship Graph for Agent Navigation. - Cher Bass, Mariana da Silva, Carole H. Sudre, Petru-Daniel Tudosiu, Stephen M. Smith, Emma C. Robinson:
ICAM: Interpretable Classification via Disentangled Representations and Feature Attribution Mapping. - Zhou Fan, Zhichao Wang:
Spectra of the Conjugate Kernel and Neural Tangent Kernel for linear-width neural networks. - Andrea Celli, Alberto Marchesi, Gabriele Farina, Nicola Gatti:
No-Regret Learning Dynamics for Extensive-Form Correlated Equilibrium. - Andreas Maurer, Massimiliano Pontil:
Estimating weighted areas under the ROC curve. - Assaf Dauber, Meir Feder, Tomer Koren, Roi Livni:
Can Implicit Bias Explain Generalization? Stochastic Convex Optimization as a Case Study. - Alexander C. Li, Lerrel Pinto, Pieter Abbeel:
Generalized Hindsight for Reinforcement Learning. - Ziyu Wang, Alexander Novikov, Konrad Zolna, Josh Merel, Jost Tobias Springenberg, Scott E. Reed, Bobak Shahriari, Noah Y. Siegel, Çaglar Gülçehre, Nicolas Heess, Nando de Freitas:
Critic Regularized Regression.