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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.