NeurIPS 2019
1428 papers
A Bayesian Theory of Conformity in Collective Decision Making
Koosha Khalvati, Saghar Mirbagheri, Seongmin A. Park, Jean-Claude Dreher, Rajesh P. Rao A Coupled Autoencoder Approach for Multi-Modal Analysis of Cell Types
Rohan Gala, Nathan Gouwens, Zizhen Yao, Agata Budzillo, Osnat Penn, Bosiljka Tasic, Gabe Murphy, Hongkui Zeng, Uygar Sümbül A Domain Agnostic Measure for Monitoring and Evaluating GANs
Paulina Grnarova, Kfir Y. Levy, Aurelien Lucchi, Nathanael Perraudin, Ian Goodfellow, Thomas Hofmann, Andreas Krause A Fourier Perspective on Model Robustness in Computer Vision
Dong Yin, Raphael Gontijo Lopes, Jon Shlens, Ekin Dogus Cubuk, Justin Gilmer A Geometric Perspective on Optimal Representations for Reinforcement Learning
Marc Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taiga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle A Meta-Analysis of Overfitting in Machine Learning
Rebecca Roelofs, Vaishaal Shankar, Benjamin Recht, Sara Fridovich-Keil, Moritz Hardt, John Miller, Ludwig Schmidt A Model to Search for Synthesizable Molecules
John Bradshaw, Brooks Paige, Matt J Kusner, Marwin Segler, José Miguel Hernández-Lobato A Primal-Dual Link Between GANs and Autoencoders
Hisham Husain, Richard Nock, Robert C. Williamson A Simple Baseline for Bayesian Uncertainty in Deep Learning
Wesley J Maddox, Pavel Izmailov, Timur Garipov, Dmitry P Vetrov, Andrew Gordon Wilson A Tensorized Transformer for Language Modeling
Xindian Ma, Peng Zhang, Shuai Zhang, Nan Duan, Yuexian Hou, Ming Zhou, Dawei Song A Universally Optimal Multistage Accelerated Stochastic Gradient Method
Necdet Serhat Aybat, Alireza Fallah, Mert Gurbuzbalaban, Asuman Ozdaglar A Zero-Positive Learning Approach for Diagnosing Software Performance Regressions
Mejbah Alam, Justin Gottschlich, Nesime Tatbul, Javier S Turek, Tim Mattson, Abdullah Muzahid Accurate Uncertainty Estimation and Decomposition in Ensemble Learning
Jeremiah Liu, John Paisley, Marianthi-Anna Kioumourtzoglou, Brent Coull Accurate, Reliable and Fast Robustness Evaluation
Wieland Brendel, Jonas Rauber, Matthias Kümmerer, Ivan Ustyuzhaninov, Matthias Bethge Adaptive Cross-Modal Few-Shot Learning
Chen Xing, Negar Rostamzadeh, Boris Oreshkin, Pedro O O. Pinheiro Adaptive Density Estimation for Generative Models
Thomas Lucas, Konstantin Shmelkov, Karteek Alahari, Cordelia Schmid, Jakob Verbeek Adaptive Gradient-Based Meta-Learning Methods
Mikhail Khodak, Maria-Florina F Balcan, Ameet S Talwalkar Adaptive Sequence Submodularity
Marko Mitrovic, Ehsan Kazemi, Moran Feldman, Andreas Krause, Amin Karbasi Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates
Carlos Riquelme, Hugo Penedones, Damien Vincent, Hartmut Maennel, Sylvain Gelly, Timothy A Mann, Andre Barreto, Gergely Neu Addressing Failure Prediction by Learning Model Confidence
Charles Corbière, Nicolas Thome, Avner Bar-Hen, Matthieu Cord, Patrick Pérez Adversarial Examples Are Not Bugs, They Are Features
Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, Aleksander Madry Adversarial Music: Real World Audio Adversary Against Wake-Word Detection System
Juncheng Li, Shuhui Qu, Xinjian Li, Joseph Szurley, J. Zico Kolter, Florian Metze Adversarial Robustness Through Local Linearization
Chongli Qin, James Martens, Sven Gowal, Dilip Krishnan, Krishnamurthy Dvijotham, Alhussein Fawzi, Soham De, Robert Stanforth, Pushmeet Kohli Adversarial Training for Free!
Ali Shafahi, Mahyar Najibi, Mohammad Amin Ghiasi, Zheng Xu, John Dickerson, Christoph Studer, Larry S. Davis, Gavin Taylor, Tom Goldstein Alleviating Label Switching with Optimal Transport
Pierre Monteiller, Sebastian Claici, Edward Chien, Farzaneh Mirzazadeh, Justin M Solomon, Mikhail Yurochkin An Adaptive Nearest Neighbor Rule for Classification
Akshay Balsubramani, Sanjoy Dasgupta, Yoav Freund, Shay Moran An Algorithmic Framework for Differentially Private Data Analysis on Trusted Processors
Joshua Allen, Bolin Ding, Janardhan Kulkarni, Harsha Nori, Olga Ohrimenko, Sergey Yekhanin ANODEV2: A Coupled Neural ODE Framework
Tianjun Zhang, Zhewei Yao, Amir Gholami, Joseph E Gonzalez, Kurt Keutzer, Michael W. Mahoney, George Biros Anti-Efficient Encoding in Emergent Communication
Rahma Chaabouni, Eugene Kharitonov, Emmanuel Dupoux, Marco Baroni Approximate Inference Turns Deep Networks into Gaussian Processes
Mohammad Emtiyaz Khan, Alexander Immer, Ehsan Abedi, Maciej Korzepa Approximating Interactive Human Evaluation with Self-Play for Open-Domain Dialog Systems
Asma Ghandeharioun, Judy Hanwen Shen, Natasha Jaques, Craig Ferguson, Noah Jones, Agata Lapedriza, Rosalind Picard Approximating the Permanent by Sampling from Adaptive Partitions
Jonathan Kuck, Tri Dao, Hamid Rezatofighi, Ashish Sabharwal, Stefano Ermon Are Anchor Points Really Indispensable in Label-Noise Learning?
Xiaobo Xia, Tongliang Liu, Nannan Wang, Bo Han, Chen Gong, Gang Niu, Masashi Sugiyama Are Disentangled Representations Helpful for Abstract Visual Reasoning?
Sjoerd van Steenkiste, Francesco Locatello, Jürgen Schmidhuber, Olivier Bachem Are Labels Required for Improving Adversarial Robustness?
Jean-Baptiste Alayrac, Jonathan Uesato, Po-Sen Huang, Alhussein Fawzi, Robert Stanforth, Pushmeet Kohli Attribution-Based Confidence Metric for Deep Neural Networks
Susmit Jha, Sunny Raj, Steven Fernandes, Sumit K Jha, Somesh Jha, Brian Jalaian, Gunjan Verma, Ananthram Swami Augmented Neural ODEs
Emilien Dupont, Arnaud Doucet, Yee Whye Teh Backpropagation-Friendly Eigendecomposition
Wei Wang, Zheng Dang, Yinlin Hu, Pascal Fua, Mathieu Salzmann Bandits with Feedback Graphs and Switching Costs
Raman Arora, Teodor Vanislavov Marinov, Mehryar Mohri Batched Multi-Armed Bandits Problem
Zijun Gao, Yanjun Han, Zhimei Ren, Zhengqing Zhou Bayesian Batch Active Learning as Sparse Subset Approximation
Robert Pinsler, Jonathan Gordon, Eric Nalisnick, José Miguel Hernández-Lobato Bayesian Joint Estimation of Multiple Graphical Models
Lingrui Gan, Xinming Yang, Naveen Narisetty, Feng Liang Bayesian Layers: A Module for Neural Network Uncertainty
Dustin Tran, Mike Dusenberry, Mark van der Wilk, Danijar Hafner Bayesian Learning of Sum-Product Networks
Martin Trapp, Robert Peharz, Hong Ge, Franz Pernkopf, Zoubin Ghahramani Bayesian Optimization with Unknown Search Space
Huong Ha, Santu Rana, Sunil Gupta, Thanh Nguyen, Hung Tran-The, Svetha Venkatesh BehaveNet: Nonlinear Embedding and Bayesian Neural Decoding of Behavioral Videos
Eleanor Batty, Matthew Whiteway, Shreya Saxena, Dan Biderman, Taiga Abe, Simon Musall, Winthrop Gillis, Jeffrey Markowitz, Anne Churchland, John P. Cunningham, Sandeep R Datta, Scott Linderman, Liam Paninski Better Exploration with Optimistic Actor Critic
Kamil Ciosek, Quan Vuong, Robert Loftin, Katja Hofmann Bias Correction of Learned Generative Models Using Likelihood-Free Importance Weighting
Aditya Grover, Jiaming Song, Ashish Kapoor, Kenneth Tran, Alekh Agarwal, Eric J Horvitz, Stefano Ermon Biases for Emergent Communication in Multi-Agent Reinforcement Learning
Tom Eccles, Yoram Bachrach, Guy Lever, Angeliki Lazaridou, Thore Graepel Blended Matching Pursuit
Cyrille Combettes, Sebastian Pokutta Blocking Bandits
Soumya Basu, Rajat Sen, Sujay Sanghavi, Sanjay Shakkottai Bootstrapping Upper Confidence Bound
Botao Hao, Yasin Abbasi Yadkori, Zheng Wen, Guang Cheng Brain-like Object Recognition with High-Performing Shallow Recurrent ANNs
Jonas Kubilius, Martin Schrimpf, Kohitij Kar, Rishi Rajalingham, Ha Hong, Najib Majaj, Elias Issa, Pouya Bashivan, Jonathan Prescott-Roy, Kailyn Schmidt, Aran Nayebi, Daniel Bear, Daniel L Yamins, James J DiCarlo Breaking the Glass Ceiling for Embedding-Based Classifiers for Large Output Spaces
Chuan Guo, Ali Mousavi, Xiang Wu, Daniel N Holtmann-Rice, Satyen Kale, Sashank Reddi, Sanjiv Kumar Budgeted Reinforcement Learning in Continuous State Space
Nicolas Carrara, Edouard Leurent, Romain Laroche, Tanguy Urvoy, Odalric-Ambrym Maillard, Olivier Pietquin Calculating Optimistic Likelihoods Using (Geodesically) Convex Optimization
Viet Anh Nguyen, Soroosh Shafieezadeh-Abadeh, Man-Chung Yue, Daniel Huhn, Wolfram Wiesemann Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift
Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, D. Sculley, Sebastian Nowozin, Joshua Dillon, Balaji Lakshminarayanan, Jasper Snoek Capacity Bounded Differential Privacy
Kamalika Chaudhuri, Jacob Imola, Ashwin Machanavajjhala Categorized Bandits
Matthieu Jedor, Vianney Perchet, Jonathan Louedec Causal Confusion in Imitation Learning
Pim de Haan, Dinesh Jayaraman, Sergey Levine Certifying Geometric Robustness of Neural Networks
Mislav Balunovic, Maximilian Baader, Gagandeep Singh, Timon Gehr, Martin Vechev Channel Gating Neural Networks
Weizhe Hua, Yuan Zhou, Christopher M De Sa, Zhiru Zhang, G. Edward Suh Chasing Ghosts: Instruction Following as Bayesian State Tracking
Peter Anderson, Ayush Shrivastava, Devi Parikh, Dhruv Batra, Stefan Lee Chirality Nets for Human Pose Regression
Raymond Yeh, Yuan-Ting Hu, Alexander Schwing Coda: An End-to-End Neural Program Decompiler
Cheng Fu, Huili Chen, Haolan Liu, Xinyun Chen, Yuandong Tian, Farinaz Koushanfar, Jishen Zhao Communication-Efficient Distributed SGD with Sketching
Nikita Ivkin, Daniel Rothchild, Enayat Ullah, Vladimir Braverman, Ion Stoica, Raman Arora Compacting, Picking and Growing for Unforgetting Continual Learning
Ching-Yi Hung, Cheng-Hao Tu, Cheng-En Wu, Chien-Hung Chen, Yi-Ming Chan, Chu-Song Chen Competitive Gradient Descent
Florian Schaefer, Anima Anandkumar Compiler Auto-Vectorization with Imitation Learning
Charith Mendis, Cambridge Yang, Yewen Pu, Dr.Saman Amarasinghe, Michael Carbin Complexity of Highly Parallel Non-Smooth Convex Optimization
Sebastien Bubeck, Qijia Jiang, Yin-Tat Lee, Yuanzhi Li, Aaron Sidford Compositional De-Attention Networks
Yi Tay, Anh Tuan Luu, Aston Zhang, Shuohang Wang, Siu Cheung Hui Compositional Plan Vectors
Coline Devin, Daniel Geng, Pieter Abbeel, Trevor Darrell, Sergey Levine Computational Mirrors: Blind Inverse Light Transport by Deep Matrix Factorization
Miika Aittala, Prafull Sharma, Lukas Murmann, Adam Yedidia, Gregory Wornell, Bill Freeman, Fredo Durand Conformal Prediction Under Covariate Shift
Ryan J Tibshirani, Rina Foygel Barber, Emmanuel Candes, Aaditya Ramdas Conformalized Quantile Regression
Yaniv Romano, Evan Patterson, Emmanuel Candes Constrained Reinforcement Learning Has Zero Duality Gap
Santiago Paternain, Luiz Chamon, Miguel Calvo-Fullana, Alejandro Ribeiro Contextual Bandits with Cross-Learning
Santiago Balseiro, Negin Golrezaei, Mohammad Mahdian, Vahab Mirrokni, Jon Schneider Continual Unsupervised Representation Learning
Dushyant Rao, Francesco Visin, Andrei Rusu, Razvan Pascanu, Yee Whye Teh, Raia Hadsell Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders
Emile Mathieu, Charline Le Lan, Chris J. Maddison, Ryota Tomioka, Yee Whye Teh Control What You Can: Intrinsically Motivated Task-Planning Agent
Sebastian Blaes, Marin Vlastelica Pogančić, Jiajie Zhu, Georg Martius Controllable Text-to-Image Generation
Bowen Li, Xiaojuan Qi, Thomas Lukasiewicz, Philip Torr Controlling Neural Level Sets
Matan Atzmon, Niv Haim, Lior Yariv, Ofer Israelov, Haggai Maron, Yaron Lipman Convergence of Adversarial Training in Overparametrized Neural Networks
Ruiqi Gao, Tianle Cai, Haochuan Li, Cho-Jui Hsieh, Liwei Wang, Jason Lee Convolution with Even-Sized Kernels and Symmetric Padding
Shuang Wu, Guanrui Wang, Pei Tang, Feng Chen, Luping Shi Copula-like Variational Inference
Marcel Hirt, Petros Dellaportas, Alain Durmus Coresets for Clustering with Fairness Constraints
Lingxiao Huang, Shaofeng Jiang, Nisheeth Vishnoi Correlation Clustering with Adaptive Similarity Queries
Marco Bressan, Nicolò Cesa-Bianchi, Andrea Paudice, Fabio Vitale Correlation Clustering with Local Objectives
Sanchit Kalhan, Konstantin Makarychev, Timothy Zhou Cost Effective Active Search
Shali Jiang, Roman Garnett, Benjamin Moseley CPM-Nets: Cross Partial Multi-View Networks
Changqing Zhang, Zongbo Han, Yajie Cui, Huazhu Fu, Joey Tianyi Zhou, Qinghua Hu Cross Attention Network for Few-Shot Classification
Ruibing Hou, Hong Chang, Bingpeng Ma, Shiguang Shan, Xilin Chen Cross-Channel Communication Networks
Jianwei Yang, Zhile Ren, Chuang Gan, Hongyuan Zhu, Devi Parikh Cross-Domain Transferability of Adversarial Perturbations
Muhammad Muzammal Naseer, Salman H Khan, Muhammad Haris Khan, Fahad Shahbaz Khan, Fatih Porikli Cross-Modal Learning with Adversarial Samples
Chao Li, Shangqian Gao, Cheng Deng, De Xie, Wei Liu Curriculum-Guided Hindsight Experience Replay
Meng Fang, Tianyi Zhou, Yali Du, Lei Han, Zhengyou Zhang Curvilinear Distance Metric Learning
Shuo Chen, Lei Luo, Jian Yang, Chen Gong, Jun Li, Heng Huang D-VAE: A Variational Autoencoder for Directed Acyclic Graphs
Muhan Zhang, Shali Jiang, Zhicheng Cui, Roman Garnett, Yixin Chen Dancing to Music
Hsin-Ying Lee, Xiaodong Yang, Ming-Yu Liu, Ting-Chun Wang, Yu-Ding Lu, Ming-Hsuan Yang, Jan Kautz Data Cleansing for Models Trained with SGD
Satoshi Hara, Atsushi Nitanda, Takanori Maehara Data-Driven Estimation of Sinusoid Frequencies
Gautier Izacard, Sreyas Mohan, Carlos Fernandez-Granda DATA: Differentiable ArchiTecture Approximation
Jianlong Chang, Xinbang Zhang, Yiwen Guo, Gaofeng Meng, Shiming Xiang, Chunhong Pan Decentralized Cooperative Stochastic Bandits
David Martínez-Rubio, Varun Kanade, Patrick Rebeschini Decentralized Sketching of Low Rank Matrices
Rakshith Sharma Srinivasa, Kiryung Lee, Marius Junge, Justin Romberg Deep Equilibrium Models
Shaojie Bai, J. Zico Kolter, Vladlen Koltun Deep Gamblers: Learning to Abstain with Portfolio Theory
Ziyin Liu, Zhikang Wang, Paul Pu Liang, Ruslan Salakhutdinov, Louis-Philippe Morency, Masahito Ueda Deep Generative Video Compression
Salvator Lombardo, Jun Han, Christopher Schroers, Stephan Mandt Deep Leakage from Gradients
Ligeng Zhu, Zhijian Liu, Song Han Deep Learning Without Weight Transport
Mohamed Akrout, Collin Wilson, Peter Humphreys, Timothy Lillicrap, Douglas B Tweed Deep Model Transferability from Attribution Maps
Jie Song, Yixin Chen, Xinchao Wang, Chengchao Shen, Mingli Song Deep Multi-State Dynamic Recurrent Neural Networks Operating on Wavelet Based Neural Features for Robust Brain Machine Interfaces
Benyamin Allahgholizadeh Haghi, Spencer Kellis, Sahil Shah, Maitreyi Ashok, Luke Bashford, Daniel Kramer, Brian Lee, Charles Liu, Richard Andersen, Azita Emami Deep Random Splines for Point Process Intensity Estimation of Neural Population Data
Gabriel Loaiza-Ganem, Sean Perkins, Karen Schroeder, Mark Churchland, John P. Cunningham Deep Set Prediction Networks
Yan Zhang, Jonathon Hare, Adam Prugel-Bennett Deep Signature Transforms
Patrick Kidger, Patric Bonnier, Imanol Perez Arribas, Cristopher Salvi, Terry Lyons DeepUSPS: Deep Robust Unsupervised Saliency Prediction via Self-Supervision
Tam Nguyen, Maximilian Dax, Chaithanya Kumar Mummadi, Nhung Ngo, Thi Hoai Phuong Nguyen, Zhongyu Lou, Thomas Brox Defending Against Neural Fake News
Rowan Zellers, Ari Holtzman, Hannah Rashkin, Yonatan Bisk, Ali Farhadi, Franziska Roesner, Yejin Choi Detecting Overfitting via Adversarial Examples
Roman Werpachowski, András György, Csaba Szepesvari DetNAS: Backbone Search for Object Detection
Yukang Chen, Tong Yang, Xiangyu Zhang, Gaofeng Meng, Xinyu Xiao, Jian Sun Diffeomorphic Temporal Alignment Nets
Ron A Shapira Weber, Matan Eyal, Nicki Skafte, Oren Shriki, Oren Freifeld Differentiable Convex Optimization Layers
Akshay Agrawal, Brandon Amos, Shane Barratt, Stephen Boyd, Steven Diamond, J. Zico Kolter Differentially Private Covariance Estimation
Kareem Amin, Travis Dick, Alex Kulesza, Andres Munoz, Sergei Vassilvitskii Differentially Private Distributed Data Summarization Under Covariate Shift
Kanthi Sarpatwar, Karthikeyan Shanmugam, Venkata Sitaramagiridharganesh Ganapavarapu, Ashish Jagmohan, Roman Vaculin Differentially Private Markov Chain Monte Carlo
Mikko Heikkilä, Joonas Jälkö, Onur Dikmen, Antti Honkela Diffusion Improves Graph Learning
Johannes Gasteiger, Stefan Weißenberger, Stephan Günnemann Discovering Neural Wirings
Mitchell Wortsman, Ali Farhadi, Mohammad Rastegari Discovery of Useful Questions as Auxiliary Tasks
Vivek Veeriah, Matteo Hessel, Zhongwen Xu, Janarthanan Rajendran, Richard L. Lewis, Junhyuk Oh, Hado P van Hasselt, David Silver, Satinder Singh Discrete Flows: Invertible Generative Models of Discrete Data
Dustin Tran, Keyon Vafa, Kumar Agrawal, Laurent Dinh, Ben Poole Discrete Object Generation with Reversible Inductive Construction
Ari Seff, Wenda Zhou, Farhan Damani, Abigail Doyle, Ryan P. Adams Discriminative Topic Modeling with Logistic LDA
Iryna Korshunova, Hanchen Xiong, Mateusz Fedoryszak, Lucas Theis Disentangled Behavioural Representations
Amir Dezfouli, Hassan Ashtiani, Omar Ghattas, Richard Nock, Peter Dayan, Cheng Soon Ong Disentangling Influence: Using Disentangled Representations to Audit Model Predictions
Charles Marx, Richard Phillips, Sorelle Friedler, Carlos Scheidegger, Suresh Venkatasubramanian DiskANN: Fast Accurate Billion-Point Nearest Neighbor Search on a Single Node
Suhas Jayaram Subramanya, Fnu Devvrit, Harsha Vardhan Simhadri, Ravishankar Krishnawamy, Rohan Kadekodi Distributed Low-Rank Matrix Factorization with Exact Consensus
Zhihui Zhu, Qiuwei Li, Xinshuo Yang, Gongguo Tang, Michael B Wakin Distributional Reward Decomposition for Reinforcement Learning
Zichuan Lin, Li Zhao, Derek Yang, Tao Qin, Tie-Yan Liu, Guangwen Yang Divergence-Augmented Policy Optimization
Qing Wang, Yingru Li, Jiechao Xiong, Tong Zhang DM2C: Deep Mixed-Modal Clustering
Yangbangyan Jiang, Qianqian Xu, Zhiyong Yang, Xiaochun Cao, Qingming Huang Doubly-Robust Lasso Bandit
Gi-Soo Kim, Myunghee Cho Paik Drill-Down: Interactive Retrieval of Complex Scenes Using Natural Language Queries
Fuwen Tan, Paola Cascante-Bonilla, Xiaoxiao Guo, Hui Wu, Song Feng, Vicente Ordonez DRUM: End-to-End Differentiable Rule Mining on Knowledge Graphs
Ali Sadeghian, Mohammadreza Armandpour, Patrick Ding, Daisy Zhe Wang DTWNet: A Dynamic Time Warping Network
Xingyu Cai, Tingyang Xu, Jinfeng Yi, Junzhou Huang, Sanguthevar Rajasekaran E2-Train: Training State-of-the-Art CNNs with over 80% Energy Savings
Yue Wang, Ziyu Jiang, Xiaohan Chen, Pengfei Xu, Yang Zhao, Yingyan Lin, Zhangyang Wang Efficient Algorithms for Smooth Minimax Optimization
Kiran K Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh Efficient and Thrifty Voting by Any Means Necessary
Debmalya Mandal, Ariel D Procaccia, Nisarg Shah, David Woodruff Efficient Characterization of Electrically Evoked Responses for Neural Interfaces
Nishal Shah, Sasidhar Madugula, Pawel Hottowy, Alexander Sher, Alan Litke, Liam Paninski, E. J. Chichilnisky Efficient Deep Approximation of GMMs
Shirin Jalali, Carl Nuzman, Iraj Saniee Efficient Forward Architecture Search
Hanzhang Hu, John Langford, Rich Caruana, Saurajit Mukherjee, Eric J Horvitz, Debadeepta Dey Efficient Graph Generation with Graph Recurrent Attention Networks
Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, Will Hamilton, David K. Duvenaud, Raquel Urtasun, Richard Zemel Efficient Meta Learning via Minibatch Proximal Update
Pan Zhou, Xiaotong Yuan, Huan Xu, Shuicheng Yan, Jiashi Feng Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
Atilim Gunes Baydin, Lei Shao, Wahid Bhimji, Lukas Heinrich, Saeid Naderiparizi, Andreas Munk, Jialin Liu, Bradley Gram-Hansen, Gilles Louppe, Lawrence Meadows, Philip Torr, Victor Lee, Kyle Cranmer, Mr. Prabhat, Frank Wood Efficient Pure Exploration in Adaptive Round Model
Tianyuan Jin, Jieming Shi, Xiaokui Xiao, Enhong Chen Efficient Rematerialization for Deep Networks
Ravi Kumar, Manish Purohit, Zoya Svitkina, Erik Vee, Joshua Wang Efficient Symmetric Norm Regression via Linear Sketching
Zhao Song, Ruosong Wang, Lin Yang, Hongyang Zhang, Peilin Zhong Efficiently Learning Fourier Sparse Set Functions
Andisheh Amrollahi, Amir Zandieh, Michael Kapralov, Andreas Krause Embedding Symbolic Knowledge into Deep Networks
Yaqi Xie, Ziwei Xu, Mohan S Kankanhalli, Kuldeep S Meel, Harold Soh End to End Learning and Optimization on Graphs
Bryan Wilder, Eric Ewing, Bistra Dilkina, Milind Tambe Envy-Free Classification
Maria-Florina F Balcan, Travis Dick, Ritesh Noothigattu, Ariel D Procaccia Episodic Memory in Lifelong Language Learning
Cyprien de Masson d'Autume, Sebastian Ruder, Lingpeng Kong, Dani Yogatama Equal Opportunity in Online Classification with Partial Feedback
Yahav Bechavod, Katrina Ligett, Aaron Roth, Bo Waggoner, Steven Z. Wu Equipping Experts/Bandits with Long-Term Memory
Kai Zheng, Haipeng Luo, Ilias Diakonikolas, Liwei Wang Equitable Stable Matchings in Quadratic Time
Nikolaos Tziavelis, Ioannis Giannakopoulos, Katerina Doka, Nectarios Koziris, Panagiotis Karras Estimating Entropy of Distributions in Constant Space
Jayadev Acharya, Sourbh Bhadane, Piotr Indyk, Ziteng Sun ETNet: Error Transition Network for Arbitrary Style Transfer
Chunjin Song, Zhijie Wu, Yang Zhou, Minglun Gong, Hui Huang Evaluating Protein Transfer Learning with TAPE
Roshan Rao, Nicholas Bhattacharya, Neil Thomas, Yan Duan, Peter Chen, John Canny, Pieter Abbeel, Yun Song Exact Combinatorial Optimization with Graph Convolutional Neural Networks
Maxime Gasse, Didier Chetelat, Nicola Ferroni, Laurent Charlin, Andrea Lodi Exact Gaussian Processes on a Million Data Points
Ke Wang, Geoff Pleiss, Jacob Gardner, Stephen Tyree, Kilian Q. Weinberger, Andrew Gordon Wilson Exact Rate-Distortion in Autoencoders via Echo Noise
Rob Brekelmans, Daniel Moyer, Aram Galstyan, Greg Ver Steeg Experience Replay for Continual Learning
David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy Lillicrap, Gregory Wayne Explaining Landscape Connectivity of Low-Cost Solutions for Multilayer Nets
Rohith Kuditipudi, Xiang Wang, Holden Lee, Yi Zhang, Zhiyuan Li, Wei Hu, Rong Ge, Sanjeev Arora Explanations Can Be Manipulated and Geometry Is to Blame
Ann-Kathrin Dombrowski, Maximillian Alber, Christopher Anders, Marcel Ackermann, Klaus-Robert Müller, Pan Kessel Exploration via Hindsight Goal Generation
Zhizhou Ren, Kefan Dong, Yuan Zhou, Qiang Liu, Jian Peng Exploring Algorithmic Fairness in Robust Graph Covering Problems
Aida Rahmattalabi, Phebe Vayanos, Anthony Fulginiti, Eric Rice, Bryan Wilder, Amulya Yadav, Milind Tambe Exponential Family Estimation via Adversarial Dynamics Embedding
Bo Dai, Zhen Liu, Hanjun Dai, Niao He, Arthur Gretton, Le Song, Dale Schuurmans Fair Algorithms for Clustering
Suman Bera, Deeparnab Chakrabarty, Nicolas Flores, Maryam Negahbani Fast AutoAugment
Sungbin Lim, Ildoo Kim, Taesup Kim, Chiheon Kim, Sungwoong Kim Fast Sparse Group Lasso
Yasutoshi Ida, Yasuhiro Fujiwara, Hisashi Kashima Fast Structure Learning with Modular Regularization
Greg Ver Steeg, Hrayr Harutyunyan, Daniel Moyer, Aram Galstyan Fast Structured Decoding for Sequence Models
Zhiqing Sun, Zhuohan Li, Haoqing Wang, Di He, Zi Lin, Zhihong Deng FastSpeech: Fast, Robust and Controllable Text to Speech
Yi Ren, Yangjun Ruan, Xu Tan, Tao Qin, Sheng Zhao, Zhou Zhao, Tie-Yan Liu Few-Shot Video-to-Video Synthesis
Ting-Chun Wang, Ming-Yu Liu, Andrew Tao, Guilin Liu, Bryan Catanzaro, Jan Kautz Finding Friend and Foe in Multi-Agent Games
Jack Serrino, Max Kleiman-Weiner, David C. Parkes, Josh Tenenbaum First Order Motion Model for Image Animation
Aliaksandr Siarohin, Stéphane Lathuilière, Sergey Tulyakov, Elisa Ricci, Nicu Sebe Fisher Efficient Inference of Intractable Models
Song Liu, Takafumi Kanamori, Wittawat Jitkrittum, Yu Chen Fixing the Train-Test Resolution Discrepancy
Hugo Touvron, Andrea Vedaldi, Matthijs Douze, Herve Jegou Focused Quantization for Sparse CNNs
Yiren Zhao, Xitong Gao, Daniel Bates, Robert Mullins, Cheng-Zhong Xu Foundations of Comparison-Based Hierarchical Clustering
Debarghya Ghoshdastidar, Michaël Perrot, Ulrike von Luxburg From Complexity to Simplicity: Adaptive ES-Active Subspaces for Blackbox Optimization
Krzysztof M Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Vikas Sindhwani Fully Dynamic Consistent Facility Location
Vincent Cohen-Addad, Niklas Oskar D Hjuler, Nikos Parotsidis, David Saulpic, Chris Schwiegelshohn Function-Space Distributions over Kernels
Gregory Benton, Wesley J Maddox, Jayson Salkey, Julio Albinati, Andrew Gordon Wilson Functional Adversarial Attacks
Cassidy Laidlaw, Soheil Feizi G2SAT: Learning to Generate SAT Formulas
Jiaxuan You, Haoze Wu, Clark Barrett, Raghuram Ramanujan, Jure Leskovec Game Design for Eliciting Distinguishable Behavior
Fan Yang, Liu Leqi, Yifan Wu, Zachary Lipton, Pradeep K Ravikumar, Tom M. Mitchell, William W. Cohen Generalization Bounds in the Predict-Then-Optimize Framework
Othman El Balghiti, Adam N. Elmachtoub, Paul Grigas, Ambuj Tewari Generalization in Generative Adversarial Networks: A Novel Perspective from Privacy Protection
Bingzhe Wu, Shiwan Zhao, Chaochao Chen, Haoyang Xu, Li Wang, Xiaolu Zhang, Guangyu Sun, Jun Zhou Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck
Maximilian Igl, Kamil Ciosek, Yingzhen Li, Sebastian Tschiatschek, Cheng Zhang, Sam Devlin, Katja Hofmann Generalization of Reinforcement Learners with Working and Episodic Memory
Meire Fortunato, Melissa Tan, Ryan Faulkner, Steven Hansen, Adrià Puigdomènech Badia, Gavin Buttimore, Charles Deck, Joel Z. Leibo, Charles Blundell Generalized Off-Policy Actor-Critic
Shangtong Zhang, Wendelin Boehmer, Shimon Whiteson Generalized Sliced Wasserstein Distances
Soheil Kolouri, Kimia Nadjahi, Umut Simsekli, Roland Badeau, Gustavo Rohde Generative Models for Graph-Based Protein Design
John Ingraham, Vikas Garg, Regina Barzilay, Tommi Jaakkola Geometry-Aware Neural Rendering
Joshua Tobin, Wojciech Zaremba, Pieter Abbeel Global Sparse Momentum SGD for Pruning Very Deep Neural Networks
Xiaohan Ding, Guiguang Ding, Xiangxin Zhou, Yuchen Guo, Jungong Han, Ji Liu Glyce: Glyph-Vectors for Chinese Character Representations
Yuxian Meng, Wei Wu, Fei Wang, Xiaoya Li, Ping Nie, Fan Yin, Muyu Li, Qinghong Han, Xiaofei Sun, Jiwei Li GNNExplainer: Generating Explanations for Graph Neural Networks
Zhitao Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, Jure Leskovec Goal-Conditioned Imitation Learning
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Weijiang Yu, Jingwen Zhou, Weihao Yu, Xiaodan Liang, Nong Xiao Hierarchical Optimal Transport for Document Representation
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Qi Liu, Maximilian Nickel, Douwe Kiela HyperGCN: A New Method for Training Graph Convolutional Networks on Hypergraphs
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Wenbo Gong, Sebastian Tschiatschek, Sebastian Nowozin, Richard E Turner, José Miguel Hernández-Lobato, Cheng Zhang Image Captioning: Transforming Objects into Words
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Qi Lei, Ajil Jalal, Inderjit S Dhillon, Alexandros G Dimakis iSplit LBI: Individualized Partial Ranking with Ties via Split LBI
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Jaemin Yoo, Minyong Cho, Taebum Kim, U Kang Landmark Ordinal Embedding
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Antreas Antoniou, Amos J. Storkey Learning to Optimize in Swarms
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Eunbyung Park, Junier B Oliva Meta-Learning with Implicit Gradients
Aravind Rajeswaran, Chelsea Finn, Sham M. Kakade, Sergey Levine Meta-Surrogate Benchmarking for Hyperparameter Optimization
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Chengzhi Mao, Ziyuan Zhong, Junfeng Yang, Carl Vondrick, Baishakhi Ray Minimum Stein Discrepancy Estimators
Alessandro Barp, Francois-Xavier Briol, Andrew Duncan, Mark Girolami, Lester Mackey Mining GOLD Samples for Conditional GANs
Sangwoo Mo, Chiheon Kim, Sungwoong Kim, Minsu Cho, Jinwoo Shin MixMatch: A Holistic Approach to Semi-Supervised Learning
David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, Colin A Raffel Mixtape: Breaking the SoftMax Bottleneck Efficiently
Zhilin Yang, Thang Luong, Ruslan Salakhutdinov, Quoc V Le Model Selection for Contextual Bandits
Dylan J Foster, Akshay Krishnamurthy, Haipeng Luo Model Similarity Mitigates Test Set Overuse
Horia Mania, John Miller, Ludwig Schmidt, Moritz Hardt, Benjamin Recht Modeling Dynamic Functional Connectivity with Latent Factor Gaussian Processes
Lingge Li, Dustin Pluta, Babak Shahbaba, Norbert Fortin, Hernando Ombao, Pierre Baldi Modeling Tabular Data Using Conditional GAN
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Christian Schroeder de Witt, Jakob Foerster, Gregory Farquhar, Philip Torr, Wendelin Boehmer, Shimon Whiteson Multi-Criteria Dimensionality Reduction with Applications to Fairness
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Srinath Sridhar, Davis Rempe, Julien Valentin, Bouaziz Sofien, Leonidas Guibas muSSP: Efficient Min-Cost Flow Algorithm for Multi-Object Tracking
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Xiangyuan Zhang, Kaiqing Zhang, Erik Miehling, Tamer Basar Nonlinear Scaling of Resource Allocation in Sensory Bottlenecks
Laura Rose Edmondson, Alejandro Jimenez Rodriguez, Hannes P. Saal Nonstochastic Multiarmed Bandits with Unrestricted Delays
Tobias Sommer Thune, Nicolò Cesa-Bianchi, Yevgeny Seldin Normalization Helps Training of Quantized LSTM
Lu Hou, Jinhua Zhu, James Kwok, Fei Gao, Tao Qin, Tie-Yan Liu ObjectNet: A Large-Scale Bias-Controlled Dataset for Pushing the Limits of Object Recognition Models
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Blossom Metevier, Stephen Giguere, Sarah Brockman, Ari Kobren, Yuriy Brun, Emma Brunskill, Philip S. Thomas Offline Contextual Bayesian Optimization
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Aditya Bhaskara, Pruthuvi Maheshakya Wijewardena On Exact Computation with an Infinitely Wide Neural Net
Sanjeev Arora, Simon S Du, Wei Hu, Zhiyuan Li, Ruslan Salakhutdinov, Ruosong Wang On Fenchel Mini-Max Learning
Chenyang Tao, Liqun Chen, Shuyang Dai, Junya Chen, Ke Bai, Dong Wang, Jianfeng Feng, Wenlian Lu, Georgiy Bobashev, Lawrence Carin On Human-Aligned Risk Minimization
Liu Leqi, Adarsh Prasad, Pradeep K Ravikumar On Making Stochastic Classifiers Deterministic
Andrew Cotter, Maya Gupta, Harikrishna Narasimhan On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks
Sunil Thulasidasan, Gopinath Chennupati, Jeff A. Bilmes, Tanmoy Bhattacharya, Sarah Michalak On Relating Explanations and Adversarial Examples
Alexey Ignatiev, Nina Narodytska, Joao Marques-Silva On Robustness of Principal Component Regression
Anish Agarwal, Devavrat Shah, Dennis Shen, Dogyoon Song On Testing for Biases in Peer Review
Ivan Stelmakh, Nihar Shah, Aarti Singh On the (In)fidelity and Sensitivity of Explanations
Chih-Kuan Yeh, Cheng-Yu Hsieh, Arun Suggala, David I Inouye, Pradeep K Ravikumar On the Calibration of Multiclass Classification with Rejection
Chenri Ni, Nontawat Charoenphakdee, Junya Honda, Masashi Sugiyama On the Convergence of Single-Call Stochastic Extra-Gradient Methods
Yu-Guan Hsieh, Franck Iutzeler, Jérôme Malick, Panayotis Mertikopoulos On the Fairness of Disentangled Representations
Francesco Locatello, Gabriele Abbati, Thomas Rainforth, Stefan Bauer, Bernhard Schölkopf, Olivier Bachem On the Hardness of Robust Classification
Pascale Gourdeau, Varun Kanade, Marta Kwiatkowska, James Worrell On the Transfer of Inductive Bias from Simulation to the Real World: A New Disentanglement Dataset
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Micah Carroll, Rohin Shah, Mark K Ho, Tom Griffiths, Sanjit Seshia, Pieter Abbeel, Anca Dragan On Tractable Computation of Expected Predictions
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Rahaf Aljundi, Eugene Belilovsky, Tinne Tuytelaars, Laurent Charlin, Massimo Caccia, Min Lin, Lucas Page-Caccia Online EXP3 Learning in Adversarial Bandits with Delayed Feedback
Ilai Bistritz, Zhengyuan Zhou, Xi Chen, Nicholas Bambos, Jose Blanchet Online Learning via the Differential Privacy Lens
Jacob D. Abernethy, Young Hun Jung, Chansoo Lee, Audra McMillan, Ambuj Tewari Online Normalization for Training Neural Networks
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Giulia Denevi, Dimitris Stamos, Carlo Ciliberto, Massimiliano Pontil Optimal Analysis of Subset-Selection Based L_p Low-Rank Approximation
Chen Dan, Hong Wang, Hongyang Zhang, Yuchen Zhou, Pradeep K Ravikumar Optimal Decision Tree with Noisy Outcomes
Su Jia, Viswanath Nagarajan, Fatemeh Navidi, R Ravi Optimal Sparse Decision Trees
Xiyang Hu, Cynthia Rudin, Margo Seltzer Optimistic Distributionally Robust Optimization for Nonparametric Likelihood Approximation
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Qiyang Li, Saminul Haque, Cem Anil, James Lucas, Roger B Grosse, Joern-Henrik Jacobsen Primal-Dual Block Generalized Frank-Wolfe
Qi Lei, Jiacheng Zhuo, Constantine Caramanis, Inderjit S Dhillon, Alexandros G Dimakis Privacy Amplification by Mixing and Diffusion Mechanisms
Borja Balle, Gilles Barthe, Marco Gaboardi, Joseph Geumlek Private Hypothesis Selection
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Dinesh Garg, Shajith Ikbal, Santosh K. Srivastava, Harit Vishwakarma, Hima Karanam, L Venkata Subramaniam Quantum Wasserstein Generative Adversarial Networks
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Ciara Pike-Burke, Steffen Grunewalder Recurrent Kernel Networks
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Robin Sandkühler, Simon Andermatt, Grzegorz Bauman, Sylvia Nyilas, Christoph Jud, Philippe C. Cattin Recurrent Space-Time Graph Neural Networks
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Zhengyang Shen, Francois-Xavier Vialard, Marc Niethammer Regression Planning Networks
Danfei Xu, Roberto Martín-Martín, De-An Huang, Yuke Zhu, Silvio Savarese, Li F Fei-Fei Regret Bounds for Learning State Representations in Reinforcement Learning
Ronald Ortner, Matteo Pirotta, Alessandro Lazaric, Ronan Fruit, Odalric-Ambrym Maillard Regularized Gradient Boosting
Corinna Cortes, Mehryar Mohri, Dmitry Storcheus Regularizing Trajectory Optimization with Denoising Autoencoders
Rinu Boney, Norman Di Palo, Mathias Berglund, Alexander Ilin, Juho Kannala, Antti Rasmus, Harri Valpola Reinforcement Learning with Convex Constraints
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Taeyoung Hahn, Myeongjang Pyeon, Gunhee Kim Self-Supervised GAN: Analysis and Improvement with Multi-Class Minimax Game
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Victor Chernozhukov, Mert Demirer, Greg Lewis, Vasilis Syrgkanis Semi-Supervisedly Co-Embedding Attributed Networks
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