Advances in Neural Information Processing Systems 32 (NeurIPS 2019)

• Compositional Plan Vectors Coline Devin, Daniel Geng, Pieter Abbeel, Trevor Darrell, Sergey Levine
• Learning to Propagate for Graph Meta-Learning LU LIU, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang
• XNAS: Neural Architecture Search with Expert Advice Niv Nayman, Asaf Noy, Tal Ridnik, Itamar Friedman, Rong Jin, Lihi Zelnik
• Multi-resolution Multi-task Gaussian Processes Oliver Hamelijnck, Theodoros Damoulas, Kangrui Wang, Mark Girolami
• Deep Equilibrium Models Shaojie Bai, J. Zico Kolter, Vladlen Koltun
• Cross Attention Network for Few-shot Classification Ruibing Hou, Hong Chang, Bingpeng MA, Shiguang Shan, Xilin Chen
• Order Optimal One-Shot Distributed Learning Arsalan Sharifnassab, Saber Salehkaleybar, S. Jamaloddin Golestani
• Exact Gaussian Processes on a Million Data Points Ke Wang, Geoff Pleiss, Jacob Gardner, Stephen Tyree, Kilian Q. Weinberger, Andrew Gordon Wilson
• Asymmetric Valleys: Beyond Sharp and Flat Local Minima Haowei He, Gao Huang, Yang Yuan
• Calculating Optimistic Likelihoods Using (Geodesically) Convex Optimization Viet Anh Nguyen, Soroosh Shafieezadeh Abadeh, Man-Chung Yue, Daniel Kuhn, Wolfram Wiesemann
• Think out of the "Box": Generically-Constrained Asynchronous Composite Optimization and Hedging Pooria Joulani, András György, Csaba Szepesvari
• Improved Precision and Recall Metric for Assessing Generative Models Tuomas Kynkäänniemi, Tero Karras, Samuli Laine, Jaakko Lehtinen, Timo Aila
• A Direct tilde{O}(1/epsilon) Iteration Parallel Algorithm for Optimal Transport Arun Jambulapati, Aaron Sidford, Kevin Tian
• Zero-Shot Semantic Segmentation Maxime Bucher, Tuan-Hung VU, Matthieu Cord, Patrick Pérez
• Hyperspherical Prototype Networks Pascal Mettes, Elise van der Pol, Cees Snoek
• Lower Bounds on Adversarial Robustness from Optimal Transport Arjun Nitin Bhagoji, Daniel Cullina, Prateek Mittal
• A Nonconvex Approach for Exact and Efficient Multichannel Sparse Blind Deconvolution Qing Qu, Xiao Li, Zhihui Zhu
• 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
• DTWNet: a Dynamic Time Warping Network Xingyu Cai, Tingyang Xu, Jinfeng Yi, Junzhou Huang, Sanguthevar Rajasekaran
• Learning Mean-Field Games Xin Guo, Anran Hu, Renyuan Xu, Junzi Zhang
• Learning Erdos-Renyi Random Graphs via Edge Detecting Queries Zihan Li, Matthias Fresacher, Jonathan Scarlett
• Cormorant: Covariant Molecular Neural Networks Brandon Anderson, Truong Son Hy, Risi Kondor
• Flattening a Hierarchical Clustering through Active Learning Fabio Vitale, Anand Rajagopalan, Claudio Gentile
• Random Projections and Sampling Algorithms for Clustering of High-Dimensional Polygonal Curves Stefan Meintrup, Alexander Munteanu, Dennis Rohde
• Explicit Explore-Exploit Algorithms in Continuous State Spaces Mikael Henaff
• How degenerate is the parametrization of neural networks with the ReLU activation function? Dennis Maximilian Elbrächter, Julius Berner, Philipp Grohs
• Hyperbolic Graph Convolutional Neural Networks Ines Chami, Zhitao Ying, Christopher Ré, Jure Leskovec
• Spherical Text Embedding Yu Meng, Jiaxin Huang, Guangyuan Wang, Chao Zhang, Honglei Zhuang, Lance Kaplan, Jiawei Han
• Random Tessellation Forests Shufei Ge, Shijia Wang, Yee Whye Teh, Liangliang Wang, Lloyd Elliott
• SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers Igor Fedorov, Ryan P. Adams, Matthew Mattina, Paul Whatmough
• Capacity Bounded Differential Privacy Kamalika Chaudhuri, Jacob Imola, Ashwin Machanavajjhala
• Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates Jeffrey Negrea, Mahdi Haghifam, Gintare Karolina Dziugaite, Ashish Khisti, Daniel M. Roy
• Efficient Algorithms for Smooth Minimax Optimization Kiran K. Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh
• Uniform convergence may be unable to explain generalization in deep learning Vaishnavh Nagarajan, J. Zico Kolter
• First order expansion of convex regularized estimators Pierre Bellec, Arun Kuchibhotla
• Robust exploration in linear quadratic reinforcement learning Jack Umenberger, Mina Ferizbegovic, Thomas B. Schön, Håkan Hjalmarsson
• Modeling Uncertainty by Learning a Hierarchy of Deep Neural Connections Raanan Yehezkel Rohekar, Yaniv Gurwicz, Shami Nisimov, Gal Novik
• Meta-Surrogate Benchmarking for Hyperparameter Optimization Aaron Klein, Zhenwen Dai, Frank Hutter, Neil Lawrence, Javier Gonzalez
• Time/Accuracy Tradeoffs for Learning a ReLU with respect to Gaussian Marginals Surbhi Goel, Sushrut Karmalkar, Adam Klivans
• Bayesian Optimization under Heavy-tailed Payoffs Sayak Ray Chowdhury, Aditya Gopalan
• Distribution Learning of a Random Spatial Field with a Location-Unaware Mobile Sensor Meera Pai, Animesh Kumar
• State Aggregation Learning from Markov Transition Data Yaqi Duan, Tracy Ke, Mengdi Wang
• Reliable training and estimation of variance networks Nicki Skafte, Martin Jørgensen, Søren Hauberg
• Meta-Learning with Implicit Gradients Aravind Rajeswaran, Chelsea Finn, Sham M. Kakade, Sergey Levine
• Differentially Private Markov Chain Monte Carlo Mikko Heikkilä, Joonas Jälkö, Onur Dikmen, Antti Honkela
• Universal Boosting Variational Inference Trevor Campbell, Xinglong Li
• LIIR: Learning Individual Intrinsic Reward in Multi-Agent Reinforcement Learning Yali Du, Lei Han, Meng Fang, Ji Liu, Tianhong Dai, Dacheng Tao
• A Normative Theory for Causal Inference and Bayes Factor Computation in Neural Circuits Wenhao Zhang, Si Wu, Brent Doiron, Tai Sing Lee
• The Geometry of Deep Networks: Power Diagram Subdivision Randall Balestriero, Romain Cosentino, Behnaam Aazhang, Richard Baraniuk
• Visual Sequence Learning in Hierarchical Prediction Networks and Primate Visual Cortex
• Equal Opportunity in Online Classification with Partial Feedback Yahav Bechavod, Katrina Ligett, Aaron Roth, Bo Waggoner, Steven Z. Wu
• Semi-Parametric Efficient Policy Learning with Continuous Actions Victor Chernozhukov, Mert Demirer, Greg Lewis, Vasilis Syrgkanis
• Concentration of risk measures: A Wasserstein distance approach Sanjay P. Bhat, Prashanth L.A.
• Interior-Point Methods Strike Back: Solving the Wasserstein Barycenter Problem DongDong Ge, Haoyue Wang, Zikai Xiong, Yinyu Ye
• Coda: An End-to-End Neural Program Decompiler Cheng Fu, Huili Chen, Haolan Liu, Xinyun Chen, Yuandong Tian, Farinaz Koushanfar, Jishen Zhao
• GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism Yanping Huang, Youlong Cheng, Ankur Bapna, Orhan Firat, Dehao Chen, Mia Chen, HyoukJoong Lee, Jiquan Ngiam, Quoc V. Le, Yonghui Wu, zhifeng Chen
• Rand-NSG: Fast Accurate Billion-point Nearest Neighbor Search on a Single Node Suhas Jayaram Subramanya, Fnu Devvrit, Harsha Vardhan Simhadri, Ravishankar Krishnawamy, Rohan Kadekodi
• Linear Stochastic Bandits Under Safety Constraints Sanae Amani, Mahnoosh Alizadeh, Christos Thrampoulidis
• Power analysis of knockoff filters for correlated designs Jingbo Liu, Philippe Rigollet
• Implicitly learning to reason in first-order logic Vaishak Belle, Brendan Juba
• Low-Rank Bandit Methods for High-Dimensional Dynamic Pricing Jonas W. Mueller, Vasilis Syrgkanis, Matt Taddy
• Learning Stable Deep Dynamics Models J. Zico Kolter, Gaurav Manek
• Beyond the Single Neuron Convex Barrier for Neural Network Certification Gagandeep Singh, Rupanshu Ganvir, Markus Püschel, Martin Vechev
• Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models Yuge Shi, Siddharth N, Brooks Paige, Philip Torr
• Language as an Abstraction for Hierarchical Deep Reinforcement Learning YiDing Jiang, Shixiang (Shane) Gu, Kevin P. Murphy, Chelsea Finn
• High-dimensional multivariate forecasting with low-rank Gaussian Copula Processes David Salinas, Michael Bohlke-Schneider, Laurent Callot, Roberto Medico, Jan Gasthaus
• Learning Macroscopic Brain Connectomes via Group-Sparse Factorization Farzane Aminmansour, Andrew Patterson, Lei Le, Yisu Peng, Daniel Mitchell, Franco Pestilli, Cesar F. Caiafa, Russell Greiner, Martha White
• Optimal Sketching for Kronecker Product Regression and Low Rank Approximation Huaian Diao, Rajesh Jayaram, Zhao Song, Wen Sun, David Woodruff
• Deep Gamblers: Learning to Abstain with Portfolio Theory Ziyin Liu, Zhikang Wang, Paul Pu Liang, Russ R. Salakhutdinov, Louis-Philippe Morency, Masahito Ueda
• DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs Ali Sadeghian, Mohammadreza Armandpour, Patrick Ding, Daisy Zhe Wang
• Combinatorial Inference against Label Noise Paul Hongsuck Seo, Geeho Kim, Bohyung Han
• Localized Structured Prediction Carlo Ciliberto, Francis Bach, Alessandro Rudi
• Fast Low-rank Metric Learning for Large-scale and High-dimensional Data Han Liu, Zhizhong Han, Yu-Shen Liu, Ming Gu
• Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent Jaehoon Lee, Lechao Xiao, Samuel Schoenholz, Yasaman Bahri, Roman Novak, Jascha Sohl-Dickstein, Jeffrey Pennington
• Retrosynthesis Prediction with Conditional Graph Logic Network Hanjun Dai, Chengtao Li, Connor Coley, Bo Dai, Le Song
• Efficient Pure Exploration in Adaptive Round model Tianyuan Jin, Jieming SHI, Xiaokui Xiao, Enhong Chen
• Unsupervised Emergence of Egocentric Spatial Structure from Sensorimotor Prediction Alban Laflaquière, Michael Garcia Ortiz
• Adversarial Robustness through Local Linearization Chongli Qin, James Martens, Sven Gowal, Dilip Krishnan, Krishnamurthy Dvijotham, Alhussein Fawzi, Soham De, Robert Stanforth, Pushmeet Kohli
• Generalized Off-Policy Actor-Critic Shangtong Zhang, Wendelin Boehmer, Shimon Whiteson
• Average Individual Fairness: Algorithms, Generalization and Experiments Saeed Sharifi-Malvajerdi, Michael Kearns, Aaron Roth
• Comparing distributions: $\ell_1$ geometry improves kernel two-sample testing meyer scetbon, Gael Varoquaux
• Nonstochastic Multiarmed Bandits with Unrestricted Delays Tobias Sommer Thune, Nicolò Cesa-Bianchi, Yevgeny Seldin
• Approximate Bayesian Inference for a Mechanistic Model of Vesicle Release at a Ribbon Synapse Cornelius Schröder, Ben James, Leon Lagnado, Philipp Berens
• Data-dependent Sample Complexity of Deep Neural Networks via Lipschitz Augmentation Colin Wei, Tengyu Ma
• Semi-supervisedly Co-embedding Attributed Networks Zaiqiao Meng, Shangsong Liang, Jinyuan Fang, Teng Xiao
• Adaptive Auxiliary Task Weighting for Reinforcement Learning Xingyu Lin, Harjatin Baweja, George Kantor, David Held
• Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders Emile Mathieu, Charline Le Lan, Chris J. Maddison, Ryota Tomioka, Yee Whye Teh
• Training Image Estimators without Image Ground Truth Zhihao Xia, Ayan Chakrabarti
• On the Convergence Rate of Training Recurrent Neural Networks Zeyuan Allen-Zhu, Yuanzhi Li, Zhao Song
• Minimizers of the Empirical Risk and Risk Monotonicity Marco Loog, Tom Viering, Alexander Mey
• Factor Group-Sparse Regularization for Efficient Low-Rank Matrix Recovery Jicong Fan, Lijun Ding, Yudong Chen, Madeleine Udell
• Möbius Transformation for Fast Inner Product Search on Graph Zhixin Zhou, Shulong Tan, Zhaozhuo Xu, Ping Li
• The Label Complexity of Active Learning from Observational Data Songbai Yan, Kamalika Chaudhuri, Tara Javidi
• Hyperbolic Graph Neural Networks Qi Liu, Maximilian Nickel, Douwe Kiela
• Learning Fairness in Multi-Agent Systems Jiechuan Jiang, Zongqing Lu
• On Robustness to Adversarial Examples and Polynomial Optimization Pranjal Awasthi, Abhratanu Dutta, Aravindan Vijayaraghavan
• In-Place Zero-Space Memory Protection for CNN Hui Guan, Lin Ning, Zhen Lin, Xipeng Shen, Huiyang Zhou, Seung-Hwan Lim
• Non-Asymptotic Gap-Dependent Regret Bounds for Tabular MDPs Max Simchowitz, Kevin G. Jamieson
• 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
• Sequential Neural Processes Gautam Singh, Jaesik Yoon, Youngsung Son, Sungjin Ahn
• Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask Hattie Zhou, Janice Lan, Rosanne Liu, Jason Yosinski
• Fast and Flexible Multi-Task Classification using Conditional Neural Adaptive Processes James Requeima, Jonathan Gordon, John Bronskill, Sebastian Nowozin, Richard E. Turner
• A Simple Baseline for Bayesian Uncertainty in Deep Learning Wesley J. Maddox, Pavel Izmailov, Timur Garipov, Dmitry P. Vetrov, Andrew Gordon Wilson
• CPM-Nets: Cross Partial Multi-View Networks Changqing Zhang, Zongbo Han, yajie cui, Huazhu Fu, Joey Tianyi Zhou, Qinghua Hu
• Low-Complexity Nonparametric Bayesian Online Prediction with Universal Guarantees Alix LHERITIER, Frederic Cazals
• Finding the Needle in the Haystack with Convolutions: on the benefits of architectural bias Stéphane d'Ascoli, Levent Sagun, Giulio Biroli, Joan Bruna
• Efficiently avoiding saddle points with zero order methods: No gradients required Emmanouil-Vasileios Vlatakis-Gkaragkounis, Lampros Flokas, Georgios Piliouras
• Learning metrics for persistence-based summaries and applications for graph classification Qi Zhao, Yusu Wang
• PasteGAN: A Semi-Parametric Method to Generate Image from Scene Graph Yikang LI, Tao Ma, Yeqi Bai, Nan Duan, Sining Wei, Xiaogang Wang
• Learning Local Search Heuristics for Boolean Satisfiability Emre Yolcu, Barnabas Poczos
• Learning to Perform Local Rewriting for Combinatorial Optimization Xinyun Chen, Yuandong Tian
• A Unified Bellman Optimality Principle Combining Reward Maximization and Empowerment Felix Leibfried, Sergio Pascual-Díaz, Jordi Grau-Moya
• Learning Representations for Time Series Clustering Qianli Ma, Jiawei Zheng, Sen Li, Gary W. Cottrell
• Statistical-Computational Tradeoff in Single Index Models Lingxiao Wang, Zhuoran Yang, Zhaoran Wang
• Probabilistic Logic Neural Networks for Reasoning Meng Qu, Jian Tang
• Joint-task Self-supervised Learning for Temporal Correspondence Xueting Li, Sifei Liu, Shalini De Mello, Xiaolong Wang, Jan Kautz, Ming-Hsuan Yang
• Learning Sparse Distributions using Iterative Hard Thresholding Jacky Y. Zhang, Rajiv Khanna, Anastasios Kyrillidis, Oluwasanmi O. Koyejo
• On Distributed Averaging for Stochastic k-PCA Aditya Bhaskara, Pruthuvi Maheshakya Wijewardena
• Learning dynamic polynomial proofs Alhussein Fawzi, Mateusz Malinowski, Hamza Fawzi, Omar Fawzi
• Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based Control Sai Qian Zhang, Qi Zhang, Jieyu Lin
• Global Convergence of Gradient Descent for Deep Linear Residual Networks Lei Wu, Qingcan Wang, Chao Ma
• Dying Experts: Efficient Algorithms with Optimal Regret Bounds Hamid Shayestehmanesh, Sajjad Azami, Nishant A. Mehta
• A Bayesian Theory of Conformity in Collective Decision Making Koosha Khalvati, Saghar Mirbagheri, Seongmin A. Park, Jean-Claude Dreher, Rajesh PN Rao
• Poisson-Randomized Gamma Dynamical Systems Aaron Schein, Scott Linderman, Mingyuan Zhou, David Blei, Hanna Wallach
• Bayesian Layers: A Module for Neural Network Uncertainty Dustin Tran, Mike Dusenberry, Mark van der Wilk, Danijar Hafner
• Sequence Modeling with Unconstrained Generation Order Dmitrii Emelianenko, Elena Voita, Pavel Serdyukov
• Online Continual Learning with Maximal Interfered Retrieval Rahaf Aljundi, Eugene Belilovsky, Tinne Tuytelaars, Laurent Charlin, Massimo Caccia, Min Lin, Lucas Page-Caccia
• Visualizing and Measuring the Geometry of BERT Emily Reif, Ann Yuan, Martin Wattenberg, Fernanda B. Viegas, Andy Coenen, Adam Pearce, Been Kim
• Learning to Predict Without Looking Ahead: World Models Without Forward Prediction Daniel Freeman, David Ha, Luke Metz
• Deep Generalized Method of Moments for Instrumental Variable Analysis Andrew Bennett, Nathan Kallus, Tobias Schnabel
• Copulas as High-Dimensional Generative Models: Vine Copula Autoencoders Natasa Tagasovska, Damien Ackerer, Thibault Vatter
• Implicit Semantic Data Augmentation for Deep Networks Yulin Wang, Xuran Pan, Shiji Song, Hong Zhang, Gao Huang, Cheng Wu
• q-means: A quantum algorithm for unsupervised machine learning Iordanis Kerenidis, Jonas Landman, Alessandro Luongo, Anupam Prakash
• RUDDER: Return Decomposition for Delayed Rewards Jose A. Arjona-Medina, Michael Gillhofer, Michael Widrich, Thomas Unterthiner, Johannes Brandstetter, Sepp Hochreiter
• Learning-Based Low-Rank Approximations Piotr Indyk, Ali Vakilian, Yang Yuan
• Convergence Guarantees for Adaptive Bayesian Quadrature Methods Motonobu Kanagawa, Philipp Hennig
• A First-Order Algorithmic Framework for Distributionally Robust Logistic Regression JIAJIN LI, SEN HUANG, Anthony Man-Cho So
• Theoretical Analysis of Adversarial Learning: A Minimax Approach Zhuozhuo Tu, Jingwei Zhang, Dacheng Tao
• Compositional De-Attention Networks Yi Tay, Anh Tuan Luu, Aston Zhang, Shuohang Wang, Siu Cheung Hui
• Robust Attribution Regularization Jiefeng Chen, Xi Wu, Vaibhav Rastogi, Yingyu Liang, Somesh Jha
• Semantic-Guided Multi-Attention Localization for Zero-Shot Learning Yizhe Zhu, Jianwen Xie, Zhiqiang Tang, Xi Peng, Ahmed Elgammal
• Distributionally Robust Optimization and Generalization in Kernel Methods Matthew Staib, Stefanie Jegelka
• Kernel Instrumental Variable Regression Rahul Singh, Maneesh Sahani, Arthur Gretton
• Metalearned Neural Memory Tsendsuren Munkhdalai, Alessandro Sordoni, TONG WANG, Adam Trischler
• Learning Bayesian Networks with Low Rank Conditional Probability Tables Adarsh Barik, Jean Honorio
• Large Scale Adversarial Representation Learning Jeff Donahue, Karen Simonyan
• Hindsight Credit Assignment Anna Harutyunyan, Will Dabney, Thomas Mesnard, Mohammad Gheshlaghi Azar, Bilal Piot, Nicolas Heess, Hado P. van Hasselt, Gregory Wayne, Satinder Singh, Doina Precup, Remi Munos
• Zero-shot Learning via Simultaneous Generating and Learning Hyeonwoo Yu, Beomhee Lee
• Direct Optimization through $\arg \max$ for Discrete Variational Auto-Encoder Guy Lorberbom, Andreea Gane, Tommi Jaakkola, Tamir Hazan
• Generalization Error Analysis of Quantized Compressive Learning Xiaoyun Li, Ping Li
• Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning David Janz, Jiri Hron, Przemysław Mazur, Katja Hofmann, José Miguel Hernández-Lobato, Sebastian Tschiatschek
• Trivializations for Gradient-Based Optimization on Manifolds Mario Lezcano Casado
• On the Fairness of Disentangled Representations Francesco Locatello, Gabriele Abbati, Thomas Rainforth, Stefan Bauer, Bernhard Schölkopf, Olivier Bachem
• When to use parametric models in reinforcement learning? Hado P. van Hasselt, Matteo Hessel, John Aslanides
• Ouroboros: On Accelerating Training of Transformer-Based Language Models Qian Yang, Zhouyuan Huo, Wenlin Wang, Lawrence Carin
• MonoForest framework for tree ensemble analysis Igor Kuralenok, Vasilii Ershov, Igor Labutin
• Correlation Priors for Reinforcement Learning Bastian Alt, Adrian Šošić, Heinz Koeppl
• Push-pull Feedback Implements Hierarchical Information Retrieval Efficiently Xiao Liu, Xiaolong Zou, Zilong Ji, Gengshuo Tian, Yuanyuan Mi, Tiejun Huang, K. Y. Michael Wong, Si Wu
• Calibration tests in multi-class classification: A unifying framework David Widmann, Fredrik Lindsten, Dave Zachariah
• Joint Optimization of Tree-based Index and Deep Model for Recommender Systems Han Zhu, Daqing Chang, Ziru Xu, Pengye Zhang, Xiang Li, Jie He, Han Li, Jian Xu, Kun Gai
• Accurate Uncertainty Estimation and Decomposition in Ensemble Learning Jeremiah Liu, John Paisley, Marianthi-Anna Kioumourtzoglou, Brent Coull
• Globally Optimal Learning for Structured Elliptical Losses Yoav Wald, Nofar Noy, Gal Elidan, Ami Wiesel
• MixMatch: A Holistic Approach to Semi-Supervised Learning David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, Colin A. Raffel
• Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks Qiyang Li, Saminul Haque, Cem Anil, James Lucas, Roger B. Grosse, Joern-Henrik Jacobsen
• Learning to Confuse: Generating Training Time Adversarial Data with Auto-Encoder Ji Feng, Qi-Zhi Cai, Zhi-Hua Zhou
• Invariance-inducing regularization using worst-case transformations suffices to boost accuracy and spatial robustness Fanny Yang, Zuowen Wang, Christina Heinze-Deml
• Attentive State-Space Modeling of Disease Progression Ahmed M. Alaa, Mihaela van der Schaar
• On two ways to use determinantal point processes for Monte Carlo integration Guillaume Gautier, Rémi Bardenet, Michal Valko
• ADDIS: an adaptive discarding algorithm for online FDR control with conservative nulls Jinjin Tian, Aaditya Ramdas
• Controllable Text-to-Image Generation Bowen Li, Xiaojuan Qi, Thomas Lukasiewicz, Philip Torr
• Exploring Algorithmic Fairness in Robust Graph Covering Problems Aida Rahmattalabi, Phebe Vayanos, Anthony Fulginiti, Eric Rice, Bryan Wilder, Amulya Yadav, Milind Tambe
• Fast Convergence of Natural Gradient Descent for Over-Parameterized Neural Networks Guodong Zhang, James Martens, Roger B. Grosse
• Reducing the variance in online optimization by transporting past gradients Sébastien Arnold, Pierre-Antoine Manzagol, Reza Babanezhad Harikandeh, Ioannis Mitliagkas, Nicolas Le Roux
• 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
• Graph Normalizing Flows Jenny Liu, Aviral Kumar, Jimmy Ba, Jamie Kiros, Kevin Swersky
• Cascaded Dilated Dense Network with Two-step Data Consistency for MRI Reconstruction Hao Zheng, Faming Fang, Guixu Zhang
• Neural networks grown and self-organized by noise Guruprasad Raghavan, Matt Thomson
• Likelihood Ratios for Out-of-Distribution Detection Jie Ren, Peter J. Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark Depristo, Joshua Dillon, Balaji Lakshminarayanan
• Root Mean Square Layer Normalization Biao Zhang, Rico Sennrich
• HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs Naganand Yadati, Madhav Nimishakavi, Prateek Yadav, Vikram Nitin, Anand Louis, Partha Talukdar
• Asymptotics for Sketching in Least Squares Regression Edgar Dobriban, Sifan Liu
• Gradient Dynamics of Shallow Univariate ReLU Networks Francis Williams, Matthew Trager, Daniele Panozzo, Claudio Silva, Denis Zorin, Joan Bruna
• Chirality Nets for Human Pose Regression Raymond Yeh, Yuan-Ting Hu, Alexander Schwing
• TAB-VCR: Tags and Attributes based VCR Baselines Jingxiang Lin, Unnat Jain, Alexander Schwing
• Multiclass Performance Metric Elicitation Gaurush Hiranandani, Shant Boodaghians, Ruta Mehta, Oluwasanmi O. Koyejo
• Assessing Social and Intersectional Biases in Contextualized Word Representations Yi Chern Tan, L. Elisa Celis
• Likelihood-Free Overcomplete ICA and Applications In Causal Discovery Chenwei DING, Mingming Gong, Kun Zhang, Dacheng Tao
• MaCow: Masked Convolutional Generative Flow Xuezhe Ma, Xiang Kong, Shanghang Zhang, Eduard Hovy
• Batched Multi-armed Bandits Problem Zijun Gao, Yanjun Han, Zhimei Ren, Zhengqing Zhou
• High-Quality Self-Supervised Deep Image Denoising Samuli Laine, Tero Karras, Jaakko Lehtinen, Timo Aila
• Generalization in multitask deep neural classifiers: a statistical physics approach Anthony Ndirango, Tyler Lee
• Causal Regularization Dominik Janzing
• Locality-Sensitive Hashing for f-Divergences: Mutual Information Loss and Beyond Lin Chen, Hossein Esfandiari, Gang Fu, Vahab Mirrokni
• Augmented Neural ODEs Emilien Dupont, Arnaud Doucet, Yee Whye Teh
• Efficient Smooth Non-Convex Stochastic Compositional Optimization via Stochastic Recursive Gradient Descent Wenqing Hu, Chris Junchi Li, Xiangru Lian, Ji Liu, Huizhuo Yuan
• Regularizing Trajectory Optimization with Denoising Autoencoders Rinu Boney, Norman Di Palo, Mathias Berglund, Alexander Ilin, Juho Kannala, Antti Rasmus, Harri Valpola
• Multi-Criteria Dimensionality Reduction with Applications to Fairness Uthaipon Tantipongpipat, Samira Samadi, Mohit Singh, Jamie H. Morgenstern, Santosh Vempala
• Structured and Deep Similarity Matching via Structured and Deep Hebbian Networks Dina Obeid, Hugo Ramambason, Cengiz Pehlevan
• Neural Trust Region/Proximal Policy Optimization Attains Globally Optimal Policy Boyi Liu, Qi Cai, Zhuoran Yang, Zhaoran Wang
• ANODEV2: A Coupled Neural ODE Framework Tianjun Zhang, Zhewei Yao, Amir Gholami, Joseph E. Gonzalez, Kurt Keutzer, Michael W. Mahoney, George Biros
• Learning Neural Networks with Adaptive Regularization Han Zhao, Yao-Hung Hubert Tsai, Russ R. Salakhutdinov, Geoffrey J. Gordon
• Turbo Autoencoder: Deep learning based channel codes for point-to-point communication channels Yihan Jiang, Hyeji Kim, Himanshu Asnani, Sreeram Kannan, Sewoong Oh, Pramod Viswanath
• DetNAS: Backbone Search for Object Detection Yukang Chen, Tong Yang, Xiangyu Zhang, GAOFENG MENG, Xinyu Xiao, Jian Sun
• Nonlinear scaling of resource allocation in sensory bottlenecks Laura Rose Edmondson, Alejandro Jimenez Rodriguez, Hannes P. Saal
• What the Vec? Towards Probabilistically Grounded Embeddings Carl Allen, Ivana Balazevic, Timothy Hospedales
• Diffusion Improves Graph Learning Johannes Klicpera, Stefan Weißenberger, Stephan Günnemann
• Inverting Deep Generative models, One layer at a time Qi Lei, Ajil Jalal, Inderjit S. Dhillon, Alexandros G. Dimakis
• Sample Complexity of Learning Mixture of Sparse Linear Regressions Akshay Krishnamurthy, Arya Mazumdar, Andrew McGregor, Soumyabrata Pal
• A Convex Relaxation Barrier to Tight Robustness Verification of Neural Networks Hadi Salman, Greg Yang, Huan Zhang, Cho-Jui Hsieh, Pengchuan Zhang
• A Latent Variational Framework for Stochastic Optimization Philippe Casgrain
• Escaping from saddle points on Riemannian manifolds Yue Sun, Nicolas Flammarion, Maryam Fazel
• Solving a Class of Non-Convex Min-Max Games Using Iterative First Order Methods Maher Nouiehed, Maziar Sanjabi, Tianjian Huang, Jason D. Lee, Meisam Razaviyayn
• The Option Keyboard: Combining Skills in Reinforcement Learning Andre Barreto, Diana Borsa, Shaobo Hou, Gheorghe Comanici, Eser Aygün, Philippe Hamel, Daniel Toyama, Jonathan hunt, Shibl Mourad, David Silver, Doina Precup
• On Learning Over-parameterized Neural Networks: A Functional Approximation Perspective Lili Su, Pengkun Yang
• Modeling Tabular data using Conditional GAN Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante, Kalyan Veeramachaneni
• Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates Sharan Vaswani, Aaron Mishkin, Issam Laradji, Mark Schmidt, Gauthier Gidel, Simon Lacoste-Julien
• Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies Yonathan Efroni, Nadav Merlis, Mohammad Ghavamzadeh, Shie Mannor
• Weighted Linear Bandits for Non-Stationary Environments Yoan Russac, Claire Vernade, Olivier Cappé
• Neural Lyapunov Control Ya-Chien Chang, Nima Roohi, Sicun Gao
• Stochastic Variance Reduced Primal Dual Algorithms for Empirical Composition Optimization Adithya M Devraj, Jianshu Chen
• Hamiltonian Neural Networks Samuel Greydanus, Misko Dzamba, Jason Yosinski
• Better Transfer Learning with Inferred Successor Maps Tamas Madarasz, Tim Behrens
• Random Quadratic Forms with Dependence: Applications to Restricted Isometry and Beyond Arindam Banerjee, Qilong Gu, Vidyashankar Sivakumar, Steven Z. Wu
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