Book
Advances in Neural Information Processing Systems 30 (NIPS 2017)
Edited by:
I. Guyon and U.V. Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett
Real Time Image Saliency for Black Box Classifiers Piotr Dabkowski, Yarin Gal Joint distribution optimal transportation for domain adaptation Nicolas Courty, Rémi Flamary, Amaury Habrard, Alain Rakotomamonjy Learning A Structured Optimal Bipartite Graph for Co-Clustering Feiping Nie, Xiaoqian Wang, Cheng Deng, Heng Huang Learning to Inpaint for Image Compression Mohammad Haris Baig, Vladlen Koltun, Lorenzo Torresani Inverse Filtering for Hidden Markov Models Robert Mattila, Cristian Rojas, Vikram Krishnamurthy, Bo Wahlberg On clustering network-valued data Soumendu Sundar Mukherjee, Purnamrita Sarkar, Lizhen Lin Langevin Dynamics with Continuous Tempering for Training Deep Neural Networks Nanyang Ye, Zhanxing Zhu, Rafal Mantiuk Beyond Worst-case: A Probabilistic Analysis of Affine Policies in Dynamic Optimization Omar El Housni, Vineet Goyal Few-Shot Learning Through an Information Retrieval Lens Eleni Triantafillou, Richard Zemel, Raquel Urtasun Accelerated consensus via Min-Sum Splitting Patrick Rebeschini, Sekhar C. Tatikonda Saliency-based Sequential Image Attention with Multiset Prediction Sean Welleck, Jialin Mao, Kyunghyun Cho, Zheng Zhang Adaptive Bayesian Sampling with Monte Carlo EM Anirban Roychowdhury, Srinivasan Parthasarathy Scalable Levy Process Priors for Spectral Kernel Learning Phillip A. Jang, Andrew Loeb, Matthew Davidow, Andrew G. Wilson Model-Powered Conditional Independence Test Rajat Sen, Ananda Theertha Suresh, Karthikeyan Shanmugam, Alexandros G. Dimakis, Sanjay Shakkottai Learning Multiple Tasks with Multilinear Relationship Networks Mingsheng Long, ZHANGJIE CAO, Jianmin Wang, Philip S. Yu Query Complexity of Clustering with Side Information Arya Mazumdar, Barna Saha Non-parametric Structured Output Networks Andreas Lehrmann, Leonid Sigal Robust Imitation of Diverse Behaviors Ziyu Wang, Josh S. Merel, Scott E. Reed, Nando de Freitas, Gregory Wayne, Nicolas Heess High-Order Attention Models for Visual Question Answering Idan Schwartz, Alexander Schwing, Tamir Hazan FALKON: An Optimal Large Scale Kernel Method Alessandro Rudi, Luigi Carratino, Lorenzo Rosasco Generalized Linear Model Regression under Distance-to-set Penalties Jason Xu, Eric Chi, Kenneth Lange Fisher GAN Youssef Mroueh, Tom Sercu Minimax Estimation of Bandable Precision Matrices Addison Hu, Sahand Negahban Kernel functions based on triplet comparisons Matthäus Kleindessner, Ulrike von Luxburg Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization Fabian Pedregosa, Rémi Leblond, Simon Lacoste-Julien A New Theory for Matrix Completion Guangcan Liu, Qingshan Liu, Xiaotong Yuan A Bayesian Data Augmentation Approach for Learning Deep Models Toan Tran, Trung Pham, Gustavo Carneiro, Lyle Palmer, Ian Reid Deep Hyperalignment Muhammad Yousefnezhad, Daoqiang Zhang Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model Jiasen Lu, Anitha Kannan, Jianwei Yang, Devi Parikh, Dhruv Batra PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference Jonathan Huggins, Ryan P. Adams, Tamara Broderick Online multiclass boosting Young Hun Jung, Jack Goetz, Ambuj Tewari State Aware Imitation Learning Yannick Schroecker, Charles L. Isbell Adaptive SVRG Methods under Error Bound Conditions with Unknown Growth Parameter Yi Xu, Qihang Lin, Tianbao Yang Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data Wei-Ning Hsu, Yu Zhang, James Glass Recurrent Ladder Networks Isabeau Prémont-Schwarz, Alexander Ilin, Tele Hao, Antti Rasmus, Rinu Boney, Harri Valpola Distral: Robust multitask reinforcement learning Yee Teh, Victor Bapst, Wojciech M. Czarnecki, John Quan, James Kirkpatrick, Raia Hadsell, Nicolas Heess, Razvan Pascanu Real-Time Bidding with Side Information arthur flajolet, Patrick Jaillet Learning Spherical Convolution for Fast Features from 360° Imagery Yu-Chuan Su, Kristen Grauman Approximate Supermodularity Bounds for Experimental Design Luiz Chamon, Alejandro Ribeiro Differentiable Learning of Logical Rules for Knowledge Base Reasoning Fan Yang, Zhilin Yang, William W. Cohen When Cyclic Coordinate Descent Outperforms Randomized Coordinate Descent Mert Gurbuzbalaban, Asuman Ozdaglar, Pablo A. Parrilo, Nuri Vanli Principles of Riemannian Geometry in Neural Networks Michael Hauser, Asok Ray Continual Learning with Deep Generative Replay Hanul Shin, Jung Kwon Lee, Jaehong Kim, Jiwon Kim Nonlinear random matrix theory for deep learning Jeffrey Pennington, Pratik Worah Identification of Gaussian Process State Space Models Stefanos Eleftheriadis, Tom Nicholson, Marc Deisenroth, James Hensman Estimation of the covariance structure of heavy-tailed distributions Xiaohan Wei, Stanislav Minsker Robust Optimization for Non-Convex Objectives Robert S. Chen, Brendan Lucier, Yaron Singer, Vasilis Syrgkanis Exploring Generalization in Deep Learning Behnam Neyshabur, Srinadh Bhojanapalli, David Mcallester, Nati Srebro Spherical convolutions and their application in molecular modelling Wouter Boomsma, Jes Frellsen Safe Adaptive Importance Sampling Sebastian U. Stich, Anant Raj, Martin Jaggi Introspective Classification with Convolutional Nets Long Jin, Justin Lazarow, Zhuowen Tu Hybrid Reward Architecture for Reinforcement Learning Harm Van Seijen, Mehdi Fatemi, Joshua Romoff, Romain Laroche, Tavian Barnes, Jeffrey Tsang When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness Chris Russell, Matt J. Kusner, Joshua Loftus, Ricardo Silva Dualing GANs Yujia Li, Alexander Schwing, Kuan-Chieh Wang, Richard Zemel A Universal Analysis of Large-Scale Regularized Least Squares Solutions Ashkan Panahi, Babak Hassibi Diffusion Approximations for Online Principal Component Estimation and Global Convergence Chris Junchi Li, Mengdi Wang, Han Liu, Tong Zhang k-Support and Ordered Weighted Sparsity for Overlapping Groups: Hardness and Algorithms Cong Han Lim, Stephen Wright Learning to Model the Tail Yu-Xiong Wang, Deva Ramanan, Martial Hebert Neural Variational Inference and Learning in Undirected Graphical Models Volodymyr Kuleshov, Stefano Ermon Aggressive Sampling for Multi-class to Binary Reduction with Applications to Text Classification Bikash Joshi, Massih R. Amini, Ioannis Partalas, Franck Iutzeler, Yury Maximov Learning Linear Dynamical Systems via Spectral Filtering Elad Hazan, Karan Singh, Cyril Zhang Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes Zhenwen Dai, Mauricio Álvarez, Neil Lawrence Semi-Supervised Learning for Optical Flow with Generative Adversarial Networks Wei-Sheng Lai, Jia-Bin Huang, Ming-Hsuan Yang Phase Transitions in the Pooled Data Problem Jonathan Scarlett, Volkan Cevher Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning Christoph Dann, Tor Lattimore, Emma Brunskill Stein Variational Gradient Descent as Gradient Flow Qiang Liu Expectation Propagation for t-Exponential Family Using q-Algebra Futoshi Futami, Issei Sato, Masashi Sugiyama Collaborative PAC Learning Avrim Blum, Nika Haghtalab, Ariel D. Procaccia, Mingda Qiao Polynomial time algorithms for dual volume sampling Chengtao Li, Stefanie Jegelka, Suvrit Sra Premise Selection for Theorem Proving by Deep Graph Embedding Mingzhe Wang, Yihe Tang, Jian Wang, Jia Deng Differentiable Learning of Submodular Models Josip Djolonga, Andreas Krause YASS: Yet Another Spike Sorter Jin Hyung Lee, David E. Carlson, Hooshmand Shokri Razaghi, Weichi Yao, Georges A. Goetz, Espen Hagen, Eleanor Batty, E.J. Chichilnisky, Gaute T. Einevoll, Liam Paninski Variational Laws of Visual Attention for Dynamic Scenes Dario Zanca, Marco Gori How regularization affects the critical points in linear networks Amirhossein Taghvaei, Jin W. Kim, Prashant Mehta On Tensor Train Rank Minimization : Statistical Efficiency and Scalable Algorithm Masaaki Imaizumi, Takanori Maehara, Kohei Hayashi EX2: Exploration with Exemplar Models for Deep Reinforcement Learning Justin Fu, John Co-Reyes, Sergey Levine Training Quantized Nets: A Deeper Understanding Hao Li, Soham De, Zheng Xu, Christoph Studer, Hanan Samet, Tom Goldstein Convolutional Gaussian Processes Mark van der Wilk, Carl Edward Rasmussen, James Hensman Best Response Regression Omer Ben-Porat, Moshe Tennenholtz Elementary Symmetric Polynomials for Optimal Experimental Design Zelda E. Mariet, Suvrit Sra Learning from Complementary Labels Takashi Ishida, Gang Niu, Weihua Hu, Masashi Sugiyama Dynamic Importance Sampling for Anytime Bounds of the Partition Function Qi Lou, Rina Dechter, Alexander T. Ihler Process-constrained batch Bayesian optimisation Pratibha Vellanki, Santu Rana, Sunil Gupta, David Rubin, Alessandra Sutti, Thomas Dorin, Murray Height, Paul Sanders, Svetha Venkatesh Uprooting and Rerooting Higher-Order Graphical Models Mark Rowland, Adrian Weller Learned in Translation: Contextualized Word Vectors Bryan McCann, James Bradbury, Caiming Xiong, Richard Socher Semisupervised Clustering, AND-Queries and Locally Encodable Source Coding Arya Mazumdar, Soumyabrata Pal Regularizing Deep Neural Networks by Noise: Its Interpretation and Optimization Hyeonwoo Noh, Tackgeun You, Jonghwan Mun, Bohyung Han Few-Shot Adversarial Domain Adaptation Saeid Motiian, Quinn Jones, Seyed Iranmanesh, Gianfranco Doretto Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes Taylor W. Killian, Samuel Daulton, George Konidaris, Finale Doshi-Velez Multi-View Decision Processes: The Helper-AI Problem Christos Dimitrakakis, David C. Parkes, Goran Radanovic, Paul Tylkin Maximum Margin Interval Trees Alexandre Drouin, Toby Hocking, Francois Laviolette Online Learning with a Hint Ofer Dekel, arthur flajolet, Nika Haghtalab, Patrick Jaillet DPSCREEN: Dynamic Personalized Screening Kartik Ahuja, William Zame, Mihaela van der Schaar Online Learning of Optimal Bidding Strategy in Repeated Multi-Commodity Auctions M. Sevi Baltaoglu, Lang Tong, Qing Zhao A-NICE-MC: Adversarial Training for MCMC Jiaming Song, Shengjia Zhao, Stefano Ermon Question Asking as Program Generation Anselm Rothe, Brenden M. Lake, Todd Gureckis Gradient Methods for Submodular Maximization Hamed Hassani, Mahdi Soltanolkotabi, Amin Karbasi Recycling Privileged Learning and Distribution Matching for Fairness Novi Quadrianto, Viktoriia Sharmanska Collecting Telemetry Data Privately Bolin Ding, Janardhan Kulkarni, Sergey Yekhanin Parallel Streaming Wasserstein Barycenters Matthew Staib, Sebastian Claici, Justin M. Solomon, Stefanie Jegelka Adaptive Accelerated Gradient Converging Method under H\"{o}lderian Error Bound Condition Mingrui Liu, Tianbao Yang What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? Alex Kendall, Yarin Gal Reconstruct & Crush Network Erinc Merdivan, Mohammad Reza Loghmani, Matthieu Geist Permutation-based Causal Inference Algorithms with Interventions Yuhao Wang, Liam Solus, Karren Yang, Caroline Uhler Deep Dynamic Poisson Factorization Model Chengyue Gong, win-bin huang Scalable Generalized Linear Bandits: Online Computation and Hashing Kwang-Sung Jun, Aniruddha Bhargava, Robert Nowak, Rebecca Willett Experimental Design for Learning Causal Graphs with Latent Variables Murat Kocaoglu, Karthikeyan Shanmugam, Elias Bareinboim Lower bounds on the robustness to adversarial perturbations Jonathan Peck, Joris Roels, Bart Goossens, Yvan Saeys Reliable Decision Support using Counterfactual Models Peter Schulam, Suchi Saria Group Additive Structure Identification for Kernel Nonparametric Regression Chao Pan, Michael Zhu A multi-agent reinforcement learning model of common-pool resource appropriation Julien Pérolat, Joel Z. Leibo, Vinicius Zambaldi, Charles Beattie, Karl Tuyls, Thore Graepel Decoding with Value Networks for Neural Machine Translation Di He, Hanqing Lu, Yingce Xia, Tao Qin, Liwei Wang, Tie-Yan Liu Population Matching Discrepancy and Applications in Deep Learning Jianfei Chen, Chongxuan LI, Yizhong Ru, Jun Zhu Predictive State Recurrent Neural Networks Carlton Downey, Ahmed Hefny, Byron Boots, Geoffrey J. Gordon, Boyue Li Robust Hypothesis Test for Nonlinear Effect with Gaussian Processes Jeremiah Liu, Brent Coull Sharpness, Restart and Acceleration Vincent Roulet, Alexandre d'Aspremont Dynamic Routing Between Capsules Sara Sabour, Nicholas Frosst, Geoffrey E. Hinton InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations Yunzhu Li, Jiaming Song, Stefano Ermon A Regularized Framework for Sparse and Structured Neural Attention Vlad Niculae, Mathieu Blondel Style Transfer from Non-Parallel Text by Cross-Alignment Tianxiao Shen, Tao Lei, Regina Barzilay, Tommi Jaakkola Unsupervised Learning of Disentangled Representations from Video Emily L. Denton, vighnesh Birodkar Countering Feedback Delays in Multi-Agent Learning Zhengyuan Zhou, Panayotis Mertikopoulos, Nicholas Bambos, Peter W. Glynn, Claire Tomlin Affinity Clustering: Hierarchical Clustering at Scale Mohammadhossein Bateni, Soheil Behnezhad, Mahsa Derakhshan, MohammadTaghi Hajiaghayi, Raimondas Kiveris, Silvio Lattanzi, Vahab Mirrokni Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks Federico Monti, Michael Bronstein, Xavier Bresson Maximizing Subset Accuracy with Recurrent Neural Networks in Multi-label Classification Jinseok Nam, Eneldo Loza Mencía, Hyunwoo J. Kim, Johannes Fürnkranz f-GANs in an Information Geometric Nutshell Richard Nock, Zac Cranko, Aditya K. Menon, Lizhen Qu, Robert C. Williamson Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples Haw-Shiuan Chang, Erik Learned-Miller, Andrew McCallum SchNet: A continuous-filter convolutional neural network for modeling quantum interactions Kristof Schütt, Pieter-Jan Kindermans, Huziel Enoc Sauceda Felix, Stefan Chmiela, Alexandre Tkatchenko, Klaus-Robert Müller GibbsNet: Iterative Adversarial Inference for Deep Graphical Models Alex M. Lamb, Devon Hjelm, Yaroslav Ganin, Joseph Paul Cohen, Aaron C. Courville, Yoshua Bengio Bayesian GAN Yunus Saatci, Andrew G. Wilson Alternating minimization for dictionary learning with random initialization Niladri Chatterji, Peter L. Bartlett Sparse Embedded $k$-Means Clustering Weiwei Liu, Xiaobo Shen, Ivor Tsang Reducing Reparameterization Gradient Variance Andrew Miller, Nick Foti, Alexander D'Amour, Ryan P. Adams Min-Max Propagation Christopher Srinivasa, Inmar Givoni, Siamak Ravanbakhsh, Brendan J. Frey Statistical Cost Sharing Eric Balkanski, Umar Syed, Sergei Vassilvitskii Dilated Recurrent Neural Networks Shiyu Chang, Yang Zhang, Wei Han, Mo Yu, Xiaoxiao Guo, Wei Tan, Xiaodong Cui, Michael Witbrock, Mark A. Hasegawa-Johnson, Thomas S. Huang The Expressive Power of Neural Networks: A View from the Width Zhou Lu, Hongming Pu, Feicheng Wang, Zhiqiang Hu, Liwei Wang Inverse Reward Design Dylan Hadfield-Menell, Smitha Milli, Pieter Abbeel, Stuart J. Russell, Anca Dragan The power of absolute discounting: all-dimensional distribution estimation Moein Falahatgar, Mesrob I. Ohannessian, Alon Orlitsky, Venkatadheeraj Pichapati A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning Marc Lanctot, Vinicius Zambaldi, Audrunas Gruslys, Angeliki Lazaridou, Karl Tuyls, Julien Perolat, David Silver, Thore Graepel Spectral Mixture Kernels for Multi-Output Gaussian Processes Gabriel Parra, Felipe Tobar Affine-Invariant Online Optimization and the Low-rank Experts Problem Tomer Koren, Roi Livni Pose Guided Person Image Generation Liqian Ma, Xu Jia, Qianru Sun, Bernt Schiele, Tinne Tuytelaars, Luc Van Gool Successor Features for Transfer in Reinforcement Learning Andre Barreto, Will Dabney, Remi Munos, Jonathan J. Hunt, Tom Schaul, Hado P. van Hasselt, David Silver On Quadratic Convergence of DC Proximal Newton Algorithm in Nonconvex Sparse Learning Xingguo Li, Lin Yang, Jason Ge, Jarvis Haupt, Tong Zhang, Tuo Zhao Hypothesis Transfer Learning via Transformation Functions Simon S. Du, Jayanth Koushik, Aarti Singh, Barnabas Poczos Finite Sample Analysis of the GTD Policy Evaluation Algorithms in Markov Setting Yue Wang, Wei Chen, Yuting Liu, Zhi-Ming Ma, Tie-Yan Liu Variational Inference via $\chi$ Upper Bound Minimization Adji Bousso Dieng, Dustin Tran, Rajesh Ranganath, John Paisley, David Blei A Probabilistic Framework for Nonlinearities in Stochastic Neural Networks Qinliang Su, xuejun Liao, Lawrence Carin Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation Yuhuai Wu, Elman Mansimov, Roger B. Grosse, Shun Liao, Jimmy Ba Optimistic posterior sampling for reinforcement learning: worst-case regret bounds Shipra Agrawal, Randy Jia Efficient Second-Order Online Kernel Learning with Adaptive Embedding Daniele Calandriello, Alessandro Lazaric, Michal Valko Solving Most Systems of Random Quadratic Equations Gang Wang, Georgios Giannakis, Yousef Saad, Jie Chen Online Reinforcement Learning in Stochastic Games Chen-Yu Wei, Yi-Te Hong, Chi-Jen Lu Independence clustering (without a matrix) Daniil Ryabko Effective Parallelisation for Machine Learning Michael Kamp, Mario Boley, Olana Missura, Thomas Gärtner Deep Mean-Shift Priors for Image Restoration Siavash Arjomand Bigdeli, Matthias Zwicker, Paolo Favaro, Meiguang Jin On Structured Prediction Theory with Calibrated Convex Surrogate Losses Anton Osokin, Francis Bach, Simon Lacoste-Julien Invariance and Stability of Deep Convolutional Representations Alberto Bietti, Julien Mairal Variational Memory Addressing in Generative Models Jörg Bornschein, Andriy Mnih, Daniel Zoran, Danilo Jimenez Rezende Shallow Updates for Deep Reinforcement Learning Nir Levine, Tom Zahavy, Daniel J. Mankowitz, Aviv Tamar, Shie Mannor Learning with Bandit Feedback in Potential Games Amélie Heliou, Johanne Cohen, Panayotis Mertikopoulos A Greedy Approach for Budgeted Maximum Inner Product Search Hsiang-Fu Yu, Cho-Jui Hsieh, Qi Lei, Inderjit S. Dhillon Riemannian approach to batch normalization Minhyung Cho, Jaehyung Lee Adaptive Clustering through Semidefinite Programming Martin Royer #Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning Haoran Tang, Rein Houthooft, Davis Foote, Adam Stooke, OpenAI Xi Chen, Yan Duan, John Schulman, Filip DeTurck, Pieter Abbeel Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition Naoya Takeishi, Yoshinobu Kawahara, Takehisa Yairi Online Prediction with Selfish Experts Tim Roughgarden, Okke Schrijvers Streaming Robust Submodular Maximization: A Partitioned Thresholding Approach Slobodan Mitrovic, Ilija Bogunovic, Ashkan Norouzi-Fard, Jakub M. Tarnawski, Volkan Cevher Neural Program Meta-Induction Jacob Devlin, Rudy R. Bunel, Rishabh Singh, Matthew Hausknecht, Pushmeet Kohli The Scaling Limit of High-Dimensional Online Independent Component Analysis Chuang Wang, Yue Lu Practical Locally Private Heavy Hitters Raef Bassily, Kobbi Nissim, Uri Stemmer, Abhradeep Guha Thakurta Mixture-Rank Matrix Approximation for Collaborative Filtering Dongsheng Li, Chao Chen, Wei Liu, Tun Lu, Ning Gu, Stephen Chu Higher-Order Total Variation Classes on Grids: Minimax Theory and Trend Filtering Methods Veeranjaneyulu Sadhanala, Yu-Xiang Wang, James L. Sharpnack, Ryan J. Tibshirani Robust Conditional Probabilities Yoav Wald, Amir Globerson Attention is All you Need Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, Illia Polosukhin A General Framework for Robust Interactive Learning Ehsan Emamjomeh-Zadeh, David Kempe Sample and Computationally Efficient Learning Algorithms under S-Concave Distributions Maria-Florina F. Balcan, Hongyang Zhang Net-Trim: Convex Pruning of Deep Neural Networks with Performance Guarantee Alireza Aghasi, Afshin Abdi, Nam Nguyen, Justin Romberg ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games Yuandong Tian, Qucheng Gong, Wenling Shang, Yuxin Wu, C. Lawrence Zitnick Task-based End-to-end Model Learning in Stochastic Optimization Priya Donti, Brandon Amos, J. Zico Kolter Fader Networks:Manipulating Images by Sliding Attributes Guillaume Lample, Neil Zeghidour, Nicolas Usunier, Antoine Bordes, Ludovic DENOYER, Marc'Aurelio Ranzato VAE Learning via Stein Variational Gradient Descent Yuchen Pu, Zhe Gan, Ricardo Henao, Chunyuan Li, Shaobo Han, Lawrence Carin Approximation and Convergence Properties of Generative Adversarial Learning Shuang Liu, Olivier Bousquet, Kamalika Chaudhuri VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning Akash Srivastava, Lazar Valkov, Chris Russell, Michael U. Gutmann, Charles Sutton Local Aggregative Games Vikas Garg, Tommi Jaakkola An Error Detection and Correction Framework for Connectomics Jonathan Zung, Ignacio Tartavull, Kisuk Lee, H. Sebastian Seung Hindsight Experience Replay Marcin Andrychowicz, Filip Wolski, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder, Bob McGrew, Josh Tobin, OpenAI Pieter Abbeel, Wojciech Zaremba Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data Joel A. Tropp, Alp Yurtsever, Madeleine Udell, Volkan Cevher The Numerics of GANs Lars Mescheder, Sebastian Nowozin, Andreas Geiger Cortical microcircuits as gated-recurrent neural networks Rui Costa, Ioannis Alexandros Assael, Brendan Shillingford, Nando de Freitas, TIm Vogels Deep Lattice Networks and Partial Monotonic Functions Seungil You, David Ding, Kevin Canini, Jan Pfeifer, Maya Gupta Zap Q-Learning Adithya M Devraj, Sean Meyn Contrastive Learning for Image Captioning Bo Dai, Dahua Lin Variational Walkback: Learning a Transition Operator as a Stochastic Recurrent Net Anirudh Goyal ALIAS PARTH GOYAL, Nan Rosemary Ke, Surya Ganguli, Yoshua Bengio Linear Time Computation of Moments in Sum-Product Networks Han Zhao, Geoffrey J. Gordon SGD Learns the Conjugate Kernel Class of the Network Amit Daniely Learning to Pivot with Adversarial Networks Gilles Louppe, Michael Kagan, Kyle Cranmer Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration Jason Altschuler, Jonathan Niles-Weed, Philippe Rigollet Universal Style Transfer via Feature Transforms Yijun Li, Chen Fang, Jimei Yang, Zhaowen Wang, Xin Lu, Ming-Hsuan Yang Ensemble Sampling Xiuyuan Lu, Benjamin Van Roy Practical Data-Dependent Metric Compression with Provable Guarantees Piotr Indyk, Ilya Razenshteyn, Tal Wagner Partial Hard Thresholding: Towards A Principled Analysis of Support Recovery Jie Shen, Ping Li Selective Classification for Deep Neural Networks Yonatan Geifman, Ran El-Yaniv Diverse and Accurate Image Description Using a Variational Auto-Encoder with an Additive Gaussian Encoding Space Liwei Wang, Alexander Schwing, Svetlana Lazebnik Deconvolutional Paragraph Representation Learning Yizhe Zhang, Dinghan Shen, Guoyin Wang, Zhe Gan, Ricardo Henao, Lawrence Carin Learning to See Physics via Visual De-animation Jiajun Wu, Erika Lu, Pushmeet Kohli, Bill Freeman, Josh Tenenbaum Adversarial Symmetric Variational Autoencoder Yuchen Pu, Weiyao Wang, Ricardo Henao, Liqun Chen, Zhe Gan, Chunyuan Li, Lawrence Carin Model evidence from nonequilibrium simulations Michael Habeck Estimating High-dimensional Non-Gaussian Multiple Index Models via Stein’s Lemma Zhuoran Yang, Krishnakumar Balasubramanian, Zhaoran Wang, Han Liu Learning spatiotemporal piecewise-geodesic trajectories from longitudinal manifold-valued data Stéphanie ALLASSONNIERE, Juliette Chevallier, Stephane Oudard SVD-Softmax: Fast Softmax Approximation on Large Vocabulary Neural Networks Kyuhong Shim, Minjae Lee, Iksoo Choi, Yoonho Boo, Wonyong Sung Concentration of Multilinear Functions of the Ising Model with Applications to Network Data Constantinos Daskalakis, Nishanth Dikkala, Gautam Kamath Rigorous Dynamics and Consistent Estimation in Arbitrarily Conditioned Linear Systems Alyson K. Fletcher, Mojtaba Sahraee-Ardakan, Sundeep Rangan, Philip Schniter OnACID: Online Analysis of Calcium Imaging Data in Real Time Andrea Giovannucci, Johannes Friedrich, Matt Kaufman, Anne Churchland, Dmitri Chklovskii, Liam Paninski, Eftychios A. Pnevmatikakis Action Centered Contextual Bandits Kristjan Greenewald, Ambuj Tewari, Susan Murphy, Predag Klasnja Cost efficient gradient boosting Sven Peter, Ferran Diego, Fred A. Hamprecht, Boaz Nadler Eigenvalue Decay Implies Polynomial-Time Learnability for Neural Networks Surbhi Goel, Adam Klivans On Separability of Loss Functions, and Revisiting Discriminative Vs Generative Models Adarsh Prasad, Alexandru Niculescu-Mizil, Pradeep K. Ravikumar ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events Evan Racah, Christopher Beckham, Tegan Maharaj, Samira Ebrahimi Kahou, Mr. Prabhat, Chris Pal A Meta-Learning Perspective on Cold-Start Recommendations for Items Manasi Vartak, Arvind Thiagarajan, Conrado Miranda, Jeshua Bratman, Hugo Larochelle Learning Unknown Markov Decision Processes: A Thompson Sampling Approach Yi Ouyang, Mukul Gagrani, Ashutosh Nayyar, Rahul Jain Deep Hyperspherical Learning Weiyang Liu, Yan-Ming Zhang, Xingguo Li, Zhiding Yu, Bo Dai, Tuo Zhao, Le Song Interpretable and Globally Optimal Prediction for Textual Grounding using Image Concepts Raymond Yeh, Jinjun Xiong, Wen-Mei Hwu, Minh Do, Alexander Schwing Off-policy evaluation for slate recommendation Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miro Dudik, John Langford, Damien Jose, Imed Zitouni Unbiased estimates for linear regression via volume sampling Michal Derezinski, Manfred K. K. Warmuth Revisiting Perceptron: Efficient and Label-Optimal Learning of Halfspaces Songbai Yan, Chicheng Zhang Renyi Differential Privacy Mechanisms for Posterior Sampling Joseph Geumlek, Shuang Song, Kamalika Chaudhuri Variable Importance Using Decision Trees Jalil Kazemitabar, Arash Amini, Adam Bloniarz, Ameet S. Talwalkar A simple model of recognition and recall memory Nisheeth Srivastava, Edward Vul Implicit Regularization in Matrix Factorization Suriya Gunasekar, Blake E. Woodworth, Srinadh Bhojanapalli, Behnam Neyshabur, Nati Srebro Continuous DR-submodular Maximization: Structure and Algorithms An Bian, Kfir Levy, Andreas Krause, Joachim M. Buhmann Decoupling "when to update" from "how to update" Eran Malach, Shai Shalev-Shwartz Regret Analysis for Continuous Dueling Bandit Wataru Kumagai One-Sided Unsupervised Domain Mapping Sagie Benaim, Lior Wolf Poincaré Embeddings for Learning Hierarchical Representations Maximillian Nickel, Douwe Kiela Variance-based Regularization with Convex Objectives Hongseok Namkoong, John C. Duchi A Sharp Error Analysis for the Fused Lasso, with Application to Approximate Changepoint Screening Kevin Lin, James L. Sharpnack, Alessandro Rinaldo, Ryan J. Tibshirani Cross-Spectral Factor Analysis Neil Gallagher, Kyle R. Ulrich, Austin Talbot, Kafui Dzirasa, Lawrence Carin, David E. Carlson Self-Normalizing Neural Networks Günter Klambauer, Thomas Unterthiner, Andreas Mayr, Sepp Hochreiter Fast amortized inference of neural activity from calcium imaging data with variational autoencoders Artur Speiser, Jinyao Yan, Evan W. Archer, Lars Buesing, Srinivas C. Turaga, Jakob H. Macke Asynchronous Parallel Coordinate Minimization for MAP Inference Ofer Meshi, Alexander Schwing Inductive Representation Learning on Large Graphs Will Hamilton, Zhitao Ying, Jure Leskovec Data-Efficient Reinforcement Learning in Continuous State-Action Gaussian-POMDPs Rowan McAllister, Carl Edward Rasmussen Coded Distributed Computing for Inverse Problems Yaoqing Yang, Pulkit Grover, Soummya Kar Dykstra's Algorithm, ADMM, and Coordinate Descent: Connections, Insights, and Extensions Ryan J. Tibshirani Training recurrent networks to generate hypotheses about how the brain solves hard navigation problems Ingmar Kanitscheider, Ila Fiete SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud Zahra Ghodsi, Tianyu Gu, Siddharth Garg Improved Graph Laplacian via Geometric Self-Consistency Dominique Joncas, Marina Meila, James McQueen Generalization Properties of Learning with Random Features Alessandro Rudi, Lorenzo Rosasco Predictive-State Decoders: Encoding the Future into Recurrent Networks Arun Venkatraman, Nicholas Rhinehart, Wen Sun, Lerrel Pinto, Martial Hebert, Byron Boots, Kris Kitani, J. Bagnell Federated Multi-Task Learning Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, Ameet S. Talwalkar Learning Causal Structures Using Regression Invariance AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Kun Zhang Practical Hash Functions for Similarity Estimation and Dimensionality Reduction Søren Dahlgaard, Mathias Knudsen, Mikkel Thorup Gaussian Quadrature for Kernel Features Tri Dao, Christopher M. De Sa, Christopher Ré Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets Karol Hausman, Yevgen Chebotar, Stefan Schaal, Gaurav Sukhatme, Joseph J. Lim Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees Francesco Locatello, Michael Tschannen, Gunnar Raetsch, Martin Jaggi On the Fine-Grained Complexity of Empirical Risk Minimization: Kernel Methods and Neural Networks Arturs Backurs, Piotr Indyk, Ludwig Schmidt Acceleration and Averaging in Stochastic Descent Dynamics Walid Krichene, Peter L. 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