NIPS 2016 Accepted Papers
 

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• Scan Order in Gibbs Sampling: Models in Which it Matters and Bounds on How Much
  Bryan He*, Stanford University; Christopher De Sa, Stanford University; Ioannis Mitliagkas, ; Christopher Ré, Stanford University
   
• Deep ADMM-Net for Compressive Sensing MRI
  Yan Yang, Xi'an Jiaotong University; Jian Sun*, Xi'an Jiaotong University; Huibin Li, ; Zongben Xu,
   
• A scaled Bregman theorem with applications
  Richard NOCK, Data61 and ANU; Aditya Menon*, ; Cheng Soon Ong, Data61
   
• Swapout: Learning an ensemble of deep architectures
  Saurabh Singh*, UIUC; Derek Hoiem, UIUC; David Forsyth, UIUC
   
• On Regularizing Rademacher Observation Losses
  Richard NOCK*, Data61 and ANU
   
• Without-Replacement Sampling for Stochastic Gradient Methods
  Ohad Shamir*, Weizmann Institute of Science
   
• Fast and Provably Good Seedings for k-Means
  Olivier Bachem*, ETH Zurich; Mario Lucic, ETH Zurich; Hamed Hassani, ETH Zurich; Andreas Krause,
   
• Unsupervised Learning for Physical Interaction through Video Prediction
  Chelsea Finn*, Google, Inc.; Ian Goodfellow, ; Sergey Levine, University of Washington
   
• Matrix Completion and Clustering in Self-Expressive Models
  Ehsan Elhamifar*,
   
• Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
  Chengkai Zhang, ; Jiajun Wu*, MIT; Tianfan Xue, ; William Freeman, ; Joshua Tenenbaum,
   
• Probabilistic Modeling of Future Frames from a Single Image
  Tianfan Xue*, ; Jiajun Wu, MIT; Katherine Bouman, MIT; William Freeman,
   
• Human Decision-Making under Limited Time
  Pedro Ortega*, ; Alan Stocker,
   
• Incremental Boosting Convolutional Neural Network for Facial Action Unit Recognition
  Shizhong Han*, University of South Carolina; Zibo Meng, University of South Carolina; Ahmed Shehab Khan, University of South Carolina; Yan Tong, University of South Carolina
   
• Natural-Parameter Networks: A Class of Probabilistic Neural Networks
  Hao Wang*, HKUST; Xingjian Shi, ; Dit-Yan Yeung,
   
• Tree-Structured Reinforcement Learning for Sequential Object Localization
  Zequn Jie*, National Univ of Singapore; Xiaodan Liang, Sun Yat-sen University; Jiashi Feng, National University of Singapo; Xiaojie Jin, NUS; Wen Feng Lu, National Univ of Singapore; Shuicheng Yan,
   
• Unsupervised Domain Adaptation with Residual Transfer Networks
  Mingsheng Long*, Tsinghua University; Han Zhu, Tsinghua University; Jianmin Wang, Tsinghua University; Michael Jordan,
   
• Verification Based Solution for Structured MAB Problems
  Zohar Karnin*,
   
• Minimizing Regret on Reflexive Banach Spaces and Nash Equilibria in Continuous Zero-Sum Games
  Maximilian Balandat*, UC Berkeley; Walid Krichene, UC Berkeley; Claire Tomlin, UC Berkeley; Alexandre Bayen, UC Berkeley
   
• Linear dynamical neural population models through nonlinear embeddings
  Yuanjun Gao, Columbia University; Evan Archer*, ; John Cunningham, ; Liam Paninski,
   
• SURGE: Surface Regularized Geometry Estimation from a Single Image
  Peng Wang*, UCLA; Xiaohui Shen, Adobe Research; Bryan Russell, ; Scott Cohen, Adobe Research; Brian Price, ; Alan Yuille,
   
• Interpretable Distribution Features with Maximum Testing Power
  Wittawat Jitkrittum*, Gatsby Unit, UCL; Zoltan Szabo, ; Kacper Chwialkowski, Gatsby Unit, UCL; Arthur Gretton,
   
• Sorting out typicality with the inverse moment matrix SOS polynomial
  Edouard Pauwels*, ; Jean-Bernard Lasserre, LAAS-CNRS
   
• Multi-armed Bandits: Competing with Optimal Sequences
  Zohar Karnin*, ; Oren Anava, Technion
   
• Multivariate tests of association based on univariate tests
  Ruth Heller*, Tel-Aviv University; Yair Heller,
   
• Learning What and Where to Draw
  Scott Reed*, University of Michigan; Zeynep Akata, Max Planck Institute for Informatics; Santosh Mohan, University of MIchigan; Samuel Tenka, University of MIchigan; Bernt Schiele, ; Honglak Lee, University of Michigan
   
• The Sound of APALM Clapping: Faster Nonsmooth Nonconvex Optimization with Stochastic Asynchronous PALM
  Damek Davis*, Cornell University; Brent Edmunds, University of California, Los Angeles; Madeleine Udell,
   
• Integrator Nets
  Hakan Bilen*, University of Oxford; Andrea Vedaldi,
   
• Combining Low-Density Separators with CNNs
  Yu-Xiong Wang*, Carnegie Mellon University; Martial Hebert, Carnegie Mellon University
   
• CNNpack: Packing Convolutional Neural Networks in the Frequency Domain
  Yunhe Wang*, Peking University ; Shan You, ; Dacheng Tao, ; Chao Xu, ; Chang Xu,
   
• Cooperative Graphical Models
  Josip Djolonga*, ETH Zurich; Stefanie Jegelka, MIT; Sebastian Tschiatschek, ETH Zurich; Andreas Krause,
   
• f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization
  Sebastian Nowozin*, Microsoft Research; Botond Cseke, Microsoft Research; Ryota Tomioka, MSRC
   
• Bayesian Optimization for Probabilistic Programs
  Tom Rainforth*, University of Oxford; Tuan Anh Le, University of Oxford; Jan-Willem van de Meent, University of Oxford; Michael Osborne, ; Frank Wood,
   
• Hierarchical Question-Image Co-Attention for Visual Question Answering
  Jiasen Lu*, Virginia Tech; Jianwei Yang, Virginia Tech; Dhruv Batra, ; Devi Parikh, Virginia Tech
   
• Optimal Sparse Linear Encoders and Sparse PCA
  Malik Magdon-Ismail*, Rensselaer; Christos Boutsidis,
   
• FPNN: Field Probing Neural Networks for 3D Data
  Yangyan Li*, Stanford University; Soeren Pirk, Stanford University; Hao Su, Stanford University; Charles Qi, Stanford University; Leonidas Guibas, Stanford University
   
• CRF-CNN: Modeling Structured Information in Human Pose Estimation
  Xiao Chu*, Cuhk; Wanli Ouyang, ; hongsheng Li, cuhk; Xiaogang Wang, Chinese University of Hong Kong
   
• Fairness in Learning: Classic and Contextual Bandits
  Matthew Joseph, University of Pennsylvania; Michael Kearns, ; Jamie Morgenstern*, University of Pennsylvania; Aaron Roth,
   
• Joint M-Best-Diverse Labelings as a Parametric Submodular Minimization
  Alexander Kirillov*, TU Dresden; Alexander Shekhovtsov, ; Carsten Rother, ; Bogdan Savchynskyy,
   
• Domain Separation Networks
  Dilip Krishnan, Google; George Trigeorgis, Google; Konstantinos Bousmalis*, ; Nathan Silberman, Google; Dumitru Erhan, Google
   
• DISCO Nets : DISsimilarity COefficients Networks
  Diane Bouchacourt*, University of Oxford; M. Pawan Kumar, University of Oxford; Sebastian Nowozin,
   
• Multimodal Residual Learning for Visual QA
  Jin-Hwa Kim*, Seoul National University; Sang-Woo Lee, Seoul National University; Dong-Hyun Kwak, Seoul National University; Min-Oh Heo, Seoul National University; Jeonghee Kim, Naver Labs; Jung-Woo Ha, Naver Labs; Byoung-Tak Zhang, Seoul National University
   
• CMA-ES with Optimal Covariance Update and Storage Complexity
  Dídac Rodríguez Arbonès, University of Copenhagen; Oswin Krause, ; Christian Igel*,
   
• R-FCN: Object Detection via Region-based Fully Convolutional Networks
  Jifeng Dai, Microsoft; Yi Li, Tsinghua University; Kaiming He*, Microsoft; Jian Sun, Microsoft
   
• GAP Safe Screening Rules for Sparse-Group Lasso
  Eugene Ndiaye, Télécom ParisTech; Olivier Fercoq, ; Alexandre Gramfort, ; Joseph Salmon*,
   
• Learning and Forecasting Opinion Dynamics in Social Networks
  Abir De, IIT Kharagpur; Isabel Valera, ; Niloy Ganguly, IIT Kharagpur; sourangshu Bhattacharya, IIT Kharagpur; Manuel Gomez Rodriguez*, MPI-SWS
   
• Gradient-based Sampling: An Adaptive Importance Sampling for Least-squares
  Rong Zhu*, Chinese Academy of Sciences
   
• Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks
  Hao Wang*, HKUST; Xingjian Shi, ; Dit-Yan Yeung,
   
• Mutual information for symmetric rank-one matrix estimation: A proof of the replica formula
  Jean Barbier, EPFL; mohamad Dia, EPFL; Florent Krzakala*, ; Thibault Lesieur, IPHT Saclay; Nicolas Macris, EPFL; Lenka Zdeborova,
   
• A Unified Approach for Learning the Parameters of Sum-Product Networks
  Han Zhao*, Carnegie Mellon University; Pascal Poupart, ; Geoff Gordon,
   
• Training and Evaluating Multimodal Word Embeddings with Large-scale Web Annotated Images
  Junhua Mao*, UCLA; Jiajing Xu, ; Kevin Jing, ; Alan Yuille,
   
• Stochastic Online AUC Maximization
  Yiming Ying*, ; Longyin Wen, State University of New York at Albany; Siwei Lyu, State University of New York at Albany
   
• The Generalized Reparameterization Gradient
  Francisco Ruiz*, Columbia University; Michalis K. Titsias, ; David Blei,
   
• Coupled Generative Adversarial Networks
  Ming-Yu Liu*, MERL; Oncel Tuzel, Mitsubishi Electric Research Labs (MERL)
   
• Exponential Family Embeddings
  Maja Rudolph*, Columbia University; Francisco J. R. Ruiz, ; Stephan Mandt, Disney Research; David Blei,
   
• Variational Information Maximization for Feature Selection
  Shuyang Gao*, ; Greg Ver Steeg, ; Aram Galstyan,
   
• Operator Variational Inference
  Rajesh Ranganath*, Princeton University; Dustin Tran, Columbia University; Jaan Altosaar, Princeton University; David Blei,
   
• Fast learning rates with heavy-tailed losses
  Vu Dinh*, Fred Hutchinson Cancer Center; Lam Ho, UCLA; Binh Nguyen, University of Science, Vietnam; Duy Nguyen, University of Wisconsin-Madison
   
• Budgeted stream-based active learning via adaptive submodular maximization
  Kaito Fujii*, Kyoto University; Hisashi Kashima, Kyoto University
   
• Learning feed-forward one-shot learners
  Luca Bertinetto, University of Oxford; Joao Henriques, University of Oxford; Jack Valmadre*, University of Oxford; Philip Torr, ; Andrea Vedaldi,
   
• Learning User Perceived Clusters with Feature-Level Supervision
  Ting-Yu Cheng, ; Kuan-Hua Lin, ; Xinyang Gong, Baidu Inc.; Kang-Jun Liu, ; Shan-Hung Wu*, National Tsing Hua University
   
• Robust Spectral Detection of Global Structures in the Data by Learning a Regularization
  Pan Zhang*, ITP, CAS
   
• Residual Networks are Exponential Ensembles of Relatively Shallow Networks
  Andreas Veit*, Cornell University; Michael Wilber, ; Serge Belongie, Cornell University
   
• Adversarial Multiclass Classification: A Risk Minimization Perspective
  Rizal Fathony*, U. of Illinois at Chicago; Anqi Liu, ; Kaiser Asif, ; Brian Ziebart,
   
• Solving Random Systems of Quadratic Equations via Truncated Generalized Gradient Flow
  Gang Wang*, University of Minnesota; Georgios Giannakis, University of Minnesota
   
• Coin Betting and Parameter-Free Online Learning
  Francesco Orabona*, Yahoo Research; David Pal,
   
• Deep Learning without Poor Local Minima
  Kenji Kawaguchi*, MIT
   
• Testing for Differences in Gaussian Graphical Models: Applications to Brain Connectivity
  Eugene Belilovsky*, CentraleSupelec; Gael Varoquaux, ; Matthew Blaschko, KU Leuven
   
• A Constant-Factor Bi-Criteria Approximation Guarantee for k-means++
  Dennis Wei*, IBM Research
   
• Generating Videos with Scene Dynamics
  Carl Vondrick*, MIT; Hamed Pirsiavash, ; Antonio Torralba,
   
• Neurally-Guided Procedural Models: Amortized Inference for Procedural Graphics Programs
  Daniel Ritchie*, Stanford University; Anna Thomas, Stanford University; Pat Hanrahan, Stanford University; Noah Goodman,
   
• A Powerful Generative Model Using Random Weights for the Deep Image Representation
  Kun He, Huazhong University of Science and Technology; Yan Wang*, HUAZHONG UNIVERSITY OF SCIENCE; John Hopcroft, Cornell University
   
• Optimizing affinity-based binary hashing using auxiliary coordinates
  Ramin Raziperchikolaei, UC Merced; Miguel Carreira-Perpinan*, UC Merced
   
• Double Thompson Sampling for Dueling Bandits
  Huasen Wu*, University of California at Davis; Xin Liu, University of California, Davis
   
• Generating Images with Perceptual Similarity Metrics based on Deep Networks
  Alexey Dosovitskiy*, ; Thomas Brox, University of Freiburg
   
• Dynamic Filter Networks
  Xu Jia*, KU Leuven; Bert De Brabandere, ; Tinne Tuytelaars, KU Leuven; Luc Van Gool, ETH Zürich
   
• A Simple Practical Accelerated Method for Finite Sums
  Aaron Defazio*, Ambiata
   
• Barzilai-Borwein Step Size for Stochastic Gradient Descent
  Conghui Tan*, The Chinese University of HK; Shiqian Ma, ; Yu-Hong Dai, ; Yuqiu Qian, The University of Hong Kong
   
• On Graph Reconstruction via Empirical Risk Minimization: Fast Learning Rates and Scalability
  Guillaume Papa, Télécom ParisTech; Aurélien Bellet*, ; Stephan Clémencon,
   
• Optimal spectral transportation with application to music transcription
  Rémi Flamary, ; Cédric Févotte*, CNRS; Nicolas Courty, ; Valentin Emiya, Aix-Marseille University
   
• Regularized Nonlinear Acceleration
  Damien Scieur*, INRIA - ENS; Alexandre D'Aspremont, ; Francis Bach,
   
• SPALS: Fast Alternating Least Squares via Implicit Leverage Scores Sampling
  Dehua Cheng*, Univ. of Southern California; Richard Peng, ; Yan Liu, ; Ioakeim Perros, Georgia Institute of Technology
   
• Single-Image Depth Perception in the Wild
  Weifeng Chen*, University of Michigan; Zhao Fu, University of Michigan; Dawei Yang, University of Michigan; Jia Deng,
   
• Computational and Statistical Tradeoffs in Learning to Rank
  Ashish Khetan*, University of Illinois Urbana-; Sewoong Oh,
   
• Learning to Poke by Poking: Experiential Learning of Intuitive Physics
  Pulkit Agrawal*, UC Berkeley; Ashvin Nair, UC Berkeley; Pieter Abbeel, ; Jitendra Malik, ; Sergey Levine, University of Washington
   
• Online Convex Optimization with Unconstrained Domains and Losses
  Ashok Cutkosky*, Stanford University; Kwabena Boahen, Stanford University
   
• An ensemble diversity approach to supervised binary hashing
  Miguel Carreira-Perpinan*, UC Merced; Ramin Raziperchikolaei, UC Merced
   
• Efficient Globally Convergent Stochastic Optimization for Canonical Correlation Analysis
  Weiran Wang*, ; Jialei Wang, University of Chicago; Dan Garber, ; Nathan Srebro,
   
• The Power of Adaptivity in Identifying Statistical Alternatives
  Kevin Jamieson*, UC Berkeley; Daniel Haas, ; Ben Recht,
   
• On Explore-Then-Commit strategies
  Aurelien Garivier, ; Tor Lattimore, ; Emilie Kaufmann*,
   
• Sublinear Time Orthogonal Tensor Decomposition
  Zhao Song*, UT-Austin; David Woodruff, ; Huan Zhang, UC-Davis
   
• DECOrrelated feature space partitioning for distributed sparse regression
  Xiangyu Wang*, Duke University; David Dunson, Duke University; Chenlei Leng, University of Warwick
   
• Deep Alternative Neural Networks: Exploring Contexts as Early as Possible for Action Recognition
  Jinzhuo Wang*, PKU; Wenmin Wang, peking university; xiongtao Chen, peking university; Ronggang Wang, peking university; Wen Gao, peking university
   
• Machine Translation Through Learning From a Communication Game
  Di He*, Microsoft; Yingce Xia, USTC; Tao Qin, Microsoft; Liwei Wang, ; Nenghai Yu, USTC; Tie-Yan Liu, Microsoft; wei-Ying Ma, Microsoft
   
• Dialog-based Language Learning
  Jason Weston*,
   
• Joint Line Segmentation and Transcription for End-to-End Handwritten Paragraph Recognition
  Theodore Bluche*, A2iA
   
• Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction
  Hsiang-Fu Yu*, University of Texas at Austin; Nikhil Rao, ; Inderjit Dhillon,
   
• Active Nearest-Neighbor Learning in Metric Spaces
  Aryeh Kontorovich, ; Sivan Sabato*, Ben-Gurion University of the Negev; Ruth Urner, MPI Tuebingen
   
• Proximal Deep Structured Models
  Shenlong Wang*, University of Toronto; Sanja Fidler, ; Raquel Urtasun,
   
• Faster Projection-free Convex Optimization over the Spectrahedron
  Dan Garber*,
   
• Bayesian Optimization with a Finite Budget: An Approximate Dynamic Programming Approach
  Remi Lam*, MIT; Karen Willcox, MIT; David Wolpert,
   
• Learning Sound Representations from Unlabeled Video
  Yusuf Aytar, MIT; Carl Vondrick*, MIT; Antonio Torralba,
   
• Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks
  Tim Salimans*, ; Diederik Kingma,
   
• Efficient Second Order Online Learning by Sketching
  Haipeng Luo*, Princeton University; Alekh Agarwal, Microsoft; Nicolò Cesa-Bianchi, ; John Langford,
   
• Dynamic Mode Decomposition with Reproducing Kernels for Koopman Spectral Analysis
  Yoshinobu Kawahara*, Osaka University
   
• Distributed Flexible Nonlinear Tensor Factorization
  Shandian Zhe*, Purdue University; Kai Zhang, Lawrence Berkeley Lab; Pengyuan Wang, Yahoo! Research; Kuang-chih Lee, ; Zenglin Xu, ; Alan Qi, ; Zoubin Ghahramani,
   
• The Robustness of Estimator Composition
  Pingfan Tang*, University of Utah; Jeff Phillips, University of Utah
   
• Efficient and Robust Spiking Neural Circuit for Navigation Inspired by Echolocating Bats
  Bipin Rajendran*, NJIT; Pulkit Tandon, IIT Bombay; Yash Malviya, IIT Bombay
   
• PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions
  Michael Figurnov*, Skolkovo Inst. of Sc and Tech; Aijan Ibraimova, Skolkovo Institute of Science and Technology; Dmitry P. Vetrov, ; Pushmeet Kohli,
   
• Differential Privacy without Sensitivity
  Kentaro Minami*, The University of Tokyo; HItomi Arai, The University of Tokyo; Issei Sato, The University of Tokyo; Hiroshi Nakagawa,
   
• Optimal Cluster Recovery in the Labeled Stochastic Block Model
  Se-Young Yun*, Los Alamos National Laboratory; Alexandre Proutiere,
   
• Even Faster SVD Decomposition Yet Without Agonizing Pain
  Zeyuan Allen-Zhu*, Princeton University; Yuanzhi Li, Princeton University
   
• An algorithm for L1 nearest neighbor search via monotonic embedding
  Xinan Wang*, UCSD; Sanjoy Dasgupta,
   
• Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations
  Kirthevasan Kandasamy*, CMU; Gautam Dasarathy, Carnegie Mellon University; Junier Oliva, ; Jeff Schneider, CMU; Barnabas Poczos,
   
• Linear-Memory and Decomposition-Invariant Linearly Convergent Conditional Gradient Algorithm for Structured Polytopes
  Dan Garber*, ; Ofer Meshi,
   
• Efficient Nonparametric Smoothness Estimation
  Shashank Singh*, Carnegie Mellon University; Simon Du, Carnegie Mellon University; Barnabas Poczos,
   
• A Theoretically Grounded Application of Dropout in Recurrent Neural Networks
  Yarin Gal*, University of Cambridge; Zoubin Ghahramani,
   
• Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation
  George Papamakarios*, University of Edinburgh; Iain Murray, University of Edinburgh
   
• Direct Feedback Alignment Provides Learning In Deep Neural Networks
  Arild Nøkland*, None
   
• Safe and Efficient Off-Policy Reinforcement Learning
  Remi Munos, Google DeepMind; Thomas Stepleton, Google DeepMind; Anna Harutyunyan, Vrije Universiteit Brussel; Marc Bellemare*, Google DeepMind
   
• A Multi-Batch L-BFGS Method for Machine Learning
  Albert Berahas*, Northwestern University; Jorge Nocedal, Northwestern University; Martin Takac, Lehigh University
   
• Semiparametric Differential Graph Models
  Pan Xu*, University of Virginia; Quanquan Gu, University of Virginia
   
• Rényi Divergence Variational Inference
  Yingzhen Li*, University of Cambridge; Richard E. Turner,
   
• Doubly Convolutional Neural Networks
  Shuangfei Zhai*, Binghamton University; Yu Cheng, IBM Research; Zhongfei Zhang, Binghamton University
   
• Density Estimation via Discrepancy Based Adaptive Sequential Partition
  Dangna Li*, Stanford university; Kun Yang, Google Inc; Wing Wong, Stanford university
   
• How Deep is the Feature Analysis underlying Rapid Visual Categorization?
  Sven Eberhardt*, Brown University; Jonah Cader, Brown University; Thomas Serre,
   
• Variational Information Maximizing Exploration
  Rein Houthooft*, Ghent University - iMinds; UC Berkeley; OpenAI; Xi Chen, UC Berkeley; OpenAI; Yan Duan, UC Berkeley; John Schulman, OpenAI; Filip De Turck, Ghent University - iMinds; Pieter Abbeel,
   
• Generalized Correspondence-LDA Models (GC-LDA) for Identifying Functional Regions in the Brain
  Timothy Rubin*, Indiana University; Sanmi Koyejo, UIUC; Michael Jones, Indiana University; Tal Yarkoni, University of Texas at Austin
   
• Solving Marginal MAP Problems with NP Oracles and Parity Constraints
  Yexiang Xue*, Cornell University; Zhiyuan Li, Tsinghua University; Stefano Ermon, ; Carla Gomes, Cornell University; Bart Selman,
   
• Multi-view Anomaly Detection via Robust Probabilistic Latent Variable Models
  Tomoharu Iwata*, ; Makoto Yamada,
   
• Fast Stochastic Methods for Nonsmooth Nonconvex Optimization
  Sashank Jakkam Reddi*, Carnegie Mellon University; Suvrit Sra, MIT; Barnabas Poczos, ; Alexander J. Smola,
   
• Variance Reduction in Stochastic Gradient Langevin Dynamics
  Kumar Dubey*, Carnegie Mellon University; Sashank Jakkam Reddi, Carnegie Mellon University; Sinead Williamson, ; Barnabas Poczos, ; Alexander J. Smola, ; Eric Xing, Carnegie Mellon University
   
• Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning
  Mehdi Sajjadi*, University of Utah; Mehran Javanmardi, University of Utah; Tolga Tasdizen, University of Utah
   
• Dense Associative Memory for Pattern Recognition
  Dmitry Krotov*, Institute for Advanced Study; John Hopfield, Princeton Neuroscience Institute
   
• Causal Bandits: Learning Good Interventions via Causal Inference
  Finnian Lattimore, Australian National University; Tor Lattimore*, ; Mark Reid,
   
• Refined Lower Bounds for Adversarial Bandits
  Sébastien Gerchinovitz, ; Tor Lattimore*,
   
• Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning
  Gang Niu*, University of Tokyo; Marthinus du Plessis, ; Tomoya Sakai, ; Yao Ma, ; Masashi Sugiyama, RIKEN / University of Tokyo
   
• Homotopy Smoothing for Non-Smooth Problems with Lower Complexity than $O(1/\epsilon)$
  Yi Xu*, The University of Iowa; Yan Yan, University of Technology Sydney; Qihang Lin, ; Tianbao Yang, University of Iowa
   
• Finite-Sample Analysis of Fixed-k Nearest Neighbor Density Functionals Estimators
  Shashank Singh*, Carnegie Mellon University; Barnabas Poczos,
   
• A state-space model of cross-region dynamic connectivity in MEG/EEG
  Ying Yang*, Carnegie Mellon University; Elissa Aminoff, Carnegie Mellon University; Michael Tarr, Carnegie Mellon University; Robert Kass, Carnegie Mellon University
   
• What Makes Objects Similar: A Unified Multi-Metric Learning Approach
  Han-Jia Ye, ; De-Chuan Zhan*, ; Xue-Min Si, Nanjing University; Yuan Jiang, Nanjing University; Zhi-Hua Zhou,
   
• Adaptive Maximization of Pointwise Submodular Functions With Budget Constraint
  Nguyen Viet Cuong*, National University of Singapore; Huan Xu, NUS
   
• Dueling Bandits: Beyond Condorcet Winners to General Tournament Solutions
  Siddartha Ramamohan, Indian Institute of Science; Arun Rajkumar, ; Shivani Agarwal*, Radcliffe Institute, Harvard
   
• Local Similarity-Aware Deep Feature Embedding
  Chen Huang*, Chinese University of HongKong; Chen Change Loy, The Chinese University of HK; Xiaoou Tang, The Chinese University of Hong Kong
   
• A Communication-Efficient Parallel Algorithm for Decision Tree
  Qi Meng*, Peking University; Guolin Ke, Microsoft Research; Taifeng Wang, Microsoft Research; Wei Chen, Microsoft Research; Qiwei Ye, Microsoft Research; Zhi-Ming Ma, Academy of Mathematics and Systems Science, Chinese Academy of Sciences; Tie-Yan Liu, Microsoft Research
   
• Convex Two-Layer Modeling with Latent Structure
  Vignesh Ganapathiraman, University Of Illinois at Chicago; Xinhua Zhang*, UIC; Yaoliang Yu, ; Junfeng Wen, UofA
   
• Sampling for Bayesian Program Learning
  Kevin Ellis*, MIT; Armando Solar-Lezama, MIT; Joshua Tenenbaum,
   
• Learning Kernels with Random Features
  Aman Sinha*, Stanford University; John Duchi,
   
• Optimal Tagging with Markov Chain Optimization
  Nir Rosenfeld*, Hebrew University of Jerusalem; Amir Globerson, Tel Aviv University
   
• Crowdsourced Clustering: Querying Edges vs Triangles
  Ramya Korlakai Vinayak*, Caltech; Hassibi Babak, Caltech
   
• Mixed vine copulas as joint models of spike counts and local field potentials
  Arno Onken*, IIT; Stefano Panzeri, IIT
   
• Achieving the KS threshold in the general stochastic block model with linearized acyclic belief propagation
  Emmanuel Abbe*, ; Colin Sandon,
   
• Adaptive Concentration Inequalities for Sequential Decision Problems
  Shengjia Zhao*, Tsinghua University; Enze Zhou, Tsinghua University; Ashish Sabharwal, Allen Institute for AI; Stefano Ermon,
• Fast mini-batch k-means by nesting
  James Newling*, Idiap Research Institute; Francois Fleuret, Idiap Research Institute
   
• Deep Learning Models of the Retinal Response to Natural Scenes
  Lane McIntosh*, Stanford University; Niru Maheswaranathan, Stanford University; Aran Nayebi, Stanford University; Surya Ganguli, Stanford; Stephen Baccus, Stanford University
   
• Preference Completion from Partial Rankings
  Suriya Gunasekar*, UT Austin; Sanmi Koyejo, UIUC; Joydeep Ghosh, UT Austin
   
• Dynamic Network Surgery for Efficient DNNs
  Yiwen Guo*, Intel Labs China; Anbang Yao, ; Yurong Chen,
   
• Learning a Metric Embedding for Face Recognition using the Multibatch Method
  Oren Tadmor, OrCam; Tal Rosenwein, Orcam; Shai Shalev-Shwartz, OrCam; Yonatan Wexler*, OrCam; Amnon Shashua, OrCam
   
• A Pseudo-Bayesian Algorithm for Robust PCA
  Tae-Hyun Oh*, KAIST; David Wipf, ; Yasuyuki Matsushita, Osaka University; In So Kweon, KAIST
   
• End-to-End Kernel Learning with Supervised Convolutional Kernel Networks
  Julien Mairal*, Inria
   
• Stochastic Variance Reduction Methods for Saddle-Point Problems
  P. Balamurugan, ; Francis Bach*,
   
• Flexible Models for Microclustering with Applications to Entity Resolution
  Brenda Betancourt, Duke University; Giacomo Zanella, The University of Warick; Jeffrey Miller, Duke University; Hanna Wallach, Microsoft Research; Abbas Zaidi, Duke University; Rebecca C. Steorts*, Duke University
   
• Catching heuristics are optimal control policies
  Boris Belousov*, TU Darmstadt; Gerhard Neumann, ; Constantin Rothkopf, ; Jan Peters,
   
• Bayesian optimization under mixed constraints with a slack-variable augmented Lagrangian
  Victor Picheny, Institut National de la Recherche Agronomique; Robert Gramacy*, ; Stefan Wild, Argonne National Lab; Sebastien Le Digabel, École Polytechnique de Montréal
   
• Adaptive Neural Compilation
  Rudy Bunel*, Oxford University; Alban Desmaison, Oxford; M. Pawan Kumar, University of Oxford; Pushmeet Kohli, ; Philip Torr,
   
• Synthesis of MCMC and Belief Propagation
  Sung-Soo Ahn*, KAIST; Misha Chertkov, Los Alamos National Laboratory; Jinwoo Shin, KAIST
   
• Learning Treewidth-Bounded Bayesian Networks with Thousands of Variables
  Mauro Scanagatta*, Idsia; Giorgio Corani, Idsia; Cassio Polpo de Campos, Queen's University Belfast; Marco Zaffalon, IDSIA
   
• Unifying Count-Based Exploration and Intrinsic Motivation
  Marc Bellemare*, Google DeepMind; Srinivasan Sriram, ; Georg Ostrovski, Google DeepMind; Tom Schaul, ; David Saxton, Google DeepMind; Remi Munos, Google DeepMind
   
• Large Margin Discriminant Dimensionality Reduction in Prediction Space
  Mohammad Saberian*, Netflix; Jose Costa Pereira, UC San Diego; Nuno Nvasconcelos, UC San Diego
   
• Stochastic Structured Prediction under Bandit Feedback
  Artem Sokolov, Heidelberg University; Julia Kreutzer, Heidelberg University; Stefan Riezler*, Heidelberg University
   
• Simple and Efficient Weighted Minwise Hashing
  Anshumali Shrivastava*, Rice University
   
• Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and Level-Set Estimation
  Ilija Bogunovic*, EPFL Lausanne; Jonathan Scarlett, ; Andreas Krause, ; Volkan Cevher,
   
• Structured Sparse Regression via Greedy Hard Thresholding
  Prateek Jain, Microsoft Research; Nikhil Rao*, ; Inderjit Dhillon,
   
• Understanding Probabilistic Sparse Gaussian Process Approximations
  Matthias Bauer*, University of Cambridge; Mark van der Wilk, University of Cambridge; Carl Rasmussen, University of Cambridge
   
• SEBOOST - Boosting Stochastic Learning Using Subspace Optimization Techniques
  Elad Richardson*, Technion; Rom Herskovitz, ; Boris Ginsburg, ; Michael Zibulevsky,
   
• Long-Term Trajectory Planning Using Hierarchical Memory Networks
  Stephan Zheng*, Caltech; Yisong Yue, ; Patrick Lucey, Stats
   
• Learning Tree Structured Potential Games
  Vikas Garg*, MIT; Tommi Jaakkola,
   
• Observational-Interventional Priors for Dose-Response Learning
  Ricardo Silva*,
   
• Learning from Rational Behavior: Predicting Solutions to Unknown Linear Programs
  Shahin Jabbari*, University of Pennsylvania; Ryan Rogers, University of Pennsylvania; Aaron Roth, ; Steven Wu, University of Pennsylvania
   
• Identification and Overidentification of Linear Structural Equation Models
  Bryant Chen*, UCLA
   
• Adaptive Skills Adaptive Partitions (ASAP)
  Daniel Mankowitz*, Technion; Timothy Mann, Google DeepMind; Shie Mannor, Technion
   
• Multiple-Play Bandits in the Position-Based Model
  Paul Lagrée*, Université Paris Sud; Claire Vernade, Université Paris Saclay; Olivier Cappe,
   
• Optimal Black-Box Reductions Between Optimization Objectives
  Zeyuan Allen-Zhu*, Princeton University; Elad Hazan,
   
• On Valid Optimal Assignment Kernels and Applications to Graph Classification
  Nils Kriege*, TU Dortmund; Pierre-Louis Giscard, University of York; Richard Wilson, University of York
   
• Robustness of classifiers: from adversarial to random noise
  Alhussein Fawzi, ; Seyed-Mohsen Moosavi-Dezfooli*, EPFL; Pascal Frossard, EPFL
   
• A Non-convex One-Pass Framework for Factorization Machines and Rank-One Matrix Sensing
  Ming Lin*, ; Jieping Ye,
   
• Exploiting the Structure: Stochastic Gradient Methods Using Raw Clusters
  Zeyuan Allen-Zhu*, Princeton University; Yang Yuan, Cornell University; Karthik Sridharan, University of Pennsylvania
   
• Combinatorial Multi-Armed Bandit with General Reward Functions
  Wei Chen*, ; Wei Hu, Princeton University; Fu Li, The University of Texas at Austin; Jian Li, Tsinghua University; Yu Liu, Tsinghua University; Pinyan Lu, Shanghai University of Finance and Economics
   
• Boosting with Abstention
  Corinna Cortes, ; Giulia DeSalvo*, ; Mehryar Mohri,
   
• Regret of Queueing Bandits
  Subhashini Krishnasamy, The University of Texas at Austin; Rajat Sen, The University of Texas at Austin; Ramesh Johari, ; Sanjay Shakkottai*, The University of Texas at Aus
   
• Deep Learning Games
  Dale Schuurmans*, ; Martin Zinkevich, Google
   
• Globally Optimal Training of Generalized Polynomial Neural Networks with Nonlinear Spectral Methods
  Antoine Gautier*, Saarland University; Quynh Nguyen, Saarland University; Matthias Hein, Saarland University
   
• Learning Volumetric 3D Object Reconstruction from Single-View with Projective Transformations
  Xinchen Yan*, University of Michigan; Jimei Yang, ; Ersin Yumer, Adobe Research; Yijie Guo, University of Michigan; Honglak Lee, University of Michigan
   
• A Credit Assignment Compiler for Joint Prediction
  Kai-Wei Chang*, ; He He, University of Maryland; Stephane Ross, Google; Hal III, ; John Langford,
   
• Accelerating Stochastic Composition Optimization
  Mengdi Wang*, ; Ji Liu,
   
• Reward Augmented Maximum Likelihood for Neural Structured Prediction
  Mohammad Norouzi*, ; Dale Schuurmans, ; Samy Bengio, ; zhifeng Chen, ; Navdeep Jaitly, ; Mike Schuster, ; Yonghui Wu,
   
• Consistent Kernel Mean Estimation for Functions of Random Variables
  Adam Scibior*, University of Cambridge; Carl-Johann Simon-Gabriel, MPI Tuebingen; Iliya Tolstikhin, ; Bernhard Schoelkopf,
   
• Towards Unifying Hamiltonian Monte Carlo and Slice Sampling
  Yizhe Zhang*, Duke university; Xiangyu Wang, Duke University; Changyou Chen, ; Ricardo Henao, ; Kai Fan, Duke university; Lawrence Carin,
   
• Scalable Adaptive Stochastic Optimization Using Random Projections
  Gabriel Krummenacher*, ETH Zurich; Brian Mcwilliams, Disney Research; Yannic Kilcher, ETH Zurich; Joachim Buhmann, ETH Zurich; Nicolai Meinshausen,
   
• Variational Inference in Mixed Probabilistic Submodular Models
  Josip Djolonga, ETH Zurich; Sebastian Tschiatschek*, ETH Zurich; Andreas Krause,
   
• Correlated-PCA: Principal Components' Analysis when Data and Noise are Correlated
  Namrata Vaswani*, ; Han Guo, Iowa State University
   
• The Multi-fidelity Multi-armed Bandit
  Kirthevasan Kandasamy*, CMU; Gautam Dasarathy, Carnegie Mellon University; Barnabas Poczos, ; Jeff Schneider, CMU
   
• Anchor-Free Correlated Topic Modeling: Identifiability and Algorithm
  Kejun Huang*, University of Minnesota; Xiao Fu, University of Minnesota; Nicholas Sidiropoulos, University of Minnesota
   
• Bootstrap Model Aggregation for Distributed Statistical Learning
  JUN HAN, Dartmouth College; Qiang Liu*,
   
• A scalable end-to-end Gaussian process adapter for irregularly sampled time series classification
  Steven Cheng-Xian Li*, UMass Amherst; Benjamin Marlin,
   
• A Bandit Framework for Strategic Regression
  Yang Liu*, Harvard University; Yiling Chen,
   
• Architectural Complexity Measures of Recurrent Neural Networks
  Saizheng Zhang*, University of Montreal; Yuhuai Wu, University of Toronto; Tong Che, IHES; Zhouhan Lin, University of Montreal; Roland Memisevic, University of Montreal; Ruslan Salakhutdinov, University of Toronto; Yoshua Bengio, U. Montreal
   
• Statistical Inference for Cluster Trees
  Jisu Kim*, Carnegie Mellon University; Yen-Chi Chen, Carnegie Mellon University; Sivaraman Balakrishnan, Carnegie Mellon University; Alessandro Rinaldo, Carnegie Mellon University; Larry Wasserman, Carnegie Mellon University
   
• Contextual-MDPs for PAC Reinforcement Learning with Rich Observations
  Akshay Krishnamurthy*, ; Alekh Agarwal, Microsoft; John Langford,
   
• Improved Deep Metric Learning with Multi-class N-pair Loss Objective
  Kihyuk Sohn*,
   
• Only H is left: Near-tight Episodic PAC RL
  Christoph Dann*, Carnegie Mellon University; Emma Brunskill, Carnegie Mellon University
  •  
• Unsupervised Learning of Spoken Language with Visual Context
  David Harwath*, MIT CSAIL; Antonio Torralba, MIT CSAIL; James Glass, MIT CSAIL
   
• Low-Rank Regression with Tensor Responses
  Guillaume Rabusseau*, Aix-Marseille University; Hachem Kadri,
   
• PAC-Bayesian Theory Meets Bayesian Inference
  Pascal Germain*, ; Francis Bach, ; Alexandre Lacoste, ; Simon Lacoste-Julien, INRIA
   
• Data Poisoning Attacks on Factorization-Based Collaborative Filtering
  Bo Li*, Vanderbilt University; Yining Wang, Carnegie Mellon University; Aarti Singh, Carnegie Mellon University; yevgeniy Vorobeychik, Vanderbilt University
   
• Learned Region Sparsity and Diversity Also Predicts Visual Attention
  Zijun Wei*, Stony Brook; Hossein Adeli, ; Minh Hoai, ; Gregory Zelinsky, ; Dimitris Samaras,
   
• End-to-End Goal-Driven Web Navigation
  Rodrigo Frassetto Nogueira*, New York University; Kyunghyun Cho, University of Montreal
   
• Automated scalable segmentation of neurons from multispectral images
  Uygar Sümbül*, Columbia University; Douglas Roossien, University of Michigan, Ann Arbor; Dawen Cai, University of Michigan, Ann Arbor; John Cunningham, Columbia University; Liam Paninski,
   
• Privacy Odometers and Filters: Pay-as-you-Go Composition
  Ryan Rogers*, University of Pennsylvania; Salil Vadhan, Harvard University; Aaron Roth, ; Jonathan Robert Ullman,
   
• Minimax Estimation of Maximal Mean Discrepancy with Radial Kernels
  Iliya Tolstikhin*, ; Bharath Sriperumbudur, ; Bernhard Schoelkopf,
   
• Adaptive optimal training of animal behavior
  Ji Hyun Bak*, Princeton University; Jung Yoon Choi, ; Ilana Witten, ; Jonathan Pillow,
   
• Hierarchical Object Representation for Open-Ended Object Category Learning and Recognition
  Hamidreza Kasaei*, IEETA, University of Aveiro
   
• Relevant sparse codes with variational information bottleneck
  Matthew Chalk*, IST Austria; Olivier Marre, Institut de la vision; Gašper Tkačik, Institute of Science and Technology Austria
   
• Combinatorial Energy Learning for Image Segmentation
  Jeremy Maitin-Shepard*, Google; Viren Jain, Google; Michal Januszewski, Google; Peter Li, ; Pieter Abbeel,
   
• Orthogonal Random Features
  Felix Xinnan Yu*, ; Ananda Theertha Suresh, ; Krzysztof Choromanski, ; Dan Holtmann-Rice, ; Sanjiv Kumar, Google
   
• Fast Active Set Methods for Online Spike Inference from Calcium Imaging
  Johannes Friedrich*, Columbia University; Liam Paninski,
   
• Diffusion-Convolutional Neural Networks
  James Atwood*, UMass Amherst
   
• Bayesian latent structure discovery from multi-neuron recordings
  Scott Linderman*, ; Ryan Adams, ; Jonathan Pillow,
   
• A Probabilistic Programming Approach To Probabilistic Data Analysis
  Feras Saad*, MIT; Vikash Mansinghka, MIT
   
• A Non-parametric Learning Method for Confidently Estimating Patient's Clinical State and Dynamics
  William Hoiles*, University of California, Los ; Mihaela Van Der Schaar,
   
• Inference by Reparameterization in Neural Population Codes
  RAJKUMAR VASUDEVA RAJU, Rice University; Xaq Pitkow*,
   
• Tensor Switching Networks
  Chuan-Yung Tsai*, ; Andrew Saxe, ; David Cox,
   
• Stochastic Gradient Richardson-Romberg Markov Chain Monte Carlo
  Alain Durmus, Telecom ParisTech; Umut Simsekli*, ; Eric Moulines, Ecole Polytechnique; Roland Badeau, Telecom ParisTech; Gaël Richard, Telecom ParisTech
   
• Coordinate-wise Power Method
  Qi Lei*, UT AUSTIN; Kai Zhong, UT AUSTIN; Inderjit Dhillon,
   
• Learning Influence Functions from Incomplete Observations
  Xinran He*, USC; Ke Xu, USC; David Kempe, USC; Yan Liu,
   
• Learning Structured Sparsity in Deep Neural Networks
  Wei Wen*, University of Pittsburgh; Chunpeng Wu, University of Pittsburgh; Yandan Wang, University of Pittsburgh; Yiran Chen, University of Pittsburgh; Hai Li, University of Pittsburg
   
• Sample Complexity of Automated Mechanism Design
  Nina Balcan, ; Tuomas Sandholm, Carnegie Mellon University; Ellen Vitercik*, Carnegie Mellon University
   
• Short-Dot: Computing Large Linear Transforms Distributedly Using Coded Short Dot Products
  SANGHAMITRA DUTTA*, Carnegie Mellon University; Viveck Cadambe, Pennsylvania State University; Pulkit Grover, Carnegie Mellon University
   
• Brains on Beats
  Umut Güçlü*, Radboud University; Jordy Thielen, Radboud University; Michael Hanke, Otto-von-Guericke University Magdeburg; Marcel Van Gerven, Radboud University
   
• Learning Transferrable Representations for Unsupervised Domain Adaptation
  Ozan Sener*, Cornell University; Hyun Oh Song, Google Research; Ashutosh Saxena, Brain of Things; Silvio Savarese, Stanford University
   
• Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles
  Stefan Lee*, Indiana University; Senthil Purushwalkam, Carnegie Mellon; Michael Cogswell, Virginia Tech; Viresh Ranjan, Virginia Tech; David Crandall, Indiana University; Dhruv Batra,
   
• Active Learning from Imperfect Labelers
  Songbai Yan*, University of California, San Diego; Kamalika Chaudhuri, University of California, San Diego; Tara Javidi, University of California, San Diego
   
• Learning to Communicate with Deep Multi-Agent Reinforcement Learning
  Jakob Foerster*, University of Oxford; Yannis Assael, University of Oxford; Nando de Freitas, University of Oxford; Shimon Whiteson,
   
• Value Iteration Networks
  Aviv Tamar*, ; Sergey Levine, ; Pieter Abbeel, ; Yi Wu, UC Berkeley; Garrett Thomas, UC Berkeley
   
• Blind Regression: Nonparametric Regression for Latent Variable Models via Collaborative Filtering
  Dogyoon Song*, MIT; Christina Lee, MIT; Yihua Li, MIT; Devavrat Shah,
   
• On the Recursive Teaching Dimension of VC Classes
  Bo Tang*, University of Oxford; Xi Chen, Columbia University; Yu Cheng, U of Southern California
   
• InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
  Xi Chen*, UC Berkeley; OpenAI; Yan Duan, UC Berkeley; Rein Houthooft, Ghent University - iMinds; UC Berkeley; OpenAI; John Schulman, OpenAI; Ilya Sutskever, ; Pieter Abbeel,
   
• Hardness of Online Sleeping Combinatorial Optimization Problems
  Satyen Kale*, ; Chansoo Lee, ; David Pal,
   
• Mixed Linear Regression with Multiple Components
  Kai Zhong*, UT AUSTIN; Prateek Jain, Microsoft Research; Inderjit Dhillon,
   
• Sequential Neural Models with Stochastic Layers
  Marco Fraccaro*, DTU; Søren Sønderby, KU; Ulrich Paquet, ; Ole Winther, DTU
   
• Stochastic Gradient Methods for Distributionally Robust Optimization with f-divergences
  Hongseok Namkoong*, Stanford University; John Duchi,
   
• Minimizing Quadratic Functions in Constant Time
  Kohei Hayashi*, AIST; Yuichi Yoshida, NII
   
• Improved Techniques for Training GANs
  Tim Salimans*, ; Ian Goodfellow, OpenAI; Wojciech Zaremba, OpenAI; Vicki Cheung, OpenAI; Alec Radford, OpenAI; Xi Chen, UC Berkeley; OpenAI
   
• DeepMath - Deep Sequence Models for Premise Selection
  Geoffrey Irving*, ; Christian Szegedy, ; Alexander Alemi, Google; Francois Chollet, ; Josef Urban, Czech Technical University in Prague
   
• Learning Multiagent Communication with Backpropagation
  Sainbayar Sukhbaatar, NYU; Arthur Szlam, ; Rob Fergus*, New York University
• Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity
  Amit Daniely*, ; Roy Frostig, Stanford University; Yoram Singer, Google
   
• Learning the Number of Neurons in Deep Networks
  Jose Alvarez*, NICTA; Mathieu Salzmann, EPFL
   
• Finding significant combinations of features in the presence of categorical covariates
  Laetitia Papaxanthos*, ETH Zurich; Felipe Llinares, ETH Zurich; Dean Bodenham, ETH Zurich; Karsten Borgwardt,
   
• Examples are not Enough, Learn to Criticize! Model Criticism for Interpretable Machine Learning
  Been Kim*, ; Rajiv Khanna, UT Austin; Sanmi Koyejo, UIUC
   
• Optimistic Bandit Convex Optimization
  Scott Yang*, New York University; Mehryar Mohri,
   
• Safe Policy Improvement by Minimizing Robust Baseline Regret
  Mohamad Ghavamzadeh*, ; Marek Petrik, ; Yinlam Chow, Stanford University
   
• Graphons, mergeons, and so on!
  Justin Eldridge*, The Ohio State University; Mikhail Belkin, ; Yusu Wang, The Ohio State University
   
• Hierarchical Clustering via Spreading Metrics
  Aurko Roy*, Georgia Tech; Sebastian Pokutta, GeorgiaTech
   
• Learning Bayesian networks with ancestral constraints
  Eunice Yuh-Jie Chen*, UCLA; Yujia Shen, ; Arthur Choi, ; Adnan Darwiche,
   
• Pruning Random Forests for Prediction on a Budget
  Feng Nan*, Boston University; Joseph Wang, Boston University; Venkatesh Saligrama,
   
• Clustering with Bregman Divergences: an Asymptotic Analysis
  Chaoyue Liu*, The Ohio State University; Mikhail Belkin,
   
• Variational Autoencoder for Deep Learning of Images, Labels and Captions
  Yunchen Pu*, Duke University; Zhe Gan, Duke; Ricardo Henao, ; Xin Yuan, Bell Labs; chunyuan Li, Duke; Andrew Stevens, Duke University; Lawrence Carin,
   
• Encode, Review, and Decode: Reviewer Module for Caption Generation
  Zhilin Yang*, Carnegie Mellon University; Ye Yuan, Carnegie Mellon University; Yuexin Wu, Carnegie Mellon University; William Cohen, Carnegie Mellon University; Ruslan Salakhutdinov, University of Toronto
   
• Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm
  Qiang Liu*, ; Dilin Wang, Dartmouth College
   
• A Bio-inspired Redundant Sensing Architecture
  Anh Tuan Nguyen*, University of Minnesota; Jian Xu, University of Minnesota; Zhi Yang, University of Minnesota
   
• Contextual semibandits via supervised learning oracles
  Akshay Krishnamurthy*, ; Alekh Agarwal, Microsoft; Miro Dudik,
   
• Blind Attacks on Machine Learners
  Alex Beatson*, Princeton University; Zhaoran Wang, Princeton University; Han Liu,
   
• Universal Correspondence Network
  Christopher Choy*, Stanford University; Manmohan Chandraker, NEC Labs America; JunYoung Gwak, Stanford University; Silvio Savarese, Stanford University
   
• Satisfying Real-world Goals with Dataset Constraints
  Gabriel Goh*, UC Davis; Andy Cotter, ; Maya Gupta, ; Michael Friedlander, UC Davis
   
• Deep Learning for Predicting Human Strategic Behavior
  Jason Hartford*, University of British Columbia; Kevin Leyton-Brown, ; James Wright, University of British Columbia
   
• Phased Exploration with Greedy Exploitation in Stochastic Combinatorial Partial Monitoring Games
  Sougata Chaudhuri*, University of Michigan ; Ambuj Tewari, University of Michigan
   
• Eliciting and Aggregating Categorical Data
  Yiling Chen, ; Rafael Frongillo, ; Chien-Ju Ho*,
   
• Measuring the reliability of MCMC inference with Bidirectional Monte Carlo
  Roger Grosse, ; Siddharth Ancha, University of Toronto; Daniel Roy*,
   
• Breaking the Bandwidth Barrier: Geometrical Adaptive Entropy Estimation
  Weihao Gao, UIUC; Sewoong Oh*, ; Pramod Viswanath, UIUC
   
• Selective inference for group-sparse linear models
  Fan Yang, University of Chicago; Rina Foygel Barber*, ; Prateek Jain, Microsoft Research; John Lafferty,
   
• Graph Clustering: Block-models and model free results
  Yali Wan*, University of Washington; Marina Meila, University of Washington
   
• Maximizing Influence in an Ising Network: A Mean-Field Optimal Solution
  Christopher Lynn*, University of Pennsylvania; Dan Lee , University of Pennsylvania
   
• Hypothesis Testing in Unsupervised Domain Adaptation with Applications in Neuroscience
  Hao Zhou, University of Wisconsin Madiso; Vamsi Ithapu*, University of Wisconsin Madison; Sathya Ravi, University of Wisconsin Madiso; Vikas Singh, UW Madison; Grace Wahba, University of Wisconsin Madison; Sterling Johnson, University of Wisconsin Madison
   
• Geometric Dirichlet Means Algorithm for Topic Inference
  Mikhail Yurochkin*, University of Michigan; Long Nguyen,
   
• Structured Prediction Theory Based on Factor Graph Complexity
  Corinna Cortes, ; Vitaly Kuznetsov*, Courant Institute; Mehryar Mohri, ; Scott Yang, New York University
   
• Improved Dropout for Shallow and Deep Learning
  Zhe Li, The University of Iowa; Boqing Gong, University of Central Florida; Tianbao Yang*, University of Iowa
   
• Constraints Based Convex Belief Propagation
  Yaniv Tenzer*, The Hebrew University; Alexander Schwing, ; Kevin Gimpel, ; Tamir Hazan,
   
• Error Analysis of Generalized Nyström Kernel Regression
  Hong Chen, University of Texas; Haifeng Xia, Huazhong Agricultural University; Heng Huang*, University of Texas Arlington
   
• A Probabilistic Framework for Deep Learning
  Ankit Patel, Baylor College of Medicine; Rice University; Tan Nguyen*, Rice University; Richard Baraniuk,
   
• General Tensor Spectral Co-clustering for Higher-Order Data
  Tao Wu*, Purdue University; Austin Benson, Stanford University; David Gleich,
   
• Cyclades: Conflict-free Asynchronous Machine Learning
  Xinghao Pan*, UC Berkeley; Stephen Tu, UC Berkeley; Maximilian Lam, UC Berkeley; Dimitris Papailiopoulos, ; Ce Zhang, Stanford; Michael Jordan, ; Kannan Ramchandran, ; Christopher Re, ; Ben Recht,
   
• Single Pass PCA of Matrix Products
  Shanshan Wu*, UT Austin; Srinadh Bhojanapalli, TTI Chicago; Sujay Sanghavi, ; Alexandros G. Dimakis,
   
• Stochastic Variational Deep Kernel Learning
  Andrew Wilson*, Carnegie Mellon University; Zhiting Hu, Carnegie Mellon University; Ruslan Salakhutdinov, University of Toronto; Eric Xing, Carnegie Mellon University
   
• Interaction Screening: Efficient and Sample-Optimal Learning of Ising Models
  Marc Vuffray*, Los Alamos National Laboratory; Sidhant Misra, Los Alamos National Laboratory; Andrey Lokhov, Los Alamos National Laboratory; Misha Chertkov, Los Alamos National Laboratory
   
• Long-term Causal Effects via Behavioral Game Theory
  Panos Toulis*, University of Chicago; David Parkes, Harvard University
   
• Measuring Neural Net Robustness with Constraints
  Osbert Bastani*, Stanford University; Yani Ioannou, University of Cambridge; Leonidas Lampropoulos, University of Pennsylvania; Dimitrios Vytiniotis, Microsoft Research; Aditya Nori, Microsoft Research; Antonio Criminisi,
   
• Reshaped Wirtinger Flow for Solving Quadratic Systems of Equations
  Huishuai Zhang*, Syracuse University; Yingbin Liang, Syracuse University
   
• Nearly Isometric Embedding by Relaxation
  James McQueen*, University of Washington; Marina Meila, University of Washington; Dominique Joncas, Google
   
• Probabilistic Inference with Generating Functions for Poisson Latent Variable Models
  Kevin Winner*, UMass CICS; Daniel Sheldon,
   
• Causal meets Submodular: Subset Selection with Directed Information
  Yuxun Zhou*, UC Berkeley; Costas Spanos,
   
• Depth from a Single Image by Harmonizing Overcomplete Local Network Predictions
  Ayan Chakrabarti*, ; Jingyu Shao, UCLA; Greg Shakhnarovich,
   
• Deep Neural Networks with Inexact Matching for Person Re-Identification
  Arulkumar Subramaniam, IIT Madras; Moitreya Chatterjee*, IIT Madras; Anurag Mittal, IIT Madras
   
• Global Analysis of Expectation Maximization for Mixtures of Two Gaussians
  Ji Xu, Columbia university; Daniel Hsu*, ; Arian Maleki, Columbia University
   
• Estimating the class prior and posterior from noisy positives and unlabeled data
  Shanatnu Jain*, Indiana University; Martha White, ; Predrag Radivojac,
   
• Kronecker Determinantal Point Processes
  Zelda Mariet*, MIT; Suvrit Sra, MIT
   
• Finite Sample Prediction and Recovery Bounds for Ordinal Embedding
  Lalit Jain*, University of Wisconsin-Madison; Kevin Jamieson, UC Berkeley; Robert Nowak, University of Wisconsin Madison
   
• Feature-distributed sparse regression: a screen-and-clean approach
  Jiyan Yang*, Stanford University; Michael Mahoney, ; Michael Saunders, Stanford University; Yuekai Sun, University of Michigan
   
• Learning Bound for Parameter Transfer Learning
  Wataru Kumagai*, Kanagawa University
   
• Learning under uncertainty: a comparison between R-W and Bayesian approach
  He Huang*, LIBR; Martin Paulus, LIBR
   
• Bi-Objective Online Matching and Submodular Allocations
  Hossein Esfandiari*, University of Maryland; Nitish Korula, Google Research; Vahab Mirrokni, Google
   
• Quantized Random Projections and Non-Linear Estimation of Cosine Similarity
  Ping Li, ; Michael Mitzenmacher, Harvard University; Martin Slawski*,
   
• The non-convex Burer-Monteiro approach works on smooth semidefinite programs
  Nicolas Boumal, ; Vlad Voroninski*, MIT; Afonso Bandeira,
   
• Dimensionality Reduction of Massive Sparse Datasets Using Coresets
  Dan Feldman, ; Mikhail Volkov*, MIT; Daniela Rus, MIT
   
• Using Social Dynamics to Make Individual Predictions: Variational Inference with Stochastic Kinetic Model
  Zhen Xu*, SUNY at Buffalo; Wen Dong, ; Sargur Srihari,
   
• Supervised learning through the lens of compression
  Ofir David*, Technion - Israel institute of technology; Shay Moran, Technion - Israel institue of Technology; Amir Yehudayoff, Technion - Israel institue of Technology
   
• Generative Shape Models: Joint Text Recognition and Segmentation with Very Little Training Data
  Xinghua Lou*, Vicarious FPC Inc; Ken Kansky, ; Wolfgang Lehrach, ; CC Laan, ; Bhaskara Marthi, ; D. Scott Phoenix, ; Dileep George,
   
• Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections
  Xiao-Jiao Mao, Nanjing University; Chunhua Shen*, ; Yu-Bin Yang,
   
• Object based Scene Representations using Fisher Scores of Local Subspace Projections
  Mandar Dixit*, UC San Diego; Nuno Vasconcelos,
   
• Active Learning with Oracle Epiphany
  Tzu-Kuo Huang, Microsoft Research; Lihong Li, Microsoft Research; Ara Vartanian, University of Wisconsin-Madison; Saleema Amershi, Microsoft; Xiaojin Zhu*,
   
• Statistical Inference for Pairwise Graphical Models Using Score Matching
  Ming Yu*, The University of Chicago; Mladen Kolar, ; Varun Gupta, University of Chicago
   
• Improved Error Bounds for Tree Representations of Metric Spaces
  Samir Chowdhury*, The Ohio State University; Facundo Memoli, ; Zane Smith,
   
• Can Peripheral Representations Improve Clutter Metrics on Complex Scenes?
  Arturo Deza*, UCSB; Miguel Eckstein, UCSB
   
• On Multiplicative Integration with Recurrent Neural Networks
  Yuhuai Wu*, University of Toronto; Saizheng Zhang, University of Montreal; ying Zhang, University of Montreal; Yoshua Bengio, U. Montreal; Ruslan Salakhutdinov, University of Toronto
   
• Learning HMMs with Nonparametric Emissions via Spectral Decompositions of Continuous Matrices
  Kirthevasan Kandasamy*, CMU; Maruan Al-Shedivat, CMU; Eric Xing, Carnegie Mellon University
   
• Regret Bounds for Non-decomposable Metrics with Missing Labels
  Nagarajan Natarajan*, Microsoft Research Bangalore; Prateek Jain, Microsoft Research
   
• Robust k-means: a Theoretical Revisit
  ALEXANDROS GEORGOGIANNIS*, TECHNICAL UNIVERSITY OF CRETE
   
• Bayesian optimization for automated model selection
  Gustavo Malkomes, Washington University; Charles Schaff, Washington University in St. Louis; Roman Garnett*,
   
• A Probabilistic Model of Social Decision Making based on Reward Maximization
  Koosha Khalvati*, University of Washington; Seongmin Park, Cognitive Neuroscience Center; Jean-Claude Dreher, Centre de Neurosciences Cognitives; Rajesh Rao, University of Washington
   
• Balancing Suspense and Surprise: Timely Decision Making with Endogenous Information Acquisition
  Ahmed Alaa*, UCLA; Mihaela Van Der Schaar,
   
• Fast and Flexible Monotonic Functions with Ensembles of Lattices
  Mahdi Fard, ; Kevin Canini, ; Andy Cotter, ; Jan Pfeifer, Google; Maya Gupta*,
   
• Conditional Generative Moment-Matching Networks
  Yong Ren, Tsinghua University; Jun Zhu*, ; Jialian Li, Tsinghua University; Yucen Luo,
   
• Stochastic Gradient MCMC with Stale Gradients
  Changyou Chen*, ; Nan Ding, Google; chunyuan Li, Duke; Yizhe Zhang, Duke university; Lawrence Carin,
   
• Composing graphical models with neural networks for structured representations and fast inference
  Matthew Johnson, ; David Duvenaud*, ; Alex Wiltschko, Harvard University and Twitter; Ryan Adams, ; Sandeep Datta, Harvard Medical School
   
• Noise-Tolerant Life-Long Matrix Completion via Adaptive Sampling
  Nina Balcan, ; Hongyang Zhang*, CMU
   
• Combinatorial semi-bandit with known covariance
  Rémy Degenne*, Université Paris Diderot; Vianney Perchet,
   
• Matrix Completion has No Spurious Local Minimum
  Rong Ge, ; Jason Lee, UC Berkeley; Tengyu Ma*, Princeton University
   
• The Multiscale Laplacian Graph Kernel
  Risi Kondor*, ; Horace Pan, UChicago
   
• Adaptive Averaging in Accelerated Descent Dynamics
  Walid Krichene*, UC Berkeley; Alexandre Bayen, UC Berkeley; Peter Bartlett,
   
• Sub-sampled Newton Methods with Non-uniform Sampling
  Peng Xu*, Stanford University; Jiyan Yang, Stanford University; Farbod Roosta-Khorasani, University of California Berkeley; Christopher Re, ; Michael Mahoney,
   
• Stochastic Gradient Geodesic MCMC Methods
  Chang Liu*, Tsinghua University; Jun Zhu, ; Yang Song, Stanford University
   
• Variational Bayes on Monte Carlo Steroids
  Aditya Grover*, Stanford University; Stefano Ermon,
   
• Showing versus doing: Teaching by demonstration
  Mark Ho*, Brown University; Michael L. Littman, ; James MacGlashan, Brown University; Fiery Cushman, Harvard University; Joe Austerweil,
   
• Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation
  Jianxu Chen*, University of Notre Dame; Lin Yang, University of Notre Dame; Yizhe Zhang, University of Notre Dame; Mark Alber, University of Notre Dame; Danny Chen, University of Notre Dame
   
• Maximization of Approximately Submodular Functions
  Thibaut Horel*, Harvard University; Yaron Singer,
   
• A Comprehensive Linear Speedup Analysis for Asynchronous Stochastic Parallel Optimization from Zeroth-Order to First-Order
  Xiangru Lian, University of Rochester; Huan Zhang, ; Cho-Jui Hsieh, ; Yijun Huang, ; Ji Liu*,
   
• Learning Infinite RBMs with Frank-Wolfe
  Wei Ping*, UC Irvine; Qiang Liu, ; Alexander Ihler,
   
• Estimating the Size of a Large Network and its Communities from a Random Sample
  Lin Chen*, Yale University; Amin Karbasi, ; Forrest Crawford, Yale University
   
• Learning Sensor Multiplexing Design through Back-propagation
  Ayan Chakrabarti*,
   
• On Robustness of Kernel Clustering
  Bowei Yan*, University of Texas at Austin; Purnamrita Sarkar, U.C. Berkeley
   
• High resolution neural connectivity from incomplete tracing data using nonnegative spline regression
  Kameron Harris*, University of Washington; Stefan Mihalas, Allen Institute for Brain Science; Eric Shea-Brown, University of Washington
   
• MoCap-guided Data Augmentation for 3D Pose Estimation in the Wild
  Gregory Rogez*, Inria; Cordelia Schmid,
   
• A New Liftable Class for First-Order Probabilistic Inference
  Seyed Mehran Kazemi*, UBC; Angelika Kimmig, KU Leuven; Guy Van den Broeck, ; David Poole, UBC
   
• The Parallel Knowledge Gradient Method for Batch Bayesian Optimization
  Jian Wu*, Cornell University; Peter I. Frazier,
   
• Improved Regret Bounds for Oracle-Based Adversarial Contextual Bandits
  Vasilis Syrgkanis*, ; Haipeng Luo, Princeton University; Akshay Krishnamurthy, ; Robert Schapire,
   
• Consistent Estimation of Functions of Data Missing Non-Monotonically and Not at Random
  Ilya Shpitser*,
   
• Optimistic Gittins Indices
  Eli Gutin*, Massachusetts Institute of Tec; Vivek Farias,
   
• Finite-Dimensional BFRY Priors and Variational Bayesian Inference for Power Law Models
  Juho Lee*, POSTECH; Lancelot James, HKUST; Seungjin Choi, POSTECH
   
• Launch and Iterate: Reducing Prediction Churn
  Mahdi Fard, ; Quentin Cormier, Google; Kevin Canini, ; Maya Gupta*,
   
• “Congruent” and “Opposite” Neurons: Sisters for Multisensory Integration and Segregation
  Wen-Hao Zhang*, Institute of Neuroscience, Chinese Academy of Sciences; He Wang, HKUST; K. Y. Michael Wong, HKUST; Si Wu,
   
• Learning shape correspondence with anisotropic convolutional neural networks
  Davide Boscaini*, University of Lugano; Jonathan Masci, ; Emanuele Rodolà, University of Lugano; Michael Bronstein, University of Lugano
   
• Pairwise Choice Markov Chains
  Stephen Ragain*, Stanford University; Johan Ugander,
   
• NESTT: A Nonconvex Primal-Dual Splitting Method for Distributed and Stochastic Optimization
  Davood Hajinezhad*, Iowa State University; Mingyi Hong, ; Tuo Zhao, Johns Hopkins University; Zhaoran Wang, Princeton University
   
• Clustering with Same-Cluster Queries
  Hassan Ashtiani, University of Waterloo; Shrinu Kushagra*, University of Waterloo; Shai Ben-David, U. Waterloo
   
• Attend, Infer, Repeat: Fast Scene Understanding with Generative Models
  S. M. Ali Eslami*, Google DeepMind; Nicolas Heess, ; Theophane Weber, ; Yuval Tassa, Google DeepMind; David Szepesvari, Google DeepMind; Koray Kavukcuoglu, Google DeepMind; Geoffrey Hinton, Google
   
• Parameter Learning for Log-supermodular Distributions
  Tatiana Shpakova*, Inria - ENS Paris; Francis Bach,
   
• Deconvolving Feedback Loops in Recommender Systems
  Ayan Sinha*, Purdue; David Gleich, ; Karthik Ramani, Purdue University
   
• Structured Matrix Recovery via the Generalized Dantzig Selector
  Sheng Chen*, University of Minnesota; Arindam Banerjee,
   
• Confusions over Time: An Interpretable Bayesian Model to Characterize Trends in Decision Making
  Himabindu Lakkaraju*, Stanford University; Jure Leskovec,
   
• Automatic Neuron Detection in Calcium Imaging Data Using Convolutional Networks
  Noah Apthorpe*, Princeton University; Alexander Riordan, Princeton University; Robert Aguilar, Princeton University; Jan Homann, Princeton University; Yi Gu, Princeton University; David Tank, Princeton University; H. Sebastian Seung, Princeton University
   
• Designing smoothing functions for improved worst-case competitive ratio in online optimization
  Reza Eghbali*, University of washington; Maryam Fazel, University of Washington
   
• Convergence guarantees for kernel-based quadrature rules in misspecified settings
  Motonobu Kanagawa*, ; Bharath Sriperumbudur, ; Kenji Fukumizu,
   
• Unsupervised Learning from Noisy Networks with Applications to Hi-C Data
  Bo Wang*, Stanford University; Junjie Zhu, Stanford University; Armin Pourshafeie, Stanford University
   
• A non-generative framework and convex relaxations for unsupervised learning
  Elad Hazan, ; Tengyu Ma*, Princeton University
   
• Equality of Opportunity in Supervised Learning
  Moritz Hardt*, ; Eric Price, ; Nathan Srebro,
   
• Scaled Least Squares Estimator for GLMs in Large-Scale Problems
  Murat Erdogdu*, Stanford University; Lee Dicker, ; Mohsen Bayati,
   
• Interpretable Nonlinear Dynamic Modeling of Neural Trajectories
  Yuan Zhao*, Stony Brook University; Il Memming Park,
   
• Search Improves Label for Active Learning
  Alina Beygelzimer, Yahoo Inc; Daniel Hsu, ; John Langford, ; Chicheng Zhang*, UCSD
   
• Higher-Order Factorization Machines
  Mathieu Blondel*, NTT; Akinori Fujino, NTT; Naonori Ueda, ; Masakazu Ishihata, Hokkaido University
   
• Exponential expressivity in deep neural networks through transient chaos
  Ben Poole*, Stanford University; Subhaneil Lahiri, Stanford University; Maithra Raghu, Cornell University; Jascha Sohl-Dickstein, ; Surya Ganguli, Stanford
   
• Split LBI: An Iterative Regularization Path with Structural Sparsity
  Chendi Huang, Peking University; Xinwei Sun, ; Jiechao Xiong, Peking University; Yuan Yao*,
   
• An equivalence between high dimensional Bayes optimal inference and M-estimation
  Madhu Advani*, Stanford University; Surya Ganguli, Stanford
   
• Synthesizing the preferred inputs for neurons in neural networks via deep generator networks
  Anh Nguyen*, University of Wyoming; Alexey Dosovitskiy, ; Jason Yosinski, Cornell; Thomas Brox, University of Freiburg; Jeff Clune,
   
• Deep Submodular Functions
  Brian Dolhansky*, University of Washington; Jeff Bilmes, University of Washington, Seattle
   
• Discriminative Gaifman Models
  Mathias Niepert*,
   
• Leveraging Sparsity for Efficient Submodular Data Summarization
  Erik Lindgren*, University of Texas at Austin; Shanshan Wu, UT Austin; Alexandros G. Dimakis,
   
• Local Minimax Complexity of Stochastic Convex Optimization
  Sabyasachi Chatterjee, University of Chicago; John Duchi, ; John Lafferty, ; Yuancheng Zhu*, University of Chicago
   
• Stochastic Optimization for Large-scale Optimal Transport
  Aude Genevay*, Université Paris Dauphine; Marco Cuturi, ; Gabriel Peyré, ; Francis Bach,
   
• On Mixtures of Markov Chains
  Rishi Gupta*, Stanford; Ravi Kumar, ; Sergei Vassilvitskii, Google
   
• Linear Contextual Bandits with Knapsacks
  Shipra Agrawal*, ; Nikhil Devanur, Microsoft Research
   
• Reconstructing Parameters of Spreading Models from Partial Observations
  Andrey Lokhov*, Los Alamos National Laboratory
   
• Spatiotemporal Residual Networksfor Video Action Recognition
  Christoph Feichtenhofer*, Graz University of Technology; Axel Pinz, Graz University of Technology; Richard Wildes, York University Toronto
   
• Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations
  Behnam Neyshabur*, TTI-Chicago; Yuhuai Wu, University of Toronto; Ruslan Salakhutdinov, University of Toronto; Nathan Srebro,
   
• Strategic Attentive Writer for Learning Macro-Actions
  Alexander Vezhnevets*, Google DeepMind; Volodymyr Mnih, ; Simon Osindero, Google DeepMind; Alex Graves, ; Oriol Vinyals, ; John Agapiou, ; Koray Kavukcuoglu, Google DeepMind
   
• The Limits of Learning with Missing Data
  Brian Bullins*, Princeton University; Elad Hazan, ; Tomer Koren, Technion---Israel Inst. of Technology
   
• RETAIN: Interpretable Predictive Model in Healthcare using Reverse Time Attention Mechanism
  Edward Choi*, Georgia Institute of Technolog; Mohammad Taha Bahadori, Gatech; Jimeng Sun,
   
• Total Variation Classes Beyond 1d: Minimax Rates, and the Limitations of Linear Smoothers
  Yu-Xiang Wang*, Carnegie Mellon University; Veeranjaneyulu Sadhanala, Carnegie Mellon University; Ryan Tibshirani,
   
• Community Detection on Evolving Graphs
  Stefano Leonardi*, Sapienza University of Rome; Aris Anagnostopoulos, Sapienza University of Rome; Jakub Łącki, Sapienza University of Rome; Silvio Lattanzi, Google; Mohammad Mahdian, Google Research, New York
   
• Online and Differentially-Private Tensor Decomposition
  Yining Wang*, Carnegie Mellon University; Anima Anandkumar, UC Irvine
   
• Dimension-Free Iteration Complexity of Finite Sum Optimization Problems
  Yossi Arjevani*, Weizmann Institute of Science; Ohad Shamir, Weizmann Institute of Science
   
• Towards Conceptual Compression
  Karol Gregor*, ; Frederic Besse, Google DeepMind; Danilo Jimenez Rezende, ; Ivo Danihelka, ; Daan Wierstra, Google DeepMind
   
• Exact Recovery of Hard Thresholding Pursuit
  Xiaotong Yuan*, Nanjing University of Informat; Ping Li, ; Tong Zhang,
   
• Data Programming: Creating Large Training Sets, Quickly
  Alexander Ratner*, Stanford University; Christopher De Sa, Stanford University; Sen Wu, Stanford University; Daniel Selsam, Stanford; Christopher Ré, Stanford University
   
• Generalization of ERM in Stochastic Convex Optimization: The Dimension Strikes Back
  Vitaly Feldman*,
   
• Dynamic matrix recovery from incomplete observations under an exact low-rank constraint
  Liangbei Xu*, Gatech; Mark Davenport,
   
• Fast Distributed Submodular Cover: Public-Private Data Summarization
  Baharan Mirzasoleiman*, ETH Zurich; Morteza Zadimoghaddam, ; Amin Karbasi,
   
• Estimating Nonlinear Neural Response Functions using GP Priors and Kronecker Methods
  Cristina Savin*, IST Austria; Gašper Tkačik, Institute of Science and Technology Austria
   
• Lifelong Learning with Weighted Majority Votes
  Anastasia Pentina*, IST Austria; Ruth Urner, MPI Tuebingen
   
• Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes
  Jack Rae*, Google DeepMind; Jonathan Hunt, ; Ivo Danihelka, ; Tim Harley, Google DeepMind; Andrew Senior, ; Greg Wayne, ; Alex Graves, ; Timothy Lillicrap, Google DeepMind
   
• Matching Networks for One Shot Learning
  Oriol Vinyals*, ; Charles Blundell, DeepMind; Timothy Lillicrap, Google DeepMind; Koray Kavukcuoglu, Google DeepMind; Daan Wierstra, Google DeepMind
   
• Tight Complexity Bounds for Optimizing Composite Objectives
  Blake Woodworth*, Toyota Technological Institute; Nathan Srebro,
   
• Graphical Time Warping for Joint Alignment of Multiple Curves
  Yizhi Wang, Virginia Tech; David Miller, The Pennsylvania State University; Kira Poskanzer, University of California, San Francisco; Yue Wang, Virginia Tech; Lin Tian, The University of California, Davis; Guoqiang Yu*,
   
• Unsupervised Risk Estimation Using Only Conditional Independence Structure
  Jacob Steinhardt*, Stanford University; Percy Liang,
   
• MetaGrad: Multiple Learning Rates in Online Learning
  Tim Van Erven*, ; Wouter M. Koolen,
   
• Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation
  Tejas Kulkarni, MIT; Karthik Narasimhan*, MIT; Ardavan Saeedi, MIT; Joshua Tenenbaum,
   
• High Dimensional Structured Superposition Models
  Qilong Gu*, University of Minnesota; Arindam Banerjee,
   
• Joint quantile regression in vector-valued RKHSs
  Maxime Sangnier*, LTCI, CNRS, Télécom ParisTech; Olivier Fercoq, ; Florence d’Alché-Buc,
   
• The Forget-me-not Process
  Kieran Milan, Google DeepMind; Joel Veness*, ; James Kirkpatrick, Google DeepMind; Michael Bowling, ; Anna Koop, University of Alberta; Demis Hassabis,
   
• Wasserstein Training of Restricted Boltzmann Machines
  Gregoire Montavon*, ; Klaus-Robert Muller, ; Marco Cuturi,
   
• Communication-Optimal Distributed Clustering
  Jiecao Chen, Indiana University Bloomington; He Sun*, The University of Bristol; David Woodruff, ; Qin Zhang,
   
• Probing the Compositionality of Intuitive Functions
  Eric Schulz*, University College London; Joshua Tenenbaum, ; David Duvenaud, ; Maarten Speekenbrink, University College London; Sam Gershman,
   
• Ladder Variational Autoencoders
  Casper Kaae Sønderby*, University of Copenhagen; Tapani Raiko, ; Lars Maaløe, Technical University of Denmark; Søren Sønderby, KU; Ole Winther, Technical University of Denmark
   
• The Multiple Quantile Graphical Model
  Alnur Ali*, Carnegie Mellon University; Zico Kolter, ; Ryan Tibshirani,
   
• Threshold Learning for Optimal Decision Making
  Nathan Lepora*, University of Bristol
   
• Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICA
  Aapo Hyvärinen*, ; Hiroshi Morioka, University of Helsinki
   
• Can Active Memory Replace Attention?
  Łukasz Kaiser*, ; Samy Bengio,
   
• Minimax Optimal Alternating Minimization for Kernel Nonparametric Tensor Learning
  Taiji Suzuki*, ; Heishiro Kanagawa, ; Hayato Kobayashi, ; Nobuyuki Shimizu, ; Yukihiro Tagami,
   
• The Product Cut
  Thomas Laurent*, Loyola Marymount University; James Von Brecht, CSULB; Xavier Bresson, ; Arthur Szlam,
   
• Learning Sparse Gaussian Graphical Models with Overlapping Blocks
  Mohammad Javad Hosseini*, University of Washington; Su-In Lee,
   
• Yggdrasil: An Optimized System for Training Deep Decision Trees at Scale
  Firas Abuzaid*, MIT; Joseph Bradley, Databricks; Feynman Liang, Cambridge University Engineering Department; Andrew Feng, Yahoo!; Lee Yang, Yahoo!; Matei Zaharia, MIT; Ameet Talwalkar,
   
• Average-case hardness of RIP certification
  Tengyao Wang, University of Cambridge; Quentin Berthet*, ; Yaniv Plan, University of British Columbia
   
• Forward models at Purkinje synapses facilitate cerebellar anticipatory control
  Ivan Herreros-Alonso*, Universitat Pompeu Fabra; Xerxes Arsiwalla, ; Paul Verschure,
   
• Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
  Michaël Defferrard*, EPFL; Xavier Bresson, ; pierre Vandergheynst, EPFL
   
• Deep Unsupervised Exemplar Learning
  MIGUEL BAUTISTA*, HEIDELBERG UNIVERSITY; Artsiom Sanakoyeu, Heidelberg University; Ekaterina Tikhoncheva, Heidelberg University; Björn Ommer,
   
• Large-Scale Price Optimization via Network Flow
  Shinji Ito*, NEC Coorporation; Ryohei Fujimaki,
   
• Online Pricing with Strategic and Patient Buyers
  Michal Feldman, TAU; Tomer Koren, Technion---Israel Inst. of Technology; Roi Livni*, Huji; Yishay Mansour, Microsoft; Aviv Zohar, huji
   
• Global Optimality of Local Search for Low Rank Matrix Recovery
  Srinadh Bhojanapalli*, TTI Chicago; Behnam Neyshabur, TTI-Chicago; Nathan Srebro,
   
• Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences
  Daniel Neil*, Institute of Neuroinformatics; Michael Pfeiffer, Institute of Neuroinformatics; Shih-Chii Liu,
   
• Improving PAC Exploration Using the Median of Means
  Jason Pazis*, MIT; Ronald Parr, ; Jonathan How, MIT
   
• Infinite Hidden Semi-Markov Modulated Interaction Point Process
  Matt Zhang*, Nicta; Peng Lin, Data61; Ting Guo, Data61; Yang Wang, Data61, CSIRO; Fang Chen, Data61, CSIRO
   
• Cooperative Inverse Reinforcement Learning
  Dylan Hadfield-Menell*, UC Berkeley; Stuart Russell, UC Berkeley; Pieter Abbeel, ; Anca Dragan,
   
• Spatio-Temporal Hilbert Maps for Continuous Occupancy Representation in Dynamic Environments
  Ransalu Senanayake*, The University of Sydney; Lionel Ott, The University of Sydney; Simon O'Callaghan, NICTA; Fabio Ramos, The University of Sydney
   
• Select-and-Sample for Spike-and-Slab Sparse Coding
  Abdul-Saboor Sheikh, University of Oldenburg; Jörg Lücke*,
   
• Tractable Operations for Arithmetic Circuits of Probabilistic Models
  Yujia Shen*, ; Arthur Choi, ; Adnan Darwiche,
   
• Greedy Feature Construction
  Dino Oglic*, University of Bonn; Thomas Gaertner, The University of Nottingham
   
• Mistake Bounds for Binary Matrix Completion
  Mark Herbster, ; Stephen Pasteris, UCL; Massimiliano Pontil*,
   
• Data driven estimation of Laplace-Beltrami operator
  Frederic Chazal, INRIA; Ilaria Giulini, ; Bertrand Michel*,
   
• Tracking the Best Expert in Non-stationary Stochastic Environments
  Chen-Yu Wei*, Academia Sinica; Yi-Te Hong, Academia Sinica; Chi-Jen Lu, Academia Sinica
   
• Learning to learn by gradient descent by gradient descent
  Marcin Andrychowicz*, Google Deepmind; Misha Denil, ; Sergio Gomez, Google DeepMind; Matthew Hoffman, Google DeepMind; David Pfau, Google DeepMind; Tom Schaul, ; Nando Freitas, Google
   
• Kernel Observers: Systems-Theoretic Modeling and Inference of Spatiotemporally Evolving Processes
   Hassan Kingravi, Pindrop Security, Harshal Maske, UIUC, Girish Chowdhary*, UIUC  
   
• Quantum Perceptron Models
  Ashish Kapoor*, ; Nathan Wiebe, Microsoft Research; Krysta M. Svore,
   
• Guided Policy Search as Approximate Mirror Descent
  William Montgomery*, University of Washington; Sergey Levine, University of Washington
   
• The Power of Optimization from Samples
  Eric Balkanski*, Harvard University; Aviad Rubinstein, UC Berkeley; Yaron Singer,
   
• Deep Exploration via Bootstrapped DQN
  Ian Osband*, DeepMind; Charles Blundell, DeepMind; Alexander Pritzel, ; Benjamin Van Roy,
   
• A Multi-step Inertial Forward-Backward Splitting Method for Non-convex Optimization
  Jingwei Liang*, GREYC, ENSICAEN; Jalal Fadili, ; Gabriel Peyré,
   
• Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages
  Yin Cheng Ng*, University College London; Pawel Chilinski, University College London; Ricardo Silva, University College London
   
• Convolutional Neural Fabrics
  Shreyas Saxena*, INRIA; Jakob Verbeek,
   
• A Neural Transducer
  Navdeep Jaitly*, ; Quoc Le, ; Oriol Vinyals, ; Ilya Sutskever, ; David Sussillo, Google; Samy Bengio,
   
• Adaptive Newton Method for Empirical Risk Minimization to Statistical Accuracy
  Aryan Mokhtari*, University of Pennsylvania; Hadi Daneshmand, ETH Zurich; Aurelien Lucchi, ; Thomas Hofmann, ; Alejandro Ribeiro, University of Pennsylvania
   
• A Sparse Interactive Model for Inductive Matrix Completion
  Jin Lu, University of Connecticut; Guannan Liang, University of Connecticut; jiangwen Sun, University of Connecticut; Jinbo Bi*, University of Connecticut
   
• Coresets for Scalable Bayesian Logistic Regression
  Jonathan Huggins*, MIT; Trevor Campbell, MIT; Tamara Broderick, MIT
   
• Agnostic Estimation for Misspecified Phase Retrieval Models
  Matey Neykov*, Princeton University; Zhaoran Wang, Princeton University; Han Liu,
   
• Linear Relaxations for Finding Diverse Elements in Metric Spaces
  Aditya Bhaskara*, University of Utah; Mehrdad Ghadiri, Sharif University of Technolog; Vahab Mirrokni, Google; Ola Svensson, EPFL
   
• Binarized Neural Networks
  Itay Hubara*, Technion; Matthieu Courbariaux, Université de Montréal; Daniel Soudry, Columbia University; Ran El-Yaniv, Technion; Yoshua Bengio, Université de Montréal
   
• On Local Maxima in the Population Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences
  Chi Jin*, UC Berkeley; Yuchen Zhang, ; Sivaraman Balakrishnan, CMU; Martin Wainwright, UC Berkeley; Michael Jordan,
   
• Memory-Efficient Backpropagation Through Time
  Audrunas Gruslys*, Google DeepMind; Remi Munos, Google DeepMind; Ivo Danihelka, ; Marc Lanctot, Google DeepMind; Alex Graves,
   
• Bayesian Optimization with Robust Bayesian Neural Networks
  Jost Tobias Springenberg*, University of Freiburg; Aaron Klein, University of Freiburg; Stefan Falkner, University of Freiburg; Frank Hutter, University of Freiburg
   
• Learnable Visual Markers
  Oleg Grinchuk, Skolkovo Institute of Science and Technology; Vadim Lebedev, Skolkovo Institute of Science and Technology; Victor Lempitsky*,
   
• Fast Algorithms for Robust PCA via Gradient Descent
  Xinyang Yi*, UT Austin; Dohyung Park, University of Texas at Austin; Yudong Chen, ; Constantine Caramanis,
   
• One-vs-Each Approximation to Softmax for Scalable Estimation of Probabilities
  Michalis K. Titsias*,
   
• Learning Deep Embeddings with Histogram Loss
  Evgeniya Ustinova, Skoltech; Victor Lempitsky*,
   
• Spectral Learning of Dynamic Systems from Nonequilibrium Data
  Hao Wu*, Free University of Berlin; Frank Noe,
   
• Markov Chain Sampling in Discrete Probabilistic Models with Constraints
  Chengtao Li*, MIT; Suvrit Sra, MIT; Stefanie Jegelka, MIT
   
• Mapping Estimation for Discrete Optimal Transport
  Michael Perrot*, University of Saint-Etienne, laboratoire Hubert Curien; Nicolas Courty, ; Rémi Flamary, ; Amaury Habrard, University of Saint-Etienne, Laboratoire Hubert Curien
   
• BBO-DPPs: Batched Bayesian Optimization via Determinantal Point Processes
  Tarun Kathuria*, Microsoft Research; Amit Deshpande, ; Pushmeet Kohli,
   
• Protein contact prediction from amino acid co-evolution using convolutional networks for graph-valued images
  Vladimir Golkov*, Technical University of Munich; Marcin Skwark, Vanderbilt University; Antonij Golkov, University of Augsburg; Alexey Dosovitskiy, ; Thomas Brox, University of Freiburg; Jens Meiler, Vanderbilt University; Daniel Cremers, Technical University of Munich
   
• Linear Feature Encoding for Reinforcement Learning
  Zhao Song*, Duke University; Ronald Parr, ; Xuejun Liao, Duke University; Lawrence Carin,
   
• A Minimax Approach to Supervised Learning
  Farzan Farnia*, Stanford University; David Tse, Stanford University
   
• Edge-Exchangeable Graphs and Sparsity
  Diana Cai*, University of Chicago; Trevor Campbell, MIT; Tamara Broderick, MIT
   
• A Locally Adaptive Normal Distribution
  Georgios Arvanitidis*, DTU; Lars Kai Hansen, ; Søren Hauberg,
   
• Completely random measures for modelling block-structured sparse networks
  Tue Herlau*, ; Mikkel Schmidt, DTU; Morten Mørup, Technical University of Denmark
   
• Sparse Support Recovery with Non-smooth Loss Functions
  Kévin Degraux*, Université catholique de Louva; Gabriel Peyré, ; Jalal Fadili, ; Laurent Jacques, Université catholique de Louvain
   
• Neurons Equipped with Intrinsic Plasticity Learn Stimulus Intensity Statistics
  Travis Monk*, University of Oldenburg; Cristina Savin, IST Austria; Jörg Lücke,
   
• Learning values across many orders of magnitude
  Hado Van Hasselt*, ; Arthur Guez, ; Matteo Hessel, Google DeepMind; Volodymyr Mnih, ; David Silver,
   
• Adaptive Smoothed Online Multi-Task Learning
  Keerthiram Murugesan*, Carnegie Mellon University; Hanxiao Liu, Carnegie Mellon University; Jaime Carbonell, CMU; Yiming Yang, CMU
   
• Safe Exploration in Finite Markov Decision Processes with Gaussian Processes
  Matteo Turchetta, ETH Zurich; Felix Berkenkamp*, ETH Zurich; Andreas Krause,
   
• Probabilistic Linear Multistep Methods
  Onur Teymur*, Imperial College London; Kostas Zygalakis, ; Ben Calderhead,
   
• Stochastic Three-Composite Convex Minimization
  Alp Yurtsever*, EPFL; Bang Vu, ; Volkan Cevher,
   
• Using Fast Weights to Attend to the Recent Past
  Jimmy Ba*, University of Toronto; Geoffrey Hinton, Google; Volodymyr Mnih, ; Joel Leibo, Google DeepMind; Catalin Ionescu, Google
   
• Maximal Sparsity with Deep Networks?
  Bo Xin*, Peking University; Yizhou Wang, Peking University; Wen Gao, peking university; David Wipf,
   
• Quantifying and Reducing Stereotypes in Word Embeddings
  Tolga Bolukbasi*, Boston University; Kai-Wei Chang, ; James Zou, ; Venkatesh Saligrama, ; Adam Kalai, Microsoft Research
   
• beta-risk: a New Surrogate Risk for Learning from Weakly Labeled Data
  Valentina Zantedeschi*, UJM Saint-Etienne, France; Rémi Emonet, ; Marc Sebban,
   
• Learning Additive Exponential Family Graphical Models via $\ell_{2,1}$-norm Regularized M-Estimation
  Xiaotong Yuan*, Nanjing University of Informat; Ping Li, ; Tong Zhang, ; Qingshan Liu, ; Guangcan Liu, NUIST
   
• Backprop KF: Learning Discriminative Deterministic State Estimators
  Tuomas Haarnoja*, UC Berkeley; Anurag Ajay, UC Berkeley; Sergey Levine, University of Washington; Pieter Abbeel,
   
• 2-Component Recurrent Neural Networks
  Xiang Li*, NJUST; Tao Qin, Microsoft; Jian Yang, ; Xiaolin Hu, ; Tie-Yan Liu, Microsoft Research
   
• Fast recovery from a union of subspaces
  Chinmay Hegde, ; Piotr Indyk, MIT; Ludwig Schmidt*, MIT
   
• Incremental Learning for Variational Sparse Gaussian Process Regression
  Ching-An Cheng*, Georgia Institute of Technolog; Byron Boots,
   
• A Consistent Regularization Approach for Structured Prediction
  Carlo Ciliberto*, MIT; Lorenzo Rosasco, ; Alessandro Rudi,
   
• Clustering Signed Networks with the Geometric Mean of Laplacians
  Pedro Eduardo Mercado Lopez*, Saarland University; Francesco Tudisco, Saarland University; Matthias Hein, Saarland University
   
• An urn model for majority voting in classification ensembles
  Víctor Soto, Columbia University; Alberto Suarez, ; Gonzalo Martínez-Muñoz*,
   
• Avoiding Imposters and Delinquents: Adversarial Crowdsourcing and Peer Prediction
  Jacob Steinhardt*, Stanford University; Gregory Valiant, ; Moses Charikar, Stanford University
   
• Fast and accurate spike sorting of high-channel count probes with KiloSort
  Marius Pachitariu*, ; Nick Steinmetz, UCL; Shabnam Kadir, ; Matteo Carandini, UCL; Kenneth Harris, UCL
   
• Combining Adversarial Guarantees and Stochastic Fast Rates in Online Learning
  Wouter M. Koolen*, ; Peter Grunwald, CWI; Tim Van Erven,
   
• Ancestral Causal Inference
  Sara Magliacane*, VU University Amsterdam; Tom Claassen, ; Joris Mooij, Radboud University Nijmegen
   
• More Supervision, Less Computation: Statistical-Computational Tradeoffs in Weakly Supervised Learning
  Xinyang Yi, UT Austin; Zhaoran Wang, Princeton University; Zhuoran Yang , Princeton University; Constantine Caramanis, ; Han Liu*,
   
• Tagger: Deep Unsupervised Perceptual Grouping
  Klaus Greff*, IDSIA; Antti Rasmus, The Curious AI Company; Mathias Berglund, The Curious AI Company; Tele Hao, The Curious AI Company; Harri Valpola, The Curious AI Company
   
• Efficient Algorithm for Streaming Submodular Cover
  Ashkan Norouzi-Fard*, EPFL; Abbas Bazzi, EPFL; Ilija Bogunovic, EPFL Lausanne; Marwa El Halabi, l; Ya-Ping Hsieh, ; Volkan Cevher,
   
• Interaction Networks for Learning about Objects, Relations and Physics
  Peter Battaglia*, Google DeepMind; Razvan Pascanu, ; Matthew Lai, Google DeepMind; Danilo Jimenez Rezende, ; Koray Kavukcuoglu, Google DeepMind
   
• Efficient state-space modularization for planning: theory, behavioral and neural signatures
  Daniel McNamee*, University of Cambridge; Daniel Wolpert, University of Cambridge; Máté Lengyel, University of Cambridge
   
• Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent
  Chi Jin*, UC Berkeley; Sham Kakade, ; Praneeth Netrapalli, Microsoft Research
   
• Online Bayesian Moment Matching for Topic Modeling with Unknown Number of Topics
  Wei-Shou Hsu*, University of Waterloo; Pascal Poupart,
   
• Computing and maximizing influence in linear threshold and triggering models
  Justin Khim*, University of Pennsylvania; Varun Jog, ; Po-Ling Loh, Berkeley
   
• Coevolutionary Latent Feature Processes for Continuous-Time User-Item Interactions
  Yichen Wang*, Georgia Tech; Nan Du, ; Rakshit Trivedi, Georgia Institute of Technolo; Le Song,
   
• Learning Deep Parsimonious Representations
  Renjie Liao*, UofT; Alexander Schwing, ; Rich Zemel, ; Raquel Urtasun,
   
• Optimal Learning for Multi-pass Stochastic Gradient Methods
  Junhong Lin*, Istituto Italiano di Tecnologia; Lorenzo Rosasco,
   
• Generative Adversarial Imitation Learning
  Jonathan Ho*, Stanford; Stefano Ermon,
   
• An End-to-End Approach for Natural Language to IFTTT Program Translation
  Chang Liu*, University of Maryland; Xinyun Chen, Shanghai Jiaotong University; Richard Shin, ; Mingcheng Chen, University of Illinois, Urbana-Champaign; Dawn Song, UC Berkeley
   
• Dual Space Gradient Descent for Online Learning
  Trung Le*, University of Pedagogy Ho Chi Minh city; Tu Nguyen, Deakin University; Vu Nguyen, Deakin University; Dinh Phung, Deakin University
   
• Fast stochastic optimization on Riemannian manifolds
  Hongyi Zhang*, MIT; Sashank Jakkam Reddi, Carnegie Mellon University; Suvrit Sra, MIT
   
• Professor Forcing: A New Algorithm for Training Recurrent Networks
  Alex Lamb, Montreal; Anirudh Goyal*, University of Montreal; ying Zhang, University of Montreal; Saizheng Zhang, University of Montreal; Aaron Courville, University of Montreal; Yoshua Bengio, U. Montreal
   
• Learning brain regions via large-scale online structured sparse dictionary learning
  Elvis DOHMATOB*, Inria; Arthur Mensch, inria; Gaël Varoquaux, ; Bertrand Thirion,
   
• Efficient Neural Codes under Metabolic Constraints
  Zhuo Wang*, University of Pennsylvania; Xue-Xin Wei, University of Pennsylvania; Alan Stocker, ; Dan Lee , University of Pennsylvania
   
• Approximate maximum entropy principles via Goemans-Williamson with applications to provable variational methods
  Andrej Risteski*, Princeton University; Yuanzhi Li, Princeton University
   
• Efficient High-Order Interaction-Aware Feature Selection Based on Conditional Mutual Information
  Alexander Shishkin, Yandex; Anastasia Bezzubtseva, Yandex; Alexey Drutsa*, Yandex; Ilia Shishkov, Yandex; Ekaterina Gladkikh, Yandex; Gleb Gusev, Yandex LLC; Pavel Serdyukov, Yandex
   
• Bayesian Intermittent Demand Forecasting for Large Inventories
  Matthias Seeger*, Amazon; David Salinas, Amazon; Valentin Flunkert, Amazon
   
• Visual Question Answering with Question Representation Update
  RUIYU LI*, CUHK; Jiaya Jia, CUHK
   
• Learning Parametric Sparse Models for Image Super-Resolution
  Yongbo Li, Xidian University; Weisheng Dong*, Xidian University; GUANGMING Shi, Xidian University; Xuemei Xie, Xidian University; Xin Li, WVU
   
• Blazing the trails before beating the path: Sample-efficient Monte-Carlo planning
  Jean-Bastien Grill, Inria Lille - Nord Europe; Michal Valko*, Inria Lille - Nord Europe; Remi Munos, Google DeepMind
   
• Asynchronous Parallel Greedy Coordinate Descent
  Yang You, UC Berkeley; Xiangru Lian, University of Rochester; Cho-Jui Hsieh*, ; Ji Liu, ; Hsiang-Fu Yu, University of Texas at Austin; Inderjit Dhillon, ; James Demmel, UC Berkeley
   
• Iterative Refinement of the Approximate Posterior for Directed Belief Networks
  Rex Devon Hjelm*, University of New Mexico; Ruslan Salakhutdinov, University of Toronto; Kyunghyun Cho, University of Montreal; Nebojsa Jojic, Microsoft Research; Vince Calhoun, Mind Research Network; Junyoung Chung, University of Montreal
   
• Assortment Optimization Under the Mallows model
  Antoine Desir*, Columbia University; Vineet Goyal, ; Srikanth Jagabathula, ; Danny Segev,
   
• Disease Trajectory Maps
  Peter Schulam*, Johns Hopkins University; Raman Arora,
   
• Multistage Campaigning in Social Networks
  Mehrdad Farajtabar*, Georgia Tech; Xiaojing Ye, Georgia State University; Sahar Harati, Emory University; Le Song, ; Hongyuan Zha, Georgia Institute of Technology
   
• Learning in Games: Robustness of Fast Convergence
  Dylan Foster, Cornell University;  Zhiyuan Li, Tsinghua University; Thodoris Lykouris*, Cornell University; Karthik Sridharan, Cornell University; Eva Tardos, Cornell University
   
• Improving Variational Autoencoders with Inverse Autoregressive Flow
  Diederik Kingma*, ; Tim Salimans,
   
• Algorithms and matching lower bounds for approximately-convex optimization
  Andrej Risteski*, Princeton University; Yuanzhi Li, Princeton University
   
• Unified Methods for Exploiting Piecewise Structure in Convex Optimization
  Tyler Johnson*, University of Washington; Carlos Guestrin,
   
• Kernel Bayesian Inference with Posterior Regularization
  Yang Song*, Stanford University; Jun Zhu, ; Yong Ren, Tsinghua University
   
• Neural universal discrete denoiser
  Taesup Moon*, DGIST; Seonwoo Min, Seoul National University; Byunghan Lee, Seoul National University; Sungroh Yoon, Seoul National University
• Optimal Architectures in a Solvable Model of Deep Networks
  Jonathan Kadmon*, Hebrew University; Haim Sompolinsky ,
   
• Conditional Image Generation with Pixel CNN Decoders
  Aaron Van den Oord*, Google Deepmind; Nal Kalchbrenner, ; Lasse Espeholt, ; Koray Kavukcuoglu, Google DeepMind; Oriol Vinyals, ; Alex Graves,
   
• Supervised Learning with Tensor Networks
  Edwin Stoudenmire*, Univ of California Irvine; David Schwab, Northwestern University
   
• Multi-step learning and underlying structure in statistical models
  Maia Fraser*, University of Ottawa
   
• Blind Optimal Recovery of Signals
  Dmitry Ostrovsky*, Univ. Grenoble Alpes; Zaid Harchaoui, NYU, Courant Institute; Anatoli Juditsky, ; Arkadi Nemirovski, Gerogia Institute of Technology
   
• An Architecture for Deep, Hierarchical Generative Models
  Philip Bachman*,
   
• Feature selection for classification of functional data using recursive maxima hunting
  José Torrecilla*, Universidad Autónoma de Madrid; Alberto Suarez,
   
• Achieving budget-optimality with adaptive schemes in crowdsourcing
  Ashish Khetan, University of Illinois Urbana-; Sewoong Oh*,
   
• Near-Optimal Smoothing of Structured Conditional Probability Matrices
  Moein Falahatgar, UCSD; Mesrob I. Ohannessian*, ; Alon Orlitsky,
   
• Supervised Word Mover's Distance
  Gao Huang, ; Chuan Guo*, Cornell University; Matt Kusner, ; Yu Sun, ; Fei Sha, University of Southern California; Kilian Weinberger,
   
• Exploiting Tradeoffs for Exact Recovery in Heterogeneous Stochastic Block Models
  Amin Jalali*, University of Washington; Qiyang Han, University of Washington; Ioana Dumitriu, University of Washington; Maryam Fazel, University of Washington
   
• Full-Capacity Unitary Recurrent Neural Networks
  Scott Wisdom*, University of Washington; Thomas Powers, ; John Hershey, ; Jonathan LeRoux, ; Les Atlas,
   
• Threshold Bandits, With and Without Censored Feedback
  Jacob Abernethy, ; Kareem Amin, ; Ruihao Zhu*, Massachusetts Institute of Technology
   
• Understanding the Effective Receptive Field in Deep Convolutional Neural Networks
  Wenjie Luo*, University of Toronto; Yujia Li, University of Toronto; Raquel Urtasun, ; Rich Zemel,
   
• Learning Supervised PageRank with Gradient-Based and Gradient-Free Optimization Methods
  Lev Bogolubsky, ; Pavel Dvurechensky*, Weierstrass Institute for Appl; Alexander Gasnikov, ; Gleb Gusev, Yandex LLC; Yurii Nesterov, ; Andrey Raigorodskii, ; Aleksey Tikhonov, ; Maksim Zhukovskii,
   
• k^*-Nearest Neighbors: From Global to Local
  Oren Anava, Technion; Kfir Levy*, Technion
   
• Normalized Spectral Map Synchronization
  Yanyao Shen*, UT Austin; Qixing Huang, Toyota Technological Institute at Chicago; Nathan Srebro, ; Sujay Sanghavi,
   
• Beyond Exchangeability: The Chinese Voting Process
  Moontae Lee*, Cornell University; Seok Hyun Jin, Cornell University; David Mimno, Cornell University
   
• A posteriori error bounds for joint matrix decomposition problems
  Nicolo Colombo, Univ of Luxembourg; Nikos Vlassis*, Adobe Research
   
• A Bayesian method for reducing bias in neural representational similarity analysis
  Ming Bo Cai*, Princeton University; Nicolas Schuck, Princeton Neuroscience Institute, Princeton University; Jonathan Pillow, ; Yael Niv,
   
• Online ICA: Understanding Global Dynamics of Nonconvex Optimization via Diffusion Processes
  Chris Junchi Li, Princeton University; Zhaoran Wang*, Princeton University; Han Liu,
   
• Following the Leader and Fast Rates in Linear Prediction: Curved Constraint Sets and Other Regularities
  Ruitong Huang*, University of Alberta; Tor Lattimore, ; András György, ; Csaba Szepesvari, U. Alberta
   
• SDP Relaxation with Randomized Rounding for Energy Disaggregation
  Kiarash Shaloudegi, ; András György*, ; Csaba Szepesvari, U. Alberta; Wilsun Xu, University of Alberta
   
• Recovery Guarantee of Non-negative Matrix Factorization via Alternating Updates
  Yuanzhi Li, Princeton University; Yingyu Liang*, ; Andrej Risteski, Princeton University
   
• Unsupervised Learning of 3D Structure from Images
  Danilo Jimenez Rezende*, ; S. M. Ali Eslami, Google DeepMind; Shakir Mohamed, Google DeepMind; Peter Battaglia, Google DeepMind; Max Jaderberg, ; Nicolas Heess,
   
• Poisson-Gamma dynamical systems
  Aaron Schein*, UMass Amherst; Hanna Wallach, Microsoft Research; Mingyuan Zhou,
   
• Gaussian Processes for Survival Analysis
  Tamara Fernandez, Oxford; Nicolas Rivera*, King's College London; Yee-Whye Teh,
   
• Dual Decomposed Learning with Factorwise Oracle for Structural SVM of Large Output Domain
  Ian En-Hsu Yen*, University of Texas at Austin; huang Xiangru, University of Texas at Austin; Kai Zhong, University of Texas at Austin; Zhang Ruohan, University of Texas at Austin; Pradeep Ravikumar, ; Inderjit Dhillon,
   
• Optimal Binary Classifier Aggregation for General Losses
  Akshay Balsubramani*, UC San Diego; Yoav Freund,
   
• Disentangling factors of variation in deep representation using adversarial training
  Michael Mathieu, NYU; Junbo Zhao, NYU; Aditya Ramesh, NYU; Pablo Sprechmann*, ; Yann LeCun, NYU
   
• A primal-dual method for constrained consensus optimization
  Necdet Aybat*, Penn State University; Erfan Yazdandoost Hamedani, Penn State University
   
• Fundamental Limits of Budget-Fidelity Trade-off in Label Crowdsourcing
  Farshad Lahouti *, Caltech ; Babak Hassibi, Caltech