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NIPS 2016 Accepted Papers






This accepted papers list has been superseded. Click here for the new list









  • 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
  • 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