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
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
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
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
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; Eneze 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
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 New England; 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,
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
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
Stacked Approximated Regression Machine: A Simple Deep Learning Approach Zhangyang Wang*, UIUC; Shiyu Chang, UIUC; Qing Ling, USTC; Shuai Huang, UW; Xia Hu, ; Honghui Shi, UIUC; Thomas Huang, UIUC
Unsupervised Learning of Spoken Language with Visual Context David Harwath*, MIT CSAIL; Antonio Torralba, MIT CSAIL; James Glass, MIT CSAIL
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
Motor Intention Decoding with Population Profile as Observation Model for Kalman Filter Zhewei Jiang*, Columbia University; Chisung Bae, Samsung Electronics; Joonseong Kang, Samsung Electronics; Sang Joon Kim, Samsung Electronics; Mingoo Seok, Columbia 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
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*,
Selective inference for group-sparse linear models Fan Yang, University of Chicago; Rina Foygel Barber*, ; Prateek Jain, Microsoft Research; John Lafferty,
Graph Clustering: Block-models and model free results Yali Wan*, University of Washington; Marina Meila, University of Washington
Maximizing Influence in an Ising Network: A Mean-Field Optimal Solution Christopher Lynn*, University of Pennsylvania; Dan Lee , University of Pennsylvania
Hypothesis Testing in Unsupervised Domain Adaptation with Applications in Neuroscience Hao Zhou, University of Wisconsin Madiso; Vamsi Ithapu*, University of Wisconsin Madison; Sathya Ravi, University of Wisconsin Madiso; Vikas Singh, UW Madison; Grace Wahba, University of Wisconsin Madison; Sterling Johnson, University of Wisconsin Madison
Geometric Dirichlet Means Algorithm for Topic Inference Mikhail Yurochkin*, University of Michigan; Long Nguyen,
Structured Prediction Theory Based on Factor Graph Complexity Corinna Cortes, ; Vitaly Kuznetsov*, Courant Institute; Mehryar Mohri, ; Scott Yang, New York University
Improved Dropout for Shallow and Deep Learning Zhe Li, The University of Iowa; Boqing Gong, University of Central Florida; Tianbao Yang*, University of Iowa
Constraints Based Convex Belief Propagation Yaniv Tenzer*, The Hebrew University; Alexander Schwing, ; Kevin Gimpel, ; Tamir Hazan,
Error Analysis of Generalized Nyström Kernel Regression Hong Chen, University of Texas; Haifeng Xia, Huazhong Agricultural University; Heng Huang*, University of Texas Arlington
A Probabilistic Framework for Deep Learning Ankit Patel, Baylor College of Medicine; Rice University; Tan Nguyen*, Rice University; Richard Baraniuk,
General Tensor Spectral Co-clustering for Higher-Order Data Tao Wu*, Purdue University; Austin Benson, Stanford University; David Gleich,
Cyclades: Conflict-free Asynchronous Machine Learning Xinghao Pan*, UC Berkeley; Stephen Tu, UC Berkeley; Maximilian Lam, UC Berkeley; Dimitris Papailiopoulos, ; Ce Zhang, Stanford; Michael Jordan, ; Kannan Ramchandran, ; Christopher Re, ; Ben Recht,
Single Pass PCA of Matrix Products Shanshan Wu*, UT Austin; Srinadh Bhojanapalli, TTI Chicago; Sujay Sanghavi, ; Alexandros G. Dimakis,
Stochastic Variational Deep Kernel Learning Andrew Wilson*, Carnegie Mellon University; Zhiting Hu, Carnegie Mellon University; Ruslan Salakhutdinov, University of Toronto; Eric Xing, Carnegie Mellon University
Interaction Screening: Efficient and Sample-Optimal Learning of Ising Models Marc Vuffray*, Los Alamos National Laboratory; Sidhant Misra, Los Alamos National Laboratory; Andrey Lokhov, Los Alamos National Laboratory; Misha Chertkov, Los Alamos National Laboratory
Long-term Causal Effects via Behavioral Game Theory Panos Toulis*, University of Chicago; David Parkes, Harvard University
Measuring Neural Net Robustness with Constraints Osbert Bastani*, Stanford University; Yani Ioannou, University of Cambridge; Leonidas Lampropoulos, University of Pennsylvania; Dimitrios Vytiniotis, Microsoft Research; Aditya Nori, Microsoft Research; Antonio Criminisi,
Reshaped Wirtinger Flow for Solving Quadratic Systems of Equations Huishuai Zhang*, Syracuse University; Yingbin Liang, Syracuse University
Nearly Isometric Embedding by Relaxation James McQueen*, University of Washington; Marina Meila, University of Washington; Dominique Joncas, Google
Probabilistic Inference with Generating Functions for Poisson Latent Variable Models Kevin Winner*, UMass CICS; Daniel Sheldon,
Causal meets Submodular: Subset Selection with Directed Information Yuxun Zhou*, UC Berkeley; Costas Spanos,
Depth from a Single Image by Harmonizing Overcomplete Local Network Predictions Ayan Chakrabarti*, ; Jingyu Shao, UCLA; Greg Shakhnarovich,
Deep Neural Networks with Inexact Matching for Person Re-Identification Arulkumar Subramaniam, IIT Madras; Moitreya Chatterjee*, IIT Madras; Anurag Mittal, IIT Madras
Global Analysis of Expectation Maximization for Mixtures of Two Gaussians Ji Xu, Columbia university; Daniel Hsu*, ; Arian Maleki, Columbia University
Estimating the class prior and posterior from noisy positives and unlabeled data Shanatnu Jain*, Indiana University; Martha White, ; Predrag Radivojac,
Kronecker Determinantal Point Processes Zelda Mariet*, MIT; Suvrit Sra, MIT
Finite Sample Prediction and Recovery Bounds for Ordinal Embedding Lalit Jain*, University of Wisconsin-Madison; Kevin Jamieson, UC Berkeley; Robert Nowak, University of Wisconsin Madison
Feature-distributed sparse regression: a screen-and-clean approach Jiyan Yang*, Stanford University; Michael Mahoney, ; Michael Saunders, Stanford University; Yuekai Sun, University of Michigan
Learning Bound for Parameter Transfer Learning Wataru Kumagai*, Kanagawa University
Learning under uncertainty: a comparison between R-W and Bayesian approach He Huang*, LIBR; Martin Paulus, LIBR
Bi-Objective Online Matching and Submodular Allocations Hossein Esfandiari*, University of Maryland; Nitish Korula, Google Research; Vahab Mirrokni, Google
Quantized Random Projections and Non-Linear Estimation of Cosine Similarity Ping Li, ; Michael Mitzenmacher, Harvard University; Martin Slawski*,
The non-convex Burer-Monteiro approach works on smooth semidefinite programs Nicolas Boumal, ; Vlad Voroninski*, MIT; Afonso Bandeira,
Dimensionality Reduction of Massive Sparse Datasets Using Coresets Dan Feldman, ; Mikhail Volkov*, MIT; Daniela Rus, MIT
Using Social Dynamics to Make Individual Predictions: Variational Inference with Stochastic Kinetic Model Zhen Xu*, SUNY at Buffalo; Wen Dong, ; Sargur Srihari,
Supervised learning through the lens of compression Ofir David*, Technion - Israel institute of technology; Shay Moran, Technion - Israel institue of Technology; Amir Yehudayoff, Technion - Israel institue of Technology
Generative Shape Models: Joint Text Recognition and Segmentation with Very Little Training Data Xinghua Lou*, Vicarious FPC Inc; Ken Kansky, ; Wolfgang Lehrach, ; CC Laan, ; Bhaskara Marthi, ; D. Scott Phoenix, ; Dileep George,
Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections Xiao-Jiao Mao, Nanjing University; Chunhua Shen*, ; Yu-Bin Yang,
Object based Scene Representations using Fisher Scores of Local Subspace Projections Mandar Dixit*, UC San Diego; Nuno Vasconcelos,
Active Learning with Oracle Epiphany Tzu-Kuo Huang, Microsoft Research; Lihong Li, Microsoft Research; Ara Vartanian, University of Wisconsin-Madison; Saleema Amershi, Microsoft; Xiaojin Zhu*,
Statistical Inference for Pairwise Graphical Models Using Score Matching Ming Yu*, The University of Chicago; Mladen Kolar, ; Varun Gupta, University of Chicago
Improved Error Bounds for Tree Representations of Metric Spaces Samir Chowdhury*, The Ohio State University; Facundo Memoli, ; Zane Smith,
Can Peripheral Representations Improve Clutter Metrics on Complex Scenes? Arturo Deza*, UCSB; Miguel Eckstein, UCSB
On Multiplicative Integration with Recurrent Neural Networks Yuhuai Wu*, University of Toronto; Saizheng Zhang, University of Montreal; ying Zhang, University of Montreal; Yoshua Bengio, U. Montreal; Ruslan Salakhutdinov, University of Toronto
Learning HMMs with Nonparametric Emissions via Spectral Decompositions of Continuous Matrices Kirthevasan Kandasamy*, CMU; Maruan Al-Shedivat, CMU; Eric Xing, Carnegie Mellon University
Regret Bounds for Non-decomposable Metrics with Missing Labels Nagarajan Natarajan*, Microsoft Research Bangalore; Prateek Jain, Microsoft Research
Robust k-means: a Theoretical Revisit ALEXANDROS GEORGOGIANNIS*, TECHNICAL UNIVERSITY OF CRETE
Bayesian optimization for automated model selection Gustavo Malkomes, Washington University; Charles Schaff, Washington University in St. Louis; Roman Garnett*,
A Probabilistic Model of Social Decision Making based on Reward Maximization Koosha Khalvati*, University of Washington; Seongmin Park, Cognitive Neuroscience Center; Jean-Claude Dreher, Centre de Neurosciences Cognitives; Rajesh Rao, University of Washington
Balancing Suspense and Surprise: Timely Decision Making with Endogenous Information Acquisition Ahmed Alaa*, UCLA; Mihaela Van Der Schaar,
Fast and Flexible Monotonic Functions with Ensembles of Lattices Mahdi Fard, ; Kevin Canini, ; Andy Cotter, ; Jan Pfeifer, Google; Maya Gupta*,
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
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 theory for unsupervised learning and efficient improper dictionary 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
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
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,
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,
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
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,
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
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
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 New England; 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
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