NeurIPS 2016
569 papers
A Communication-Efficient Parallel Algorithm for Decision Tree
Qi Meng, Guolin Ke, Taifeng Wang, Wei Chen, Qiwei Ye, Zhi-Ming Ma, Tie-Yan Liu A Credit Assignment Compiler for Joint Prediction
Kai-Wei Chang, He He, Stephane Ross, Hal Daume Iii, John Langford A Locally Adaptive Normal Distribution
Georgios Arvanitidis, Lars K. Hansen, Søren Hauberg A Probabilistic Framework for Deep Learning
Ankit B Patel, Minh Tan Nguyen, Richard Baraniuk A Pseudo-Bayesian Algorithm for Robust PCA
Tae-Hyun Oh, Yasuyuki Matsushita, In Kweon, David Wipf A Scaled Bregman Theorem with Applications
Richard Nock, Aditya Menon, Cheng Soon Ong Active Learning from Imperfect Labelers
Songbai Yan, Kamalika Chaudhuri, Tara Javidi Active Learning with Oracle Epiphany
Tzu-Kuo Huang, Lihong Li, Ara Vartanian, Saleema Amershi, Xiaojin Zhu Adaptive Averaging in Accelerated Descent Dynamics
Walid Krichene, Alexandre Bayen, Peter L Bartlett Adaptive Neural Compilation
Rudy R Bunel, Alban Desmaison, Pawan K Mudigonda, Pushmeet Kohli, Philip Torr Adaptive Newton Method for Empirical Risk Minimization to Statistical Accuracy
Aryan Mokhtari, Hadi Daneshmand, Aurelien Lucchi, Thomas Hofmann, Alejandro Ribeiro Adaptive Optimal Training of Animal Behavior
Ji Hyun Bak, Jung Yoon Choi, Athena Akrami, Ilana Witten, Jonathan W Pillow Adaptive Skills Adaptive Partitions (ASAP)
Daniel J Mankowitz, Timothy A Mann, Shie Mannor Adaptive Smoothed Online Multi-Task Learning
Keerthiram Murugesan, Hanxiao Liu, Jaime Carbonell, Yiming Yang An Efficient Streaming Algorithm for the Submodular Cover Problem
Ashkan Norouzi-Fard, Abbas Bazzi, Ilija Bogunovic, Marwa El Halabi, Ya-Ping Hsieh, Volkan Cevher An Online Sequence-to-Sequence Model Using Partial Conditioning
Navdeep Jaitly, Quoc V Le, Oriol Vinyals, Ilya Sutskever, David Sussillo, Samy Bengio Ancestral Causal Inference
Sara Magliacane, Tom Claassen, Joris M. Mooij Architectural Complexity Measures of Recurrent Neural Networks
Saizheng Zhang, Yuhuai Wu, Tong Che, Zhouhan Lin, Roland Memisevic, Ruslan Salakhutdinov, Yoshua Bengio Assortment Optimization Under the Mallows Model
Antoine Desir, Vineet Goyal, Srikanth Jagabathula, Danny Segev Asynchronous Parallel Greedy Coordinate Descent
Yang You, Xiangru Lian, Ji Liu, Hsiang-Fu Yu, Inderjit S Dhillon, James Demmel, Cho-Jui Hsieh Attend, Infer, Repeat: Fast Scene Understanding with Generative Models
S. M. Ali Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa, David Szepesvari, Koray Kavukcuoglu, Geoffrey E. Hinton Automated Scalable Segmentation of Neurons from Multispectral Images
Uygar Sümbül, Douglas Roossien, Dawen Cai, Fei Chen, Nicholas Barry, John P. Cunningham, Edward Boyden, Liam Paninski Automatic Neuron Detection in Calcium Imaging Data Using Convolutional Networks
Noah Apthorpe, Alexander Riordan, Robert Aguilar, Jan Homann, Yi Gu, David Tank, H. Sebastian Seung Average-Case Hardness of RIP Certification
Tengyao Wang, Quentin Berthet, Yaniv Plan Bayesian Optimization for Probabilistic Programs
Tom Rainforth, Tuan Anh Le, Jan-Willem van de Meent, Michael A Osborne, Frank Wood Bayesian Optimization with Robust Bayesian Neural Networks
Jost Tobias Springenberg, Aaron Klein, Stefan Falkner, Frank Hutter Binarized Neural Networks
Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, Yoshua Bengio Blind Attacks on Machine Learners
Alex Beatson, Zhaoran Wang, Han Liu Boosting with Abstention
Corinna Cortes, Giulia DeSalvo, Mehryar Mohri Brains on Beats
Umut Güçlü, Jordy Thielen, Michael Hanke, Marcel van Gerven Catching Heuristics Are Optimal Control Policies
Boris Belousov, Gerhard Neumann, Constantin A Rothkopf, Jan R Peters CliqueCNN: Deep Unsupervised Exemplar Learning
Miguel A Bautista, Artsiom Sanakoyeu, Ekaterina Tikhoncheva, Bjorn Ommer Clustering with Same-Cluster Queries
Hassan Ashtiani, Shrinu Kushagra, Shai Ben-David Combinatorial Energy Learning for Image Segmentation
Jeremy B Maitin-Shepard, Viren Jain, Michal Januszewski, Peter Li, Pieter Abbeel Communication-Optimal Distributed Clustering
Jiecao Chen, He Sun, David Woodruff, Qin Zhang Community Detection on Evolving Graphs
Aris Anagnostopoulos, Jakub Łącki, Silvio Lattanzi, Stefano Leonardi, Mohammad Mahdian Conditional Image Generation with PixelCNN Decoders
Aaron van den Oord, Nal Kalchbrenner, Lasse Espeholt, Koray Kavukcuoglu, Oriol Vinyals, Alex Graves Consistent Kernel Mean Estimation for Functions of Random Variables
Carl-Johann Simon-Gabriel, Adam Scibior, Ilya O Tolstikhin, Bernhard Schölkopf Constraints Based Convex Belief Propagation
Yaniv Tenzer, Alex Schwing, Kevin Gimpel, Tamir Hazan Convex Two-Layer Modeling with Latent Structure
Vignesh Ganapathiraman, Xinhua Zhang, Yaoliang Yu, Junfeng Wen Convolutional Neural Fabrics
Shreyas Saxena, Jakob Verbeek Cooperative Graphical Models
Josip Djolonga, Stefanie Jegelka, Sebastian Tschiatschek, Andreas Krause Cooperative Inverse Reinforcement Learning
Dylan Hadfield-Menell, Stuart Russell, Pieter Abbeel, Anca Dragan Coordinate-Wise Power Method
Qi Lei, Kai Zhong, Inderjit S Dhillon Coresets for Scalable Bayesian Logistic Regression
Jonathan Huggins, Trevor Campbell, Tamara Broderick Cyclades: Conflict-Free Asynchronous Machine Learning
Xinghao Pan, Maximilian Lam, Stephen Tu, Dimitris Papailiopoulos, Ce Zhang, Michael I Jordan, Kannan Ramchandran, Christopher Ré Data Programming: Creating Large Training Sets, Quickly
Alexander J Ratner, Christopher M De Sa, Sen Wu, Daniel Selsam, Christopher Ré Deep ADMM-Net for Compressive Sensing MRI
Yan Yang, Jian Sun, Huibin Li, Zongben Xu Deep Exploration via Bootstrapped DQN
Ian Osband, Charles Blundell, Alexander Pritzel, Benjamin Van Roy Deep Learning for Predicting Human Strategic Behavior
Jason S Hartford, James R Wright, Kevin Leyton-Brown Deep Learning Games
Dale Schuurmans, Martin A Zinkevich Deep Learning Models of the Retinal Response to Natural Scenes
Lane McIntosh, Niru Maheswaranathan, Aran Nayebi, Surya Ganguli, Stephen Baccus DeepMath - Deep Sequence Models for Premise Selection
Geoffrey Irving, Christian Szegedy, Alexander A Alemi, Niklas Een, Francois Chollet, Josef Urban Differential Privacy Without Sensitivity
Kentaro Minami, HItomi Arai, Issei Sato, Hiroshi Nakagawa DISCO Nets : DISsimilarity COefficients Networks
Diane Bouchacourt, Pawan K Mudigonda, Sebastian Nowozin Disease Trajectory Maps
Peter Schulam, Raman Arora Disentangling Factors of Variation in Deep Representation Using Adversarial Training
Michael F Mathieu, Junbo Jake Zhao, Junbo Zhao, Aditya Ramesh, Pablo Sprechmann, Yann LeCun Distributed Flexible Nonlinear Tensor Factorization
Shandian Zhe, Kai Zhang, Pengyuan Wang, Kuang-chih Lee, Zenglin Xu, Yuan Qi, Zoubin Ghahramani Domain Separation Networks
Konstantinos Bousmalis, George Trigeorgis, Nathan Silberman, Dilip Krishnan, Dumitru Erhan Doubly Convolutional Neural Networks
Shuangfei Zhai, Yu Cheng, Zhongfei Zhang, Weining Lu Dual Decomposed Learning with Factorwise Oracle for Structural SVM of Large Output Domain
Ian En-Hsu Yen, Xiangru Huang, Kai Zhong, Ruohan Zhang, Pradeep K Ravikumar, Inderjit S Dhillon Dual Learning for Machine Translation
Di He, Yingce Xia, Tao Qin, Liwei Wang, Nenghai Yu, Tie-Yan Liu, Wei-Ying Ma Dynamic Filter Networks
Xu Jia, Bert De Brabandere, Tinne Tuytelaars, Luc V. Gool Edge-Exchangeable Graphs and Sparsity
Diana Cai, Trevor Campbell, Tamara Broderick Efficient High-Order Interaction-Aware Feature Selection Based on Conditional Mutual Information
Alexander Shishkin, Anastasia Bezzubtseva, Alexey Drutsa, Ilia Shishkov, Ekaterina Gladkikh, Gleb Gusev, Pavel Serdyukov Efficient Neural Codes Under Metabolic Constraints
Zhuo Wang, Xue-Xin Wei, Alan Stocker, Daniel D Lee Efficient Second Order Online Learning by Sketching
Haipeng Luo, Alekh Agarwal, Nicolò Cesa-Bianchi, John Langford Equality of Opportunity in Supervised Learning
Moritz Hardt, Eric Price, Ecprice, Nati Srebro Exponential Expressivity in Deep Neural Networks Through Transient Chaos
Ben Poole, Subhaneil Lahiri, Maithra Raghu, Jascha Sohl-Dickstein, Surya Ganguli Exponential Family Embeddings
Maja Rudolph, Francisco Ruiz, Stephan Mandt, David Blei Fairness in Learning: Classic and Contextual Bandits
Matthew Joseph, Michael Kearns, Jamie H Morgenstern, Aaron Roth Fast Algorithms for Robust PCA via Gradient Descent
Xinyang Yi, Dohyung Park, Yudong Chen, Constantine Caramanis Fast and Accurate Spike Sorting of High-Channel Count Probes with KiloSort
Marius Pachitariu, Nicholas A Steinmetz, Shabnam N Kadir, Matteo Carandini, Kenneth D Harris Fast and Flexible Monotonic Functions with Ensembles of Lattices
Mahdi Milani Fard, Kevin Canini, Andrew Cotter, Jan Pfeifer, Maya Gupta Fast and Provably Good Seedings for K-Means
Olivier Bachem, Mario Lucic, Hamed Hassani, Andreas Krause Fast Learning Rates with Heavy-Tailed Losses
Vu C Dinh, Lam S Ho, Binh Nguyen, Duy Nguyen Fast Recovery from a Union of Subspaces
Chinmay Hegde, Piotr Indyk, Ludwig Schmidt Flexible Models for Microclustering with Application to Entity Resolution
Brenda Betancourt, Giacomo Zanella, Jeffrey W Miller, Hanna Wallach, Abbas Zaidi, Rebecca C. Steorts FPNN: Field Probing Neural Networks for 3D Data
Yangyan Li, Soeren Pirk, Hao Su, Charles R Qi, Leonidas Guibas Full-Capacity Unitary Recurrent Neural Networks
Scott Wisdom, Thomas Powers, John Hershey, Jonathan Le Roux, Les Atlas GAP Safe Screening Rules for Sparse-Group Lasso
Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon Gaussian Process Bandit Optimisation with Multi-Fidelity Evaluations
Kirthevasan Kandasamy, Gautam Dasarathy, Junier B Oliva, Jeff Schneider, Barnabas Poczos Gaussian Processes for Survival Analysis
Tamara Fernandez, Nicolas Rivera, Yee Whye Teh Generating Videos with Scene Dynamics
Carl Vondrick, Hamed Pirsiavash, Antonio Torralba Generative Shape Models: Joint Text Recognition and Segmentation with Very Little Training Data
Xinghua Lou, Ken Kansky, Wolfgang Lehrach, Cc Laan, Bhaskara Marthi, D. Phoenix, Dileep George Graphical Time Warping for Joint Alignment of Multiple Curves
Yizhi Wang, David J. Miller, Kira Poskanzer, Yue Wang, Lin Tian, Guoqiang Yu Graphons, Mergeons, and so on!
Justin Eldridge, Mikhail Belkin, Yusu Wang Greedy Feature Construction
Dino Oglic, Thomas Gärtner Higher-Order Factorization Machines
Mathieu Blondel, Akinori Fujino, Naonori Ueda, Masakazu Ishihata Hypothesis Testing in Unsupervised Domain Adaptation with Applications in Alzheimer's Disease
Hao Zhou, Vamsi K Ithapu, Sathya Narayanan Ravi, Vikas Singh, Grace Wahba, Sterling C Johnson Improved Regret Bounds for Oracle-Based Adversarial Contextual Bandits
Vasilis Syrgkanis, Haipeng Luo, Akshay Krishnamurthy, Robert E. Schapire Improved Techniques for Training GANs
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, Xi Chen, Xi Chen Improved Variational Inference with Inverse Autoregressive Flow
Diederik P. Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, Max Welling Infinite Hidden Semi-Markov Modulated Interaction Point Process
Matt Zhang, Peng Lin, Peng Lin, Ting Guo, Yang Wang, Yang Wang, Fang Chen Interaction Networks for Learning About Objects, Relations and Physics
Peter Battaglia, Razvan Pascanu, Matthew Lai, Danilo Jimenez Rezende, Koray Kavukcuoglu Interpretable Distribution Features with Maximum Testing Power
Wittawat Jitkrittum, Zoltán Szabó, Kacper P Chwialkowski, Arthur Gretton Iterative Refinement of the Approximate Posterior for Directed Belief Networks
Devon Hjelm, Ruslan Salakhutdinov, Kyunghyun Cho, Nebojsa Jojic, Vince Calhoun, Junyoung Chung Joint M-Best-Diverse Labelings as a Parametric Submodular Minimization
Alexander Kirillov, Alexander Shekhovtsov, Carsten Rother, Bogdan Savchynskyy Joint Quantile Regression in Vector-Valued RKHSs
Maxime Sangnier, Olivier Fercoq, Florence d'Alché-Buc Ladder Variational Autoencoders
Casper Kaae Sønderby, Tapani Raiko, Lars Maaløe, Søren Kaae Sønderby, Ole Winther Large Margin Discriminant Dimensionality Reduction in Prediction Space
Mohammad Saberian, Jose Costa Pereira, Can Xu, Jian Yang, Nuno Nvasconcelos Latent Attention for If-Then Program Synthesis
Chang Liu, Xinyun Chen, Eui Chul Shin, Mingcheng Chen, Dawn Song Launch and Iterate: Reducing Prediction Churn
Mahdi Milani Fard, Quentin Cormier, Kevin Canini, Maya Gupta Learnable Visual Markers
Oleg Grinchuk, Vadim Lebedev, Victor Lempitsky Learned Region Sparsity and Diversity Also Predicts Visual Attention
Zijun Wei, Hossein Adeli, Minh Hoai Nguyen, Greg Zelinsky, Dimitris Samaras Learning a Metric Embedding for Face Recognition Using the Multibatch Method
Oren Tadmor, Tal Rosenwein, Shai Shalev-Shwartz, Yonatan Wexler, Amnon Shashua Learning and Forecasting Opinion Dynamics in Social Networks
Abir De, Isabel Valera, Niloy Ganguly, Sourangshu Bhattacharya, Manuel Gomez Rodriguez Learning Bayesian Networks with Ancestral Constraints
Eunice Yuh-Jie Chen, Yujia Shen, Arthur Choi, Adnan Darwiche Learning Deep Parsimonious Representations
Renjie Liao, Alex Schwing, Richard Zemel, Raquel Urtasun Learning Feed-Forward One-Shot Learners
Luca Bertinetto, João F. Henriques, Jack Valmadre, Philip Torr, Andrea Vedaldi Learning in Games: Robustness of Fast Convergence
Dylan J Foster, Zhiyuan Li, Thodoris Lykouris, Karthik Sridharan, Eva Tardos Learning Parametric Sparse Models for Image Super-Resolution
Yongbo Li, Weisheng Dong, Xuemei Xie, Guangming Shi, Xin Li, Donglai Xu Learning Structured Sparsity in Deep Neural Networks
Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, Hai Li Learning Supervised PageRank with Gradient-Based and Gradient-Free Optimization Methods
Lev Bogolubsky, Pavel Dvurechenskii, Alexander Gasnikov, Gleb Gusev, Yurii Nesterov, Andrei M Raigorodskii, Aleksey Tikhonov, Maksim Zhukovskii Learning to Communicate with Deep Multi-Agent Reinforcement Learning
Jakob Foerster, Ioannis Alexandros Assael, Nando de Freitas, Shimon Whiteson Learning to Learn by Gradient Descent by Gradient Descent
Marcin Andrychowicz, Misha Denil, Sergio Gómez, Matthew W Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, Nando de Freitas Learning to Poke by Poking: Experiential Learning of Intuitive Physics
Pulkit Agrawal, Ashvin V Nair, Pieter Abbeel, Jitendra Malik, Sergey Levine Learning User Perceived Clusters with Feature-Level Supervision
Ting-Yu Cheng, Guiguan Lin, Xinyang Gong, Kang-Jun Liu, Shan-Hung Wu Learning Values Across Many Orders of Magnitude
Hado P van Hasselt, Arthur Guez, Arthur Guez, Matteo Hessel, Volodymyr Mnih, David Silver Learning What and Where to Draw
Scott E Reed, Zeynep Akata, Santosh Mohan, Samuel Tenka, Bernt Schiele, Honglak Lee Linear Feature Encoding for Reinforcement Learning
Zhao Song, Ronald E Parr, Xuejun Liao, Lawrence Carin Local Minimax Complexity of Stochastic Convex Optimization
Sabyasachi Chatterjee, John C. Duchi, John Lafferty, Yuancheng Zhu Mapping Estimation for Discrete Optimal Transport
Michaël Perrot, Nicolas Courty, Rémi Flamary, Amaury Habrard Matching Networks for One Shot Learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Koray Kavukcuoglu, Daan Wierstra Maximal Sparsity with Deep Networks?
Bo Xin, Yizhou Wang, Wen Gao, David Wipf, Baoyuan Wang Measuring Neural Net Robustness with Constraints
Osbert Bastani, Yani Ioannou, Leonidas Lampropoulos, Dimitrios Vytiniotis, Aditya Nori, Antonio Criminisi Memory-Efficient Backpropagation Through Time
Audrunas Gruslys, Remi Munos, Ivo Danihelka, Marc Lanctot, Alex Graves Minimax Optimal Alternating Minimization for Kernel Nonparametric Tensor Learning
Taiji Suzuki, Heishiro Kanagawa, Hayato Kobayashi, Nobuyuki Shimizu, Yukihiro Tagami Mistake Bounds for Binary Matrix Completion
Mark Herbster, Stephen Pasteris, Massimiliano Pontil Multimodal Residual Learning for Visual QA
Jin-Hwa Kim, Sang-Woo Lee, Donghyun Kwak, Min-Oh Heo, Jeonghee Kim, Jung-Woo Ha, Byoung-Tak Zhang Multistage Campaigning in Social Networks
Mehrdad Farajtabar, Xiaojing Ye, Sahar Harati, Le Song, Hongyuan Zha Mutual Information for Symmetric Rank-One Matrix Estimation: A Proof of the Replica Formula
Jean Barbier, Mohamad Dia, Nicolas Macris, Florent Krzakala, Thibault Lesieur, Lenka Zdeborová Nearly Isometric Embedding by Relaxation
James McQueen, Marina Meila, Dominique Joncas Nested Mini-Batch K-Means
James Newling, François Fleuret Neural Universal Discrete Denoiser
Taesup Moon, Seonwoo Min, Byunghan Lee, Sungroh Yoon New Liftable Classes for First-Order Probabilistic Inference
Seyed Mehran Kazemi, Angelika Kimmig, Guy Van den Broeck, David Poole Normalized Spectral mAP Synchronization
Yanyao Shen, Qixing Huang, Nati Srebro, Sujay Sanghavi On Explore-Then-Commit Strategies
Aurelien Garivier, Tor Lattimore, Emilie Kaufmann On Mixtures of Markov Chains
Rishi Gupta, Ravi Kumar, Sergei Vassilvitskii On Multiplicative Integration with Recurrent Neural Networks
Yuhuai Wu, Saizheng Zhang, Ying Zhang, Yoshua Bengio, Ruslan Salakhutdinov Online Pricing with Strategic and Patient Buyers
Michal Feldman, Tomer Koren, Roi Livni, Yishay Mansour, Aviv Zohar Operator Variational Inference
Rajesh Ranganath, Dustin Tran, Jaan Altosaar, David Blei Orthogonal Random Features
Felix Xinnan X Yu, Ananda Theertha Suresh, Krzysztof M Choromanski, Daniel N Holtmann-Rice, Sanjiv Kumar PAC Reinforcement Learning with Rich Observations
Akshay Krishnamurthy, Alekh Agarwal, John Langford PAC-Bayesian Theory Meets Bayesian Inference
Pascal Germain, Francis Bach, Alexandre Lacoste, Simon Lacoste-Julien Pairwise Choice Markov Chains
Stephen Ragain, Johan Ugander Poisson-Gamma Dynamical Systems
Aaron Schein, Hanna Wallach, Mingyuan Zhou Preference Completion from Partial Rankings
Suriya Gunasekar, Oluwasanmi O Koyejo, Joydeep Ghosh Privacy Odometers and Filters: Pay-as-You-Go Composition
Ryan M Rogers, Aaron Roth, Jonathan Ullman, Salil Vadhan Probabilistic Linear Multistep Methods
Onur Teymur, Kostas Zygalakis, Ben Calderhead Probing the Compositionality of Intuitive Functions
Eric Schulz, Josh Tenenbaum, David K. Duvenaud, Maarten Speekenbrink, Samuel J Gershman Professor Forcing: A New Algorithm for Training Recurrent Networks
Alex M Lamb, Anirudh Goyal ALIAS PARTH Goyal, Ying Zhang, Saizheng Zhang, Aaron C. Courville, Yoshua Bengio Protein Contact Prediction from Amino Acid Co-Evolution Using Convolutional Networks for Graph-Valued Images
Vladimir Golkov, Marcin J Skwark, Antonij Golkov, Alexey Dosovitskiy, Thomas Brox, Jens Meiler, Daniel Cremers Proximal Deep Structured Models
Shenlong Wang, Sanja Fidler, Raquel Urtasun Quantum Perceptron Models
Ashish Kapoor, Nathan Wiebe, Krysta Svore Regret of Queueing Bandits
Subhashini Krishnasamy, Rajat Sen, Ramesh Johari, Sanjay Shakkottai Regularized Nonlinear Acceleration
Damien Scieur, Alexandre d'Aspremont, Francis Bach Review Networks for Caption Generation
Zhilin Yang, Ye Yuan, Yuexin Wu, William W. Cohen, Ruslan Salakhutdinov Reward Augmented Maximum Likelihood for Neural Structured Prediction
Mohammad Norouzi, Samy Bengio, Zhifeng Chen, Navdeep Jaitly, Mike Schuster, Yonghui Wu, Dale Schuurmans Robustness of Classifiers: From Adversarial to Random Noise
Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard Safe and Efficient Off-Policy Reinforcement Learning
Remi Munos, Tom Stepleton, Anna Harutyunyan, Marc Bellemare Sample Complexity of Automated Mechanism Design
Maria-Florina F Balcan, Tuomas Sandholm, Ellen Vitercik Sampling for Bayesian Program Learning
Kevin Ellis, Armando Solar-Lezama, Josh Tenenbaum Satisfying Real-World Goals with Dataset Constraints
Gabriel Goh, Andrew Cotter, Maya Gupta, Michael P Friedlander Scalable Adaptive Stochastic Optimization Using Random Projections
Gabriel Krummenacher, Brian McWilliams, Yannic Kilcher, Joachim M Buhmann, Nicolai Meinshausen Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes
Jack Rae, Jonathan J Hunt, Ivo Danihelka, Timothy Harley, Andrew W. Senior, Gregory Wayne, Alex Graves, Timothy Lillicrap Search Improves Label for Active Learning
Alina Beygelzimer, Daniel J. Hsu, John Langford, Chicheng Zhang Selective Inference for Group-Sparse Linear Models
Fan Yang, Rina Foygel Barber, Prateek Jain, John Lafferty Sequential Neural Models with Stochastic Layers
Marco Fraccaro, Søren Kaae Sønderby, Ulrich Paquet, Ole Winther Showing Versus Doing: Teaching by Demonstration
Mark K Ho, Michael Littman, James MacGlashan, Fiery Cushman, Joseph L Austerweil Single Pass PCA of Matrix Products
Shanshan Wu, Srinadh Bhojanapalli, Sujay Sanghavi, Alexandros G Dimakis Single-Image Depth Perception in the Wild
Weifeng Chen, Zhao Fu, Dawei Yang, Jia Deng Solving Marginal MAP Problems with NP Oracles and Parity Constraints
Yexiang Xue, Zhiyuan Li, Stefano Ermon, Carla P. Gomes, Bart Selman Sparse Support Recovery with Non-Smooth Loss Functions
Kévin Degraux, Gabriel Peyré, Jalal Fadili, Laurent Jacques Statistical Inference for Cluster Trees
Jisu Kim, Yen-Chi Chen, Sivaraman Balakrishnan, Alessandro Rinaldo, Larry Wasserman Stochastic Gradient MCMC with Stale Gradients
Changyou Chen, Nan Ding, Chunyuan Li, Yizhe Zhang, Lawrence Carin Stochastic Gradient Richardson-Romberg Markov Chain Monte Carlo
Alain Durmus, Umut Simsekli, Eric Moulines, Roland Badeau, Gaël Richard Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles
Stefan Lee, Senthil Purushwalkam Shiva Prakash, Michael Cogswell, Viresh Ranjan, David Crandall, Dhruv Batra Stochastic Online AUC Maximization
Yiming Ying, Longyin Wen, Siwei Lyu Stochastic Structured Prediction Under Bandit Feedback
Artem Sokolov, Julia Kreutzer, Stefan Riezler, Christopher Lo Stochastic Variational Deep Kernel Learning
Andrew G Wilson, Zhiting Hu, Ruslan Salakhutdinov, Eric P Xing Strategic Attentive Writer for Learning Macro-Actions
Alexander Vezhnevets, Volodymyr Mnih, Simon Osindero, Alex Graves, Oriol Vinyals, John Agapiou, Koray Kavukcuoglu Structure-Blind Signal Recovery
Dmitry Ostrovsky, Zaid Harchaoui, Anatoli Juditsky, Arkadi S. Nemirovski Sub-Sampled Newton Methods with Non-Uniform Sampling
Peng Xu, Jiyan Yang, Fred Roosta, Christopher Ré, Michael W. Mahoney Supervised Word Mover's Distance
Gao Huang, Chuan Guo, Matt J Kusner, Yu Sun, Fei Sha, Kilian Q. Weinberger SURGE: Surface Regularized Geometry Estimation from a Single Image
Peng Wang, Xiaohui Shen, Bryan Russell, Scott Cohen, Brian Price, Alan L. Yuille Synthesis of MCMC and Belief Propagation
Sung-Soo Ahn, Michael Chertkov, Jinwoo Shin Tagger: Deep Unsupervised Perceptual Grouping
Klaus Greff, Antti Rasmus, Mathias Berglund, Tele Hao, Harri Valpola, Jürgen Schmidhuber Tensor Switching Networks
Chuan-Yung Tsai, Andrew M Saxe, Andrew M Saxe, David Cox The Forget-Me-Not Process
Kieran Milan, Joel Veness, James Kirkpatrick, Michael Bowling, Anna Koop, Demis Hassabis The Generalized Reparameterization Gradient
Francisco R Ruiz, Michalis Titsias RC Aueb, David Blei The Multi-Fidelity Multi-Armed Bandit
Kirthevasan Kandasamy, Gautam Dasarathy, Barnabas Poczos, Jeff Schneider The Multiple Quantile Graphical Model
Alnur Ali, J. Zico Kolter, Ryan J Tibshirani The Power of Optimization from Samples
Eric Balkanski, Aviad Rubinstein, Yaron Singer The Product Cut
Thomas Laurent, James von Brecht, Xavier Bresson, Arthur Szlam Towards Conceptual Compression
Karol Gregor, Frederic Besse, Danilo Jimenez Rezende, Ivo Danihelka, Daan Wierstra Towards Unifying Hamiltonian Monte Carlo and Slice Sampling
Yizhe Zhang, Xiangyu Wang, Changyou Chen, Ricardo Henao, Kai Fan, Lawrence Carin Tree-Structured Reinforcement Learning for Sequential Object Localization
Zequn Jie, Xiaodan Liang, Jiashi Feng, Xiaojie Jin, Wen Lu, Shuicheng Yan Unifying Count-Based Exploration and Intrinsic Motivation
Marc Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, Remi Munos Universal Correspondence Network
Christopher B Choy, JunYoung Gwak, Silvio Savarese, Manmohan Chandraker Unsupervised Learning from Noisy Networks with Applications to Hi-C Data
Bo Wang, Junjie Zhu, Armin Pourshafeie, Oana Ursu, Serafim Batzoglou, Anshul Kundaje Unsupervised Learning of 3D Structure from Images
Danilo Jimenez Rezende, S. M. Ali Eslami, Shakir Mohamed, Peter Battaglia, Max Jaderberg, Nicolas Heess Using Fast Weights to Attend to the Recent past
Jimmy Ba, Geoffrey E. Hinton, Volodymyr Mnih, Joel Z. Leibo, Catalin Ionescu Value Iteration Networks
Aviv Tamar, Yi Wu, Garrett Thomas, Sergey Levine, Pieter Abbeel Variance Reduction in Stochastic Gradient Langevin Dynamics
Kumar Avinava Dubey, Sashank J. Reddi, Sinead A Williamson, Barnabas Poczos, Alexander J Smola, Eric P Xing Variational Autoencoder for Deep Learning of Images, Labels and Captions
Yunchen Pu, Zhe Gan, Ricardo Henao, Xin Yuan, Chunyuan Li, Andrew Stevens, Lawrence Carin VIME: Variational Information Maximizing Exploration
Rein Houthooft, Xi Chen, Xi Chen, Yan Duan, John Schulman, Filip De Turck, Pieter Abbeel Wasserstein Training of Restricted Boltzmann Machines
Grégoire Montavon, Klaus-Robert Müller, Marco Cuturi Yggdrasil: An Optimized System for Training Deep Decision Trees at Scale
Firas Abuzaid, Joseph K. Bradley, Feynman T Liang, Andrew Feng, Lee Yang, Matei Zaharia, Ameet S Talwalkar