ICML 2016
322 papers
A Deep Learning Approach to Unsupervised Ensemble Learning
Uri Shaham, Xiuyuan Cheng, Omer Dror, Ariel Jaffe, Boaz Nadler, Joseph Chang, Yuval Kluger A Kernel Test of Goodness of Fit
Kacper Chwialkowski, Heiko Strathmann, Arthur Gretton A New PAC-Bayesian Perspective on Domain Adaptation
Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant A Random Matrix Approach to Echo-State Neural Networks
Romain Couillet, Gilles Wainrib, Hafiz Tiomoko Ali, Harry Sevi A Ranking Approach to Global Optimization
Cedric Malherbe, Emile Contal, Nicolas Vayatis A Simple and Provable Algorithm for Sparse Diagonal CCA
Megasthenis Asteris, Anastasios Kyrillidis, Oluwasanmi Koyejo, Russell Poldrack A Theory of Generative ConvNet
Jianwen Xie, Yang Lu, Song-Chun Zhu, Yingnian Wu ADIOS: Architectures Deep in Output Space
Moustapha Cisse, Maruan Al-Shedivat, Samy Bengio An Optimal Algorithm for the Thresholding Bandit Problem
Andrea Locatelli, Maurilio Gutzeit, Alexandra Carpentier Analysis of Deep Neural Networks with Extended Data Jacobian Matrix
Shengjie Wang, Abdel-rahman Mohamed, Rich Caruana, Jeff Bilmes, Matthai Plilipose, Matthew Richardson, Krzysztof Geras, Gregor Urban, Ozlem Aslan Ask Me Anything: Dynamic Memory Networks for Natural Language Processing
Ankit Kumar, Ozan Irsoy, Peter Ondruska, Mohit Iyyer, James Bradbury, Ishaan Gulrajani, Victor Zhong, Romain Paulus, Richard Socher Associative Long Short-Term Memory
Ivo Danihelka, Greg Wayne, Benigno Uria, Nal Kalchbrenner, Alex Graves Asynchronous Methods for Deep Reinforcement Learning
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, Koray Kavukcuoglu Autoencoding Beyond Pixels Using a Learned Similarity Metric
Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Hugo Larochelle, Ole Winther Auxiliary Deep Generative Models
Lars Maaløe, Casper Kaae Sønderby, Søren Kaae Sønderby, Ole Winther Benchmarking Deep Reinforcement Learning for Continuous Control
Yan Duan, Xi Chen, Rein Houthooft, John Schulman, Pieter Abbeel Beyond CCA: Moment Matching for Multi-View Models
Anastasia Podosinnikova, Francis Bach, Simon Lacoste-Julien Bidirectional Helmholtz Machines
Jorg Bornschein, Samira Shabanian, Asja Fischer, Yoshua Bengio Binary Embeddings with Structured Hashed Projections
Anna Choromanska, Krzysztof Choromanski, Mariusz Bojarski, Tony Jebara, Sanjiv Kumar, Yann LeCun Black-Box Alpha Divergence Minimization
Jose Hernandez-Lobato, Yingzhen Li, Mark Rowland, Thang Bui, Daniel Hernandez-Lobato, Richard Turner Community Recovery in Graphs with Locality
Yuxin Chen, Govinda Kamath, Changho Suh, David Tse Complex Embeddings for Simple Link Prediction
Théo Trouillon, Johannes Welbl, Sebastian Riedel, Eric Gaussier, Guillaume Bouchard Compressive Spectral Clustering
Nicolas Tremblay, Gilles Puy, Remi Gribonval, Pierre Vandergheynst Conservative Bandits
Yifan Wu, Roshan Shariff, Tor Lattimore, Csaba Szepesvari Contextual Combinatorial Cascading Bandits
Shuai Li, Baoxiang Wang, Shengyu Zhang, Wei Chen Continuous Deep Q-Learning with Model-Based Acceleration
Shixiang Gu, Timothy Lillicrap, Ilya Sutskever, Sergey Levine Correcting Forecasts with Multifactor Neural Attention
Matthew Riemer, Aditya Vempaty, Flavio Calmon, Fenno Heath, Richard Hull, Elham Khabiri CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy
Ran Gilad-Bachrach, Nathan Dowlin, Kim Laine, Kristin Lauter, Michael Naehrig, John Wernsing DCM Bandits: Learning to Rank with Multiple Clicks
Sumeet Katariya, Branislav Kveton, Csaba Szepesvari, Zheng Wen Deconstructing the Ladder Network Architecture
Mohammad Pezeshki, Linxi Fan, Philemon Brakel, Aaron Courville, Yoshua Bengio Deep Gaussian Processes for Regression Using Approximate Expectation Propagation
Thang Bui, Daniel Hernandez-Lobato, Jose Hernandez-Lobato, Yingzhen Li, Richard Turner Deep Speech 2 : End-to-End Speech Recognition in English and Mandarin
Dario Amodei, Sundaram Ananthanarayanan, Rishita Anubhai, Jingliang Bai, Eric Battenberg, Carl Case, Jared Casper, Bryan Catanzaro, Qiang Cheng, Guoliang Chen, Jie Chen, Jingdong Chen, Zhijie Chen, Mike Chrzanowski, Adam Coates, Greg Diamos, Ke Ding, Niandong Du, Erich Elsen, Jesse Engel, Weiwei Fang, Linxi Fan, Christopher Fougner, Liang Gao, Caixia Gong, Awni Hannun, Tony Han, Lappi Johannes, Bing Jiang, Cai Ju, Billy Jun, Patrick LeGresley, Libby Lin, Junjie Liu, Yang Liu, Weigao Li, Xiangang Li, Dongpeng Ma, Sharan Narang, Andrew Ng, Sherjil Ozair, Yiping Peng, Ryan Prenger, Sheng Qian, Zongfeng Quan, Jonathan Raiman, Vinay Rao, Sanjeev Satheesh, David Seetapun, Shubho Sengupta, Kavya Srinet, Anuroop Sriram, Haiyuan Tang, Liliang Tang, Chong Wang, Jidong Wang, Kaifu Wang, Yi Wang, Zhijian Wang, Zhiqian Wang, Shuang Wu, Likai Wei, Bo Xiao, Wen Xie, Yan Xie, Dani Yogatama, Bin Yuan, Jun Zhan, Zhenyao Zhu Dictionary Learning for Massive Matrix Factorization
Arthur Mensch, Julien Mairal, Bertrand Thirion, Gael Varoquaux Differentially Private Policy Evaluation
Borja Balle, Maziar Gomrokchi, Doina Precup Discrete Deep Feature Extraction: A Theory and New Architectures
Thomas Wiatowski, Michael Tschannen, Aleksandar Stanic, Philipp Grohs, Helmut Boelcskei Domain Adaptation with Conditional Transferable Components
Mingming Gong, Kun Zhang, Tongliang Liu, Dacheng Tao, Clark Glymour, Bernhard Schölkopf Dropout Distillation
Samuel Rota Bulò, Lorenzo Porzi, Peter Kontschieder Dueling Network Architectures for Deep Reinforcement Learning
Ziyu Wang, Tom Schaul, Matteo Hessel, Hado Hasselt, Marc Lanctot, Nando Freitas Dynamic Capacity Networks
Amjad Almahairi, Nicolas Ballas, Tim Cooijmans, Yin Zheng, Hugo Larochelle, Aaron Courville Efficient Algorithms for Adversarial Contextual Learning
Vasilis Syrgkanis, Akshay Krishnamurthy, Robert Schapire Energetic Natural Gradient Descent
Philip Thomas, Bruno Castro Silva, Christoph Dann, Emma Brunskill Estimating Cosmological Parameters from the Dark Matter Distribution
Siamak Ravanbakhsh, Junier Oliva, Sebastian Fromenteau, Layne Price, Shirley Ho, Jeff Schneider, Barnabas Poczos Estimation from Indirect Supervision with Linear Moments
Aditi Raghunathan, Roy Frostig, John Duchi, Percy Liang Extended and Unscented Kitchen Sinks
Edwin Bonilla, Daniel Steinberg, Alistair Reid Extreme F-Measure Maximization Using Sparse Probability Estimates
Kalina Jasinska, Krzysztof Dembczynski, Robert Busa-Fekete, Karlson Pfannschmidt, Timo Klerx, Eyke Hullermeier Fast Algorithms for Segmented Regression
Jayadev Acharya, Ilias Diakonikolas, Jerry Li, Ludwig Schmidt Faster Eigenvector Computation via Shift-and-Invert Preconditioning
Dan Garber, Elad Hazan, Chi Jin, Sham, Cameron Musco, Praneeth Netrapalli, Aaron Sidford Gaussian Process Nonparametric Tensor Estimator and Its Minimax Optimality
Heishiro Kanagawa, Taiji Suzuki, Hayato Kobayashi, Nobuyuki Shimizu, Yukihiro Tagami Generative Adversarial Text to Image Synthesis
Scott Reed, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, Honglak Lee Geometric Mean Metric Learning
Pourya Zadeh, Reshad Hosseini, Suvrit Sra Greedy Column Subset Selection: New Bounds and Distributed Algorithms
Jason Altschuler, Aditya Bhaskara, Gang Fu, Vahab Mirrokni, Afshin Rostamizadeh, Morteza Zadimoghaddam Hierarchical Variational Models
Rajesh Ranganath, Dustin Tran, David Blei Horizontally Scalable Submodular Maximization
Mario Lucic, Olivier Bachem, Morteza Zadimoghaddam, Andreas Krause How to Fake Multiply by a Gaussian Matrix
Michael Kapralov, Vamsi Potluru, David Woodruff Interacting Particle Markov Chain Monte Carlo
Tom Rainforth, Christian Naesseth, Fredrik Lindsten, Brooks Paige, Jan-Willem Vandemeent, Arnaud Doucet, Frank Wood Isotonic Hawkes Processes
Yichen Wang, Bo Xie, Nan Du, Le Song K-Variates++: More Pluses in the K-Means++
Richard Nock, Raphael Canyasse, Roksana Boreli, Frank Nielsen Learning Convolutional Neural Networks for Graphs
Mathias Niepert, Mohamed Ahmed, Konstantin Kutzkov Learning Privately from Multiparty Data
Jihun Hamm, Yingjun Cao, Mikhail Belkin Learning Simple Algorithms from Examples
Wojciech Zaremba, Tomas Mikolov, Armand Joulin, Rob Fergus Learning to Generate with Memory
Chongxuan Li, Jun Zhu, Bo Zhang Low-Rank Matrix Approximation with Stability
Dongsheng Li, Chao Chen, Qin Lv, Junchi Yan, Li Shang, Stephen Chu Low-Rank Solutions of Linear Matrix Equations via Procrustes Flow
Stephen Tu, Ross Boczar, Max Simchowitz, Mahdi Soltanolkotabi, Ben Recht Meta-Learning with Memory-Augmented Neural Networks
Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, Timothy Lillicrap Metadata-Conscious Anonymous Messaging
Giulia Fanti, Peter Kairouz, Sewoong Oh, Kannan Ramchandran, Pramod Viswanath Minding the Gaps for Block Frank-Wolfe Optimization of Structured SVMs
Anton Osokin, Jean-Baptiste Alayrac, Isabella Lukasewitz, Puneet Dokania, Simon Lacoste-Julien Model-Free Trajectory Optimization for Reinforcement Learning
Riad Akrour, Gerhard Neumann, Hany Abdulsamad, Abbas Abdolmaleki Network Morphism
Tao Wei, Changhu Wang, Yong Rui, Chang Wen Chen Noisy Activation Functions
Caglar Gulcehre, Marcin Moczulski, Misha Denil, Yoshua Bengio Non-Negative Matrix Factorization Under Heavy Noise
Chiranjib Bhattacharya, Navin Goyal, Ravindran Kannan, Jagdeep Pani One-Shot Generalization in Deep Generative Models
Danilo Rezende, Shakir, Ivo Danihelka, Karol Gregor, Daan Wierstra Online Stochastic Linear Optimization Under One-Bit Feedback
Lijun Zhang, Tianbao Yang, Rong Jin, Yichi Xiao, Zhi-hua Zhou Opponent Modeling in Deep Reinforcement Learning
He He, Jordan Boyd-Graber, Kevin Kwok, Hal Daumé Optimal Classification with Multivariate Losses
Nagarajan Natarajan, Oluwasanmi Koyejo, Pradeep Ravikumar, Inderjit Dhillon Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithms
Yu-Xiang Wang, Veeranjaneyulu Sadhanala, Wei Dai, Willie Neiswanger, Suvrit Sra, Eric Xing Persistent RNNs: Stashing Recurrent Weights On-Chip
Greg Diamos, Shubho Sengupta, Bryan Catanzaro, Mike Chrzanowski, Adam Coates, Erich Elsen, Jesse Engel, Awni Hannun, Sanjeev Satheesh PHOG: Probabilistic Model for Code
Pavol Bielik, Veselin Raychev, Martin Vechev Pixel Recurrent Neural Networks
Aäron Oord, Nal Kalchbrenner, Koray Kavukcuoglu Pliable Rejection Sampling
Akram Erraqabi, Michal Valko, Alexandra Carpentier, Odalric Maillard Preconditioning Kernel Matrices
Kurt Cutajar, Michael Osborne, John Cunningham, Maurizio Filippone Predictive Entropy Search for Multi-Objective Bayesian Optimization
Daniel Hernandez-Lobato, Jose Hernandez-Lobato, Amar Shah, Ryan Adams Pricing a Low-Regret Seller
Hoda Heidari, Mohammad Mahdian, Umar Syed, Sergei Vassilvitskii, Sadra Yazdanbod Primal-Dual Rates and Certificates
Celestine Dünner, Simone Forte, Martin Takac, Martin Jaggi Provable Algorithms for Inference in Topic Models
Sanjeev Arora, Rong Ge, Frederic Koehler, Tengyu Ma, Ankur Moitra Recommendations as Treatments: Debiasing Learning and Evaluation
Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, Thorsten Joachims Representational Similarity Learning with Application to Brain Networks
Urvashi Oswal, Christopher Cox, Matthew Lambon-Ralph, Timothy Rogers, Robert Nowak Softened Approximate Policy Iteration for Markov Games
Julien Pérolat, Bilal Piot, Matthieu Geist, Bruno Scherrer, Olivier Pietquin Sparse Parameter Recovery from Aggregated Data
Avradeep Bhowmik, Joydeep Ghosh, Oluwasanmi Koyejo Stability of Controllers for Gaussian Process Forward Models
Julia Vinogradska, Bastian Bischoff, Duy Nguyen-Tuong, Anne Romer, Henner Schmidt, Jan Peters Stochastic Quasi-Newton Langevin Monte Carlo
Umut Simsekli, Roland Badeau, Taylan Cemgil, Gaël Richard Stochastic Variance Reduction for Nonconvex Optimization
Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabas Poczos, Alex Smola Stratified Sampling Meets Machine Learning
Edo Liberty, Kevin Lang, Konstantin Shmakov Structure Learning of Partitioned Markov Networks
Song Liu, Taiji Suzuki, Masashi Sugiyama, Kenji Fukumizu The Arrow of Time in Multivariate Time Series
Stefan Bauer, Bernhard Schölkopf, Jonas Peters The Information Sieve
Greg Ver Steeg, Aram Galstyan Training Neural Networks Without Gradients: A Scalable ADMM Approach
Gavin Taylor, Ryan Burmeister, Zheng Xu, Bharat Singh, Ankit Patel, Tom Goldstein Truthful Univariate Estimators
Ioannis Caragiannis, Ariel Procaccia, Nisarg Shah Variable Elimination in the Fourier Domain
Yexiang Xue, Stefano Ermon, Ronan Le Bras, Carla, Bart Selman