NeurIPS 2015
403 papers
3D Object Proposals for Accurate Object Class Detection
Xiaozhi Chen, Kaustav Kundu, Yukun Zhu, Andrew G Berneshawi, Huimin Ma, Sanja Fidler, Raquel Urtasun A Gaussian Process Model of Quasar Spectral Energy Distributions
Andrew Miller, Albert Wu, Jeff Regier, Jon McAuliffe, Dustin Lang, Mr. Prabhat, David Schlegel, Ryan P. Adams A Market Framework for Eliciting Private Data
Bo Waggoner, Rafael Frongillo, Jacob D. Abernethy A Recurrent Latent Variable Model for Sequential Data
Junyoung Chung, Kyle Kastner, Laurent Dinh, Kratarth Goel, Aaron C. Courville, Yoshua Bengio A Reduced-Dimension fMRI Shared Response Model
Po-Hsuan Chen, Janice Chen, Yaara Yeshurun, Uri Hasson, James Haxby, Peter J. Ramadge A Theory of Decision Making Under Dynamic Context
Michael Shvartsman, Vaibhav Srivastava, Jonathan D. Cohen Action-Conditional Video Prediction Using Deep Networks in Atari Games
Junhyuk Oh, Xiaoxiao Guo, Honglak Lee, Richard L. Lewis, Satinder Singh Adaptive Online Learning
Dylan J Foster, Alexander Rakhlin, Karthik Sridharan Approximating Sparse PCA from Incomplete Data
Abhisek Kundu, Petros Drineas, Malik Magdon-Ismail Attention-Based Models for Speech Recognition
Jan K Chorowski, Dzmitry Bahdanau, Dmitriy Serdyuk, Kyunghyun Cho, Yoshua Bengio Automatic Variational Inference in Stan
Alp Kucukelbir, Rajesh Ranganath, Andrew Gelman, David Blei Backpropagation for Energy-Efficient Neuromorphic Computing
Steve K Esser, Rathinakumar Appuswamy, Paul Merolla, John V. Arthur, Dharmendra S Modha BACKSHIFT: Learning Causal Cyclic Graphs from Unknown Shift Interventions
Dominik Rothenhäusler, Christina Heinze, Jonas Peters, Nicolai Meinshausen Barrier Frank-Wolfe for Marginal Inference
Rahul G Krishnan, Simon Lacoste-Julien, David Sontag Bayesian Active Model Selection with an Application to Automated Audiometry
Jacob Gardner, Gustavo Malkomes, Roman Garnett, Kilian Q. Weinberger, Dennis Barbour, John P. Cunningham Bayesian Dark Knowledge
Anoop Korattikara Balan, Vivek Rathod, Kevin P. Murphy, Max Welling Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM)
Mijung Park, Wittawat Jitkrittum, Ahmad Qamar, Zoltan Szabo, Lars Buesing, Maneesh Sahani Bayesian Optimization with Exponential Convergence
Kenji Kawaguchi, Leslie Pack Kaelbling, Tomás Lozano-Pérez Bidirectional Recurrent Neural Networks as Generative Models
Mathias Berglund, Tapani Raiko, Mikko Honkala, Leo Kärkkäinen, Akos Vetek, Juha T Karhunen Biologically Inspired Dynamic Textures for Probing Motion Perception
Jonathan Vacher, Andrew Isaac Meso, Laurent U Perrinet, Gabriel Peyré Calibrated Structured Prediction
Volodymyr Kuleshov, Percy Liang COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-Evolution
Mehrdad Farajtabar, Yichen Wang, Manuel Gomez Rodriguez, Shuang Li, Hongyuan Zha, Le Song Column Selection via Adaptive Sampling
Saurabh Paul, Malik Magdon-Ismail, Petros Drineas Combinatorial Bandits Revisited
Richard Combes, Mohammad Sadegh Talebi Mazraeh Shahi, Alexandre Proutiere, Marc Lelarge Combinatorial Cascading Bandits
Branislav Kveton, Zheng Wen, Azin Ashkan, Csaba Szepesvari Consistent Multilabel Classification
Oluwasanmi O Koyejo, Nagarajan Natarajan, Pradeep K Ravikumar, Inderjit S Dhillon Convergence Rates of Active Learning for Maximum Likelihood Estimation
Kamalika Chaudhuri, Sham M. Kakade, Praneeth Netrapalli, Sujay Sanghavi Convolutional Networks on Graphs for Learning Molecular Fingerprints
David K. Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alan Aspuru-Guzik, Ryan P. Adams Copeland Dueling Bandits
Masrour Zoghi, Zohar S Karnin, Shimon Whiteson, Maarten de Rijke Copula Variational Inference
Dustin Tran, David Blei, Edoardo M. Airoldi Deep Convolutional Inverse Graphics Network
Tejas D Kulkarni, William F. Whitney, Pushmeet Kohli, Josh Tenenbaum Deep Knowledge Tracing
Chris Piech, Jonathan Bassen, Jonathan Huang, Surya Ganguli, Mehran Sahami, Leonidas Guibas, Jascha Sohl-Dickstein Deep Learning with Elastic Averaging SGD
Sixin Zhang, Anna E Choromanska, Yann LeCun Deep Poisson Factor Modeling
Ricardo Henao, Zhe Gan, James Lu, Lawrence Carin Deep Temporal Sigmoid Belief Networks for Sequence Modeling
Zhe Gan, Chunyuan Li, Ricardo Henao, David E Carlson, Lawrence Carin Deep Visual Analogy-Making
Scott E Reed, Yi Zhang, Yuting Zhang, Honglak Lee Deeply Learning the Messages in Message Passing Inference
Guosheng Lin, Chunhua Shen, Ian Reid, Anton van den Hengel Discrete Rényi Classifiers
Meisam Razaviyayn, Farzan Farnia, David Tse Discriminative Robust Transformation Learning
Jiaji Huang, Qiang Qiu, Guillermo Sapiro, Robert Calderbank Distributed Submodular Cover: Succinctly Summarizing Massive Data
Baharan Mirzasoleiman, Amin Karbasi, Ashwinkumar Badanidiyuru, Andreas Krause Distributionally Robust Logistic Regression
Soroosh Shafieezadeh-Abadeh, Peyman Mohajerin Esfahani, Daniel Huhn Efficient and Parsimonious Agnostic Active Learning
Tzu-Kuo Huang, Alekh Agarwal, Daniel J. Hsu, John Langford, Robert E. Schapire Efficient and Robust Automated Machine Learning
Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Springenberg, Manuel Blum, Frank Hutter Efficient Non-Greedy Optimization of Decision Trees
Mohammad Norouzi, Maxwell Collins, Matthew A Johnson, David J Fleet, Pushmeet Kohli Efficient Output Kernel Learning for Multiple Tasks
Pratik Kumar Jawanpuria, Maksim Lapin, Matthias Hein, Bernt Schiele Embedding Inference for Structured Multilabel Prediction
Farzaneh Mirzazadeh, Siamak Ravanbakhsh, Nan Ding, Dale Schuurmans End-to-End Learning of LDA by Mirror-Descent Back Propagation over a Deep Architecture
Jianshu Chen, Ji He, Yelong Shen, Lin Xiao, Xiaodong He, Jianfeng Gao, Xinying Song, Li Deng End-to-End Memory Networks
Sainbayar Sukhbaatar, Arthur Szlam, Jason Weston, Rob Fergus Expectation Particle Belief Propagation
Thibaut Lienart, Yee Whye Teh, Arnaud Doucet Fast and Guaranteed Tensor Decomposition via Sketching
Yining Wang, Hsiao-Yu Tung, Alexander J Smola, Anima Anandkumar Fast Convergence of Regularized Learning in Games
Vasilis Syrgkanis, Alekh Agarwal, Haipeng Luo, Robert E. Schapire Fast Distributed K-Center Clustering with Outliers on Massive Data
Gustavo Malkomes, Matt J Kusner, Wenlin Chen, Kilian Q. Weinberger, Benjamin Moseley Fast Lifted MAP Inference via Partitioning
Somdeb Sarkhel, Parag Singla, Vibhav G Gogate Fighting Bandits with a New Kind of Smoothness
Jacob D. Abernethy, Chansoo Lee, Ambuj Tewari Generalization in Adaptive Data Analysis and Holdout Reuse
Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toni Pitassi, Omer Reingold, Aaron Roth GP Kernels for Cross-Spectrum Analysis
Kyle R Ulrich, David E Carlson, Kafui Dzirasa, Lawrence Carin Gradient Estimation Using Stochastic Computation Graphs
John Schulman, Nicolas Heess, Theophane Weber, Pieter Abbeel Gradient-Free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families
Heiko Strathmann, Dino Sejdinovic, Samuel Livingstone, Zoltan Szabo, Arthur Gretton Grammar as a Foreign Language
Oriol Vinyals, Łukasz Kaiser, Terry Koo, Slav Petrov, Ilya Sutskever, Geoffrey Hinton Halting in Random Walk Kernels
Mahito Sugiyama, Karsten Borgwardt Hidden Technical Debt in Machine Learning Systems
D. Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-François Crespo, Dan Dennison Human Memory Search as Initial-Visit Emitting Random Walk
Kwang-Sung Jun, Xiaojin Zhu, Timothy T. Rogers, Zhuoran Yang, Ming Yuan Infinite Factorial Dynamical Model
Isabel Valera, Francisco Ruiz, Lennart Svensson, Fernando Perez-Cruz Interactive Control of Diverse Complex Characters with Neural Networks
Igor Mordatch, Kendall Lowrey, Galen Andrew, Zoran Popovic, Emanuel V. Todorov Kullback-Leibler Proximal Variational Inference
Mohammad Emtiyaz Khan, Pierre Baque, François Fleuret, Pascal Fua Learnability of Influence in Networks
Harikrishna Narasimhan, David C. Parkes, Yaron Singer Learning Bayesian Networks with Thousands of Variables
Mauro Scanagatta, Cassio P de Campos, Giorgio Corani, Marco Zaffalon Learning Causal Graphs with Small Interventions
Karthikeyan Shanmugam, Murat Kocaoglu, Alexandros G Dimakis, Sriram Vishwanath Learning Continuous Control Policies by Stochastic Value Gradients
Nicolas Heess, Gregory Wayne, David Silver, Timothy Lillicrap, Tom Erez, Yuval Tassa Learning Spatiotemporal Trajectories from Manifold-Valued Longitudinal Data
Jean-Baptiste Schiratti, Stéphanie Allassonniere, Olivier Colliot, Stanley Durrleman Learning to Linearize Under Uncertainty
Ross Goroshin, Michael F Mathieu, Yann LeCun Learning to Segment Object Candidates
Pedro O O. Pinheiro, Ronan Collobert, Piotr Dollar Learning to Transduce with Unbounded Memory
Edward Grefenstette, Karl Moritz Hermann, Mustafa Suleyman, Phil Blunsom Learning Visual Biases from Human Imagination
Carl Vondrick, Hamed Pirsiavash, Aude Oliva, Antonio Torralba Learning Wake-Sleep Recurrent Attention Models
Jimmy Ba, Ruslan Salakhutdinov, Roger B Grosse, Brendan J. Frey Learning with a Wasserstein Loss
Charlie Frogner, Chiyuan Zhang, Hossein Mobahi, Mauricio Araya, Tomaso A Poggio Less Is More: Nyström Computational Regularization
Alessandro Rudi, Raffaello Camoriano, Lorenzo Rosasco Lifelong Learning with Non-I.i.d. Tasks
Anastasia Pentina, Christoph H. Lampert Lifted Inference Rules with Constraints
Happy Mittal, Anuj Mahajan, Vibhav G Gogate, Parag Singla M-Best-Diverse Labelings for Submodular Energies and Beyond
Alexander Kirillov, Dmytro Shlezinger, Dmitry P Vetrov, Carsten Rother, Bogdan Savchynskyy Matrix Completion with Noisy Side Information
Kai-Yang Chiang, Cho-Jui Hsieh, Inderjit S Dhillon Max-Margin Deep Generative Models
Chongxuan Li, Jun Zhu, Tianlin Shi, Bo Zhang MCMC for Variationally Sparse Gaussian Processes
James Hensman, Alexander G Matthews, Maurizio Filippone, Zoubin Ghahramani Minimax Time Series Prediction
Wouter M. Koolen, Alan Malek, Peter L Bartlett, Yasin Abbasi Yadkori Model-Based Relative Entropy Stochastic Search
Abbas Abdolmaleki, Rudolf Lioutikov, Jan R Peters, Nuno Lau, Luis Pualo Reis, Gerhard Neumann Natural Neural Networks
Guillaume Desjardins, Karen Simonyan, Razvan Pascanu, Koray Kavukcuoglu Nearly Optimal Private LASSO
Kunal Talwar, Abhradeep Guha Thakurta, Li Zhang Neural Adaptive Sequential Monte Carlo
Shixiang Gu, Zoubin Ghahramani, Richard E Turner No-Regret Learning in Bayesian Games
Jason Hartline, Vasilis Syrgkanis, Eva Tardos Nonparametric Von Mises Estimators for Entropies, Divergences and Mutual Informations
Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabas Poczos, Larry Wasserman, James M Robins On Elicitation Complexity
Rafael Frongillo, Ian Kash On the Accuracy of Self-Normalized Log-Linear Models
Jacob Andreas, Maxim Rabinovich, Michael I Jordan, Dan Klein On-the-Job Learning with Bayesian Decision Theory
Keenon Werling, Arun Tejasvi Chaganty, Percy Liang, Christopher D. Manning Online F-Measure Optimization
Róbert Busa-Fekete, Balázs Szörényi, Krzysztof Dembczynski, Eyke Hüllermeier Online Gradient Boosting
Alina Beygelzimer, Elad Hazan, Satyen Kale, Haipeng Luo Optimal Ridge Detection Using Coverage Risk
Yen-Chi Chen, Christopher R Genovese, Shirley Ho, Larry Wasserman Optimal Testing for Properties of Distributions
Jayadev Acharya, Constantinos Daskalakis, Gautam Kamath Orthogonal NMF Through Subspace Exploration
Megasthenis Asteris, Dimitris Papailiopoulos, Alexandros G Dimakis Parallel Correlation Clustering on Big Graphs
Xinghao Pan, Dimitris Papailiopoulos, Samet Oymak, Benjamin Recht, Kannan Ramchandran, Michael I Jordan Parallelizing MCMC with Random Partition Trees
Xiangyu Wang, Fangjian Guo, Katherine A. Heller, David B Dunson Particle Gibbs for Infinite Hidden Markov Models
Nilesh Tripuraneni, Shixiang Gu, Hong Ge, Zoubin Ghahramani Pointer Networks
Oriol Vinyals, Meire Fortunato, Navdeep Jaitly Policy Evaluation Using the Ω-Return
Philip S. Thomas, Scott Niekum, Georgios Theocharous, George Konidaris Policy Gradient for Coherent Risk Measures
Aviv Tamar, Yinlam Chow, Mohammad Ghavamzadeh, Shie Mannor Practical and Optimal LSH for Angular Distance
Alexandr Andoni, Piotr Indyk, Thijs Laarhoven, Ilya Razenshteyn, Ludwig Schmidt Preconditioned Spectral Descent for Deep Learning
David E Carlson, Edo Collins, Ya-Ping Hsieh, Lawrence Carin, Volkan Cevher Private Graphon Estimation for Sparse Graphs
Christian Borgs, Jennifer Chayes, Adam Smith Rate-Agnostic (Causal) Structure Learning
Sergey Plis, David Danks, Cynthia Freeman, Vince Calhoun Recognizing Retinal Ganglion Cells in the Dark
Emile Richard, Georges A Goetz, E. J. Chichilnisky Rectified Factor Networks
Djork-Arné Clevert, Andreas Mayr, Thomas Unterthiner, Sepp Hochreiter Regularization Path of Cross-Validation Error Lower Bounds
Atsushi Shibagaki, Yoshiki Suzuki, Masayuki Karasuyama, Ichiro Takeuchi Rethinking LDA: Moment Matching for Discrete ICA
Anastasia Podosinnikova, Francis Bach, Simon Lacoste-Julien Robust PCA with Compressed Data
Wooseok Ha, Rina Foygel Barber Robust Portfolio Optimization
Huitong Qiu, Fang Han, Han Liu, Brian Caffo Robust Regression via Hard Thresholding
Kush Bhatia, Prateek Jain, Purushottam Kar Sampling from Probabilistic Submodular Models
Alkis Gotovos, Hamed Hassani, Andreas Krause Secure Multi-Party Differential Privacy
Peter Kairouz, Sewoong Oh, Pramod Viswanath Semi-Supervised Factored Logistic Regression for High-Dimensional Neuroimaging Data
Danilo Bzdok, Michael Eickenberg, Olivier Grisel, Bertrand Thirion, Gael Varoquaux Semi-Supervised Learning with Ladder Networks
Antti Rasmus, Mathias Berglund, Mikko Honkala, Harri Valpola, Tapani Raiko Shepard Convolutional Neural Networks
Jimmy SJ Ren, Li Xu, Qiong Yan, Wenxiu Sun Skip-Thought Vectors
Ryan Kiros, Yukun Zhu, Ruslan Salakhutdinov, Richard Zemel, Raquel Urtasun, Antonio Torralba, Sanja Fidler Softstar: Heuristic-Guided Probabilistic Inference
Mathew Monfort, Brenden M Lake, Brenden M Lake, Brian Ziebart, Patrick Lucey, Josh Tenenbaum Space-Time Local Embeddings
Ke Sun, Jun Wang, Alexandros Kalousis, Stephane Marchand-Maillet Sparse and Low-Rank Tensor Decomposition
Parikshit Shah, Nikhil Rao, Gongguo Tang Sparse Linear Programming via Primal and Dual Augmented Coordinate Descent
Ian En-Hsu Yen, Kai Zhong, Cho-Jui Hsieh, Pradeep K Ravikumar, Inderjit S Dhillon Sparse Local Embeddings for Extreme Multi-Label Classification
Kush Bhatia, Himanshu Jain, Purushottam Kar, Manik Varma, Prateek Jain Sparse PCA via Bipartite Matchings
Megasthenis Asteris, Dimitris Papailiopoulos, Anastasios Kyrillidis, Alexandros G Dimakis Spatial Transformer Networks
Max Jaderberg, Karen Simonyan, Andrew Zisserman, Koray Kavukcuoglu Spectral Norm Regularization of Orthonormal Representations for Graph Transduction
Rakesh Shivanna, Bibaswan K Chatterjee, Raman Sankaran, Chiranjib Bhattacharyya, Francis Bach Spherical Random Features for Polynomial Kernels
Jeffrey Pennington, Felix Xinnan X Yu, Sanjiv Kumar Statistical Topological Data Analysis - A Kernel Perspective
Roland Kwitt, Stefan Huber, Marc Niethammer, Weili Lin, Ulrich Bauer Stochastic Expectation Propagation
Yingzhen Li, José Miguel Hernández-Lobato, Richard E Turner StopWasting My Gradients: Practical SVRG
Reza Babanezhad Harikandeh, Mohamed Osama Ahmed, Alim Virani, Mark Schmidt, Jakub Konečný, Scott Sallinen Streaming Min-Max Hypergraph Partitioning
Dan Alistarh, Jennifer Iglesias, Milan Vojnovic Submodular Hamming Metrics
Jennifer A Gillenwater, Rishabh K Iyer, Bethany Lusch, Rahul Kidambi, Jeff A. Bilmes Super-Resolution Off the Grid
Qingqing Huang, Sham M. Kakade Taming the Wild: A Unified Analysis of Hogwild-Style Algorithms
Christopher M De Sa, Ce Zhang, Kunle Olukotun, Christopher Ré, Christopher Ré Teaching Machines to Read and Comprehend
Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, Phil Blunsom Tensorizing Neural Networks
Alexander Novikov, Dmitrii Podoprikhin, Anton Osokin, Dmitry P Vetrov The Human Kernel
Andrew G Wilson, Christoph Dann, Chris Lucas, Eric P Xing The Poisson Gamma Belief Network
Mingyuan Zhou, Yulai Cong, Bo Chen Top-K Multiclass SVM
Maksim Lapin, Matthias Hein, Bernt Schiele Tractable Learning for Complex Probability Queries
Jessa Bekker, Jesse Davis, Arthur Choi, Adnan Darwiche, Guy Van den Broeck Training Very Deep Networks
Rupesh K Srivastava, Klaus Greff, Jürgen Schmidhuber Unsupervised Learning by Program Synthesis
Kevin Ellis, Armando Solar-Lezama, Josh Tenenbaum Variance Reduced Stochastic Gradient Descent with Neighbors
Thomas Hofmann, Aurelien Lucchi, Simon Lacoste-Julien, Brian McWilliams Variational Consensus Monte Carlo
Maxim Rabinovich, Elaine Angelino, Michael I Jordan Visalogy: Answering Visual Analogy Questions
Fereshteh Sadeghi, C. Lawrence Zitnick, Ali Farhadi Where Are They Looking?
Adria Recasens, Aditya Khosla, Carl Vondrick, Antonio Torralba Winner-Take-All Autoencoders
Alireza Makhzani, Brendan J. Frey