NeurIPS 2013
360 papers
A Gang of Bandits
Nicolò Cesa-Bianchi, Claudio Gentile, Giovanni Zappella A Kernel Test for Three-Variable Interactions
Dino Sejdinovic, Arthur Gretton, Wicher Bergsma A Message-Passing Algorithm for Multi-Agent Trajectory Planning
José Bento, Nate Derbinsky, Javier Alonso-Mora, Jonathan S. Yedidia A New Convex Relaxation for Tensor Completion
Bernardino Romera-Paredes, Massimiliano Pontil Adaptive Anonymity via $b$-Matching
Krzysztof M Choromanski, Tony Jebara, Kui Tang Adaptive Step-Size for Policy Gradient Methods
Matteo Pirotta, Marcello Restelli, Luca Bascetta Adaptive Submodular Maximization in Bandit Setting
Victor Gabillon, Branislav Kveton, Zheng Wen, Brian Eriksson, S. Muthukrishnan An Approximate, Efficient LP Solver for LP Rounding
Srikrishna Sridhar, Stephen Wright, Christopher Re, Ji Liu, Victor Bittorf, Ce Zhang Analyzing Hogwild Parallel Gaussian Gibbs Sampling
Matthew J Johnson, James Saunderson, Alan Willsky Annealing Between Distributions by Averaging Moments
Roger B Grosse, Chris J Maddison, Ruslan Salakhutdinov Bellman Error Based Feature Generation Using Random Projections on Sparse Spaces
Mahdi Milani Fard, Yuri Grinberg, Amir-massoud Farahmand, Joelle Pineau, Doina Precup BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables
Cho-Jui Hsieh, Matyas A Sustik, Inderjit S Dhillon, Pradeep K Ravikumar, Russell Poldrack Blind Calibration in Compressed Sensing Using Message Passing Algorithms
Christophe Schulke, Francesco Caltagirone, Florent Krzakala, Lenka Zdeborová Cluster Trees on Manifolds
Sivaraman Balakrishnan, Srivatsan Narayanan, Alessandro Rinaldo, Aarti Singh, Larry Wasserman Compete to Compute
Rupesh K Srivastava, Jonathan Masci, Sohrob Kazerounian, Faustino Gomez, Jürgen Schmidhuber Compressive Feature Learning
Hristo S Paskov, Robert West, John C Mitchell, Trevor Hastie Computing the Stationary Distribution Locally
Christina E. Lee, Asuman Ozdaglar, Devavrat Shah Conditional Random Fields via Univariate Exponential Families
Eunho Yang, Pradeep K Ravikumar, Genevera I Allen, Zhandong Liu Contrastive Learning Using Spectral Methods
James Y Zou, Daniel J. Hsu, David C. Parkes, Ryan P. Adams Convex Relaxations for Permutation Problems
Fajwel Fogel, Rodolphe Jenatton, Francis Bach, Alexandre D'Aspremont Convex Two-Layer Modeling
Özlem Aslan, Hao Cheng, Xinhua Zhang, Dale Schuurmans Decision Jungles: Compact and Rich Models for Classification
Jamie Shotton, Toby Sharp, Pushmeet Kohli, Sebastian Nowozin, John Winn, Antonio Criminisi Deep Content-Based Music Recommendation
Aaron van den Oord, Sander Dieleman, Benjamin Schrauwen Deep Neural Networks for Object Detection
Christian Szegedy, Alexander Toshev, Dumitru Erhan Demixing Odors - Fast Inference in Olfaction
Agnieszka Grabska-Barwinska, Jeff Beck, Alexandre Pouget, Peter Latham Designed Measurements for Vector Count Data
Liming Wang, David E Carlson, Miguel Rodrigues, David Wilcox, Robert Calderbank, Lawrence Carin DeViSE: A Deep Visual-Semantic Embedding Model
Andrea Frome, Greg S Corrado, Jon Shlens, Samy Bengio, Jeff Dean, Marc'Aurelio Ranzato, Tomas Mikolov Dirty Statistical Models
Eunho Yang, Pradeep K Ravikumar Distributed Exploration in Multi-Armed Bandits
Eshcar Hillel, Zohar S Karnin, Tomer Koren, Ronny Lempel, Oren Somekh Documents as Multiple Overlapping Windows into Grids of Counts
Alessandro Perina, Nebojsa Jojic, Manuele Bicego, Andrzej Truski Efficient Optimization for Sparse Gaussian Process Regression
Yanshuai Cao, Marcus A Brubaker, David J Fleet, Aaron Hertzmann Embed and Project: Discrete Sampling with Universal Hashing
Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman Estimating LASSO Risk and Noise Level
Mohsen Bayati, Murat A Erdogdu, Andrea Montanari Extracting Regions of Interest from Biological Images with Convolutional Sparse Block Coding
Marius Pachitariu, Adam M Packer, Noah Pettit, Henry Dalgleish, Michael Hausser, Maneesh Sahani Firing Rate Predictions in Optimal Balanced Networks
David G Barrett, Sophie Denève, Christian K. Machens First-Order Decomposition Trees
Nima Taghipour, Jesse Davis, Hendrik Blockeel From Bandits to Experts: A Tale of Domination and Independence
Noga Alon, Nicolò Cesa-Bianchi, Claudio Gentile, Yishay Mansour Generalized Method-of-Moments for Rank Aggregation
Hossein Azari Soufiani, William Chen, David C. Parkes, Lirong Xia Generalized Random Utility Models with Multiple Types
Hossein Azari Soufiani, Hansheng Diao, Zhenyu Lai, David C. Parkes High-Dimensional Gaussian Process Bandits
Josip Djolonga, Andreas Krause, Volkan Cevher Inferring Neural Population Dynamics from Multiple Partial Recordings of the Same Neural Circuit
Srini Turaga, Lars Buesing, Adam M Packer, Henry Dalgleish, Noah Pettit, Michael Hausser, Jakob H Macke Large Scale Distributed Sparse Precision Estimation
Huahua Wang, Arindam Banerjee, Cho-Jui Hsieh, Pradeep K Ravikumar, Inderjit S Dhillon Latent Maximum Margin Clustering
Guang-Tong Zhou, Tian Lan, Arash Vahdat, Greg Mori Latent Structured Active Learning
Wenjie Luo, Alex Schwing, Raquel Urtasun Learning Chordal Markov Networks by Constraint Satisfaction
Jukka Corander, Tomi Janhunen, Jussi Rintanen, Henrik Nyman, Johan Pensar Learning from Limited Demonstrations
Beomjoon Kim, Amir-massoud Farahmand, Joelle Pineau, Doina Precup Learning Stochastic Inverses
Andreas Stuhlmüller, Jacob Taylor, Noah Goodman Learning with Noisy Labels
Nagarajan Natarajan, Inderjit S Dhillon, Pradeep K Ravikumar, Ambuj Tewari Least Informative Dimensions
Fabian Sinz, Anna Stockl, Jan Grewe, Jan Benda Lexical and Hierarchical Topic Regression
Viet-An Nguyen, Jordan L Ying, Philip Resnik Mapping Paradigm Ontologies to and from the Brain
Yannick Schwartz, Bertrand Thirion, Gael Varoquaux Marginals-to-Models Reducibility
Tim Roughgarden, Michael Kearns Matrix Factorization with Binary Components
Martin Slawski, Matthias Hein, Pavlo Lutsik Memory Limited, Streaming PCA
Ioannis Mitliagkas, Constantine Caramanis, Prateek Jain Mixed Optimization for Smooth Functions
Mehrdad Mahdavi, Lijun Zhang, Rong Jin More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server
Qirong Ho, James Cipar, Henggang Cui, Seunghak Lee, Jin Kyu Kim, Phillip B. Gibbons, Garth A Gibson, Greg Ganger, Eric P Xing Multi-Prediction Deep Boltzmann Machines
Ian Goodfellow, Mehdi Mirza, Aaron Courville, Yoshua Bengio Multi-Task Bayesian Optimization
Kevin Swersky, Jasper Snoek, Ryan P. Adams Multiclass Total Variation Clustering
Xavier Bresson, Thomas Laurent, David Uminsky, James von Brecht Near-Optimal Entrywise Sampling for Data Matrices
Dimitris Achlioptas, Zohar S Karnin, Edo Liberty Noise-Enhanced Associative Memories
Amin Karbasi, Amir Hesam Salavati, Amin Shokrollahi, Lav R. Varshney Non-Linear Domain Adaptation with Boosting
Carlos J Becker, Christos M Christoudias, Pascal Fua On Poisson Graphical Models
Eunho Yang, Pradeep K Ravikumar, Genevera I Allen, Zhandong Liu On the Sample Complexity of Subspace Learning
Alessandro Rudi, Guillermo D Canas, Lorenzo Rosasco Online Learning with Costly Features and Labels
Navid Zolghadr, Gabor Bartok, Russell Greiner, András György, Csaba Szepesvari Online PCA for Contaminated Data
Jiashi Feng, Huan Xu, Shie Mannor, Shuicheng Yan Optimistic Concurrency Control for Distributed Unsupervised Learning
Xinghao Pan, Joseph E Gonzalez, Stefanie Jegelka, Tamara Broderick, Michael I Jordan Optimizing Instructional Policies
Robert Lindsey, Michael Mozer, William J Huggins, Harold Pashler Parametric Task Learning
Ichiro Takeuchi, Tatsuya Hongo, Masashi Sugiyama, Shinichi Nakajima Phase Retrieval Using Alternating Minimization
Praneeth Netrapalli, Prateek Jain, Sujay Sanghavi Policy Shaping: Integrating Human Feedback with Reinforcement Learning
Shane Griffith, Kaushik Subramanian, Jonathan Scholz, Charles L Isbell, Andrea L Thomaz Predicting Parameters in Deep Learning
Misha Denil, Babak Shakibi, Laurent Dinh, Marc'Aurelio Ranzato, Nando de Freitas Probabilistic Movement Primitives
Alexandros Paraschos, Christian Daniel, Jan R Peters, Gerhard Neumann Projected Natural Actor-Critic
Philip S. Thomas, William C Dabney, Stephen Giguere, Sridhar Mahadevan Real-Time Inference for a Gamma Process Model of Neural Spiking
David E Carlson, Vinayak Rao, Joshua T Vogelstein, Lawrence Carin Regret Based Robust Solutions for Uncertain Markov Decision Processes
Asrar Ahmed, Pradeep Varakantham, Yossiri Adulyasak, Patrick Jaillet Restricting Exchangeable Nonparametric Distributions
Sinead A Williamson, Steve N MacEachern, Eric P Xing Robust Bloom Filters for Large MultiLabel Classification Tasks
Moustapha M Cisse, Nicolas Usunier, Thierry Artières, Patrick Gallinari Robust Data-Driven Dynamic Programming
Grani Adiwena Hanasusanto, Daniel Huhn Robust Multimodal Graph Matching: Sparse Coding Meets Graph Matching
Marcelo Fiori, Pablo Sprechmann, Joshua Vogelstein, Pablo Muse, Guillermo Sapiro Robust Spatial Filtering with Beta Divergence
Wojciech Samek, Duncan Blythe, Klaus-Robert Müller, Motoaki Kawanabe Scalable Inference for Logistic-Normal Topic Models
Jianfei Chen, Jun Zhu, Zi Wang, Xun Zheng, Bo Zhang Scalable Kernels for Graphs with Continuous Attributes
Aasa Feragen, Niklas Kasenburg, Jens Petersen, Marleen de Bruijne, Karsten Borgwardt Sign Cauchy Projections and Chi-Square Kernel
Ping Li, Gennady Samorodnitsk, John Hopcroft Similarity Component Analysis
Soravit Changpinyo, Kuan Liu, Fei Sha Speeding up Permutation Testing in Neuroimaging
Chris Hinrichs, Vamsi K Ithapu, Qinyuan Sun, Sterling C Johnson, Vikas Singh Statistical Active Learning Algorithms
Maria-Florina F Balcan, Vitaly Feldman Streaming Variational Bayes
Tamara Broderick, Nicholas Boyd, Andre Wibisono, Ashia C Wilson, Michael I Jordan Supervised Sparse Analysis and Synthesis Operators
Pablo Sprechmann, Roee Litman, Tal Ben Yakar, Alexander M Bronstein, Guillermo Sapiro The Fast Convergence of Incremental PCA
Akshay Balsubramani, Sanjoy Dasgupta, Yoav Freund The Power of Asymmetry in Binary Hashing
Behnam Neyshabur, Nati Srebro, Ruslan Salakhutdinov, Yury Makarychev, Payman Yadollahpour The Randomized Dependence Coefficient
David Lopez-Paz, Philipp Hennig, Bernhard Schölkopf Top-Down Regularization of Deep Belief Networks
Hanlin Goh, Nicolas Thome, Matthieu Cord, Joo-Hwee Lim Transfer Learning in a Transductive Setting
Marcus Rohrbach, Sandra Ebert, Bernt Schiele Translating Embeddings for Modeling Multi-Relational Data
Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, Oksana Yakhnenko Understanding Dropout
Pierre Baldi, Peter J Sadowski Variational Planning for Graph-Based MDPs
Qiang Cheng, Qiang Liu, Feng Chen, Alex Ihler Zero-Shot Learning Through Cross-Modal Transfer
Richard Socher, Milind Ganjoo, Christopher D. Manning, Andrew Ng