default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCID23. NIPS 2010: Vancouver, British Columbia, Canada
- John D. Lafferty, Christopher K. I. Williams, John Shawe-Taylor, Richard S. Zemel, Aron Culotta:
Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, Vancouver, British Columbia, Canada. Curran Associates, Inc. 2010 - Jacob D. Abernethy, Manfred K. Warmuth:
Repeated Games against Budgeted Adversaries. 1-9 - Margareta Ackerman, Shai Ben-David, David Loker:
Towards Property-Based Classification of Clustering Paradigms. 10-18 - Ryan Prescott Adams, Zoubin Ghahramani, Michael I. Jordan:
Tree-Structured Stick Breaking for Hierarchical Data. 19-27 - Felix V. Agakov, Paul McKeigue, Jon Krohn, Amos J. Storkey:
Sparse Instrumental Variables (SPIV) for Genome-Wide Studies. 28-36 - Alekh Agarwal, Sahand N. Negahban, Martin J. Wainwright:
Fast global convergence rates of gradient methods for high-dimensional statistical recovery. 37-45 - Arvind Agarwal, Hal Daumé III, Samuel Gerber:
Learning Multiple Tasks using Manifold Regularization. 46-54 - Mauricio A. Álvarez, Jan Peters, Bernhard Schölkopf, Neil D. Lawrence:
Switched Latent Force Models for Movement Segmentation. 55-63 - Mauricio Araya-López, Olivier Buffet, Vincent Thomas, François Charpillet:
A POMDP Extension with Belief-dependent Rewards. 64-72 - Nimar S. Arora, Stuart Russell, Paul Kidwell, Erik B. Sudderth:
Global seismic monitoring as probabilistic inference. 73-81 - Joseph L. Austerweil, Thomas L. Griffiths:
Learning invariant features using the Transformed Indian Buffet Process. 82-90 - Pranjal Awasthi, Reza Bosagh Zadeh:
Supervised Clustering. 91-99 - Alper Ayvaci, Michalis Raptis, Stefano Soatto:
Occlusion Detection and Motion Estimation with Convex Optimization. 100-108 - Javad Azimi, Alan Fern, Xiaoli Z. Fern:
Batch Bayesian Optimization via Simulation Matching. 109-117 - Francis R. Bach:
Structured sparsity-inducing norms through submodular functions. 118-126 - Stephen H. Bach, Marcus A. Maloof:
A Bayesian Approach to Concept Drift. 127-135 - Chris Barber, Joseph Bockhorst, Paul Roebber:
Auto-Regressive HMM Inference with Incomplete Data for Short-Horizon Wind Forecasting. 136-144 - Mohsen Bayati, José Bento, Andrea Montanari:
The LASSO risk: asymptotic results and real world examples. 145-153 - Gowtham Bellala, Suresh K. Bhavnani, Clayton Scott:
Extensions of Generalized Binary Search to Group Identification and Exponential Costs. 154-162 - Samy Bengio, Jason Weston, David Grangier:
Label Embedding Trees for Large Multi-Class Tasks. 163-171 - Alessandro Bergamo, Lorenzo Torresani:
Exploiting weakly-labeled Web images to improve object classification: a domain adaptation approach. 181-189 - Andrey Bernstein, Shie Mannor, Nahum Shimkin:
Online Classification with Specificity Constraints. 190-198 - Alina Beygelzimer, Daniel J. Hsu, John Langford, Tong Zhang:
Agnostic Active Learning Without Constraints. 199-207 - Danny Bickson, Carlos Guestrin:
Inference with Multivariate Heavy-Tails in Linear Models. 208-216 - Jacob Bien, Ya Xu, Michael W. Mahoney:
CUR from a Sparse Optimization Viewpoint. 217-225 - Gilles Blanchard, Nicole Krämer:
Optimal learning rates for Kernel Conjugate Gradient regression. 226-234 - Matthew B. Blaschko, Andrea Vedaldi, Andrew Zisserman:
Simultaneous Object Detection and Ranking with Weak Supervision. 235-243 - Liefeng Bo, Xiaofeng Ren, Dieter Fox:
Kernel Descriptors for Visual Recognition. 244-252 - Sander M. Bohté, Jaldert O. Rombouts:
Fractionally Predictive Spiking Neurons. 253-261 - Edwin V. Bonilla, Shengbo Guo, Scott Sanner:
Gaussian Process Preference Elicitation. 262-270 - Byron Boots, Geoffrey J. Gordon:
Predictive State Temporal Difference Learning. 271-279 - Alexandre Bouchard-Côté, Michael I. Jordan:
Variational Inference over Combinatorial Spaces. 280-288 - Abdeslam Boularias, Brahim Chaib-draa:
Bootstrapping Apprenticeship Learning. 289-297 - Christos Boutsidis, Anastasios Zouzias, Petros Drineas
:
Random Projections for $k$-means Clustering. 298-306 - William Brendel, Sinisa Todorovic:
Segmentation as Maximum-Weight Independent Set. 307-315 - Matthias Broecheler, Lise Getoor:
Computing Marginal Distributions over Continuous Markov Networks for Statistical Relational Learning. 316-324 - Serhat Selcuk Bucak, Rong Jin, Anil K. Jain:
Multi-label Multiple Kernel Learning by Stochastic Approximation: Application to Visual Object Recognition. 325-333 - America Chambers, Padhraic Smyth, Mark Steyvers:
Learning concept graphs from text with stick-breaking priors. 334-342 - Kamalika Chaudhuri, Sanjoy Dasgupta:
Rates of convergence for the cluster tree. 343-351 - Anton Chechetka, Carlos Guestrin:
Evidence-Specific Structures for Rich Tractable CRFs. 352-360 - Ning Chen, Jun Zhu, Eric P. Xing:
Predictive Subspace Learning for Multi-view Data: a Large Margin Approach. 361-369 - Wei Chen, Tie-Yan Liu, Zhiming Ma:
Two-Layer Generalization Analysis for Ranking Using Rademacher Average. 370-378 - Sylvain Chevallier, Hélène Paugam-Moisy, Michèle Sebag:
SpikeAnts, a spiking neuron network modelling the emergence of organization in a complex system. 379-387 - Silvia Chiappa, Jan Peters:
Movement extraction by detecting dynamics switches and repetitions. 388-396 - Alessandro Chiuso, Gianluigi Pillonetto:
Learning sparse dynamic linear systems using stable spline kernels and exponential hyperpriors. 397-405 - Andreas Christmann, Ingo Steinwart:
Universal Kernels on Non-Standard Input Spaces. 406-414 - Tom Claassen, Tom Heskes:
Causal discovery in multiple models from different experiments. 415-423 - Shay B. Cohen, Noah A. Smith:
Empirical Risk Minimization with Approximations of Probabilistic Grammars. 424-432 - Elaine A. Corbett, Eric J. Perreault, Konrad P. Körding:
Mixture of time-warped trajectory models for movement decoding. 433-441 - Corinna Cortes, Yishay Mansour, Mehryar Mohri:
Learning Bounds for Importance Weighting. 442-450 - Koby Crammer, Daniel D. Lee:
Learning via Gaussian Herding. 451-459 - Rémi Cuingnet, Marie Chupin, Habib Benali, Olivier Colliot:
Spatial and anatomical regularization of SVM for brain image analysis. 460-468 - George E. Dahl, Marc'Aurelio Ranzato, Abdel-rahman Mohamed, Geoffrey E. Hinton:
Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine. 469-477 - Aman Dhesi, Purushottam Kar:
Random Projection Trees Revisited. 496-504 - Uwe Dick, Peter Haider, Thomas Vanck, Michael Brückner, Tobias Scheffer:
Throttling Poisson Processes. 505-513 - Nan Ding, S. V. N. Vishwanathan:
t-logistic regression. 514-522 - Justin Domke:
Implicit Differentiation by Perturbation. 523-531 - Finale Doshi-Velez, David Wingate, Nicholas Roy, Joshua B. Tenenbaum:
Nonparametric Bayesian Policy Priors for Reinforcement Learning. 532-540 - Shaul Druckmann, Dmitri B. Chklovskii:
Over-complete representations on recurrent neural networks can support persistent percepts. 541-549 - John C. Duchi, Alekh Agarwal, Martin J. Wainwright:
Distributed Dual Averaging In Networks. 550-558 - Gal Elidan:
Copula Bayesian Networks. 559-567 - Amir Massoud Farahmand, Rémi Munos, Csaba Szepesvári:
Error Propagation for Approximate Policy and Value Iteration. 568-576 - Mahdi Milani Fard, Joelle Pineau:
PAC-Bayesian Model Selection for Reinforcement Learning. 1624-1632 - Alan Fern, Prasad Tadepalli:
A Computational Decision Theory for Interactive Assistants. 577-585 - Sarah Filippi, Olivier Cappé, Aurélien Garivier, Csaba Szepesvári:
Parametric Bandits: The Generalized Linear Case. 586-594 - Nicholas Fisher, Arunava Banerjee:
A Novel Kernel for Learning a Neuron Model from Spike Train Data. 595-603 - Rina Foygel, Mathias Drton:
Extended Bayesian Information Criteria for Gaussian Graphical Models. 604-612 - Bela A. Frigyik, Maya R. Gupta, Yihua Chen:
Shadow Dirichlet for Restricted Probability Modeling. 613-621 - Mario Fritz, Kate Saenko, Trevor Darrell:
Size Matters: Metric Visual Search Constraints from Monocular Metadata. 622-630 - Vicky Froyen, Jacob Feldman, Manish Singh:
A Bayesian Framework for Figure-Ground Interpretation. 631-639 - C. C. Alan Fung, K. Y. Michael Wong, He Wang, Si Wu:
Attractor Dynamics with Synaptic Depression. 640-648 - Kun Gai, Guangyun Chen, Changshui Zhang:
Learning Kernels with Radiuses of Minimum Enclosing Balls. 649-657 - Surya Ganguli, Haim Sompolinsky:
Short-term memory in neuronal networks through dynamical compressed sensing. 667-675 - Deep Ganguli, Eero P. Simoncelli:
Implicit encoding of prior probabilities in optimal neural populations. 658-666 - Pierre Garrigues, Bruno A. Olshausen:
Group Sparse Coding with a Laplacian Scale Mixture Prior. 676-684 - Jan Gasthaus, Yee Whye Teh:
Improvements to the Sequence Memoizer. 685-693 - Andrew Gelfand, Yutian Chen, Laurens van der Maaten, Max Welling:
On Herding and the Perceptron Cycling Theorem. 694-702 - Felipe Gerhard, Wulfram Gerstner:
Rescaling, thinning or complementing? On goodness-of-fit procedures for point process models and Generalized Linear Models. 703-711 - Samuel Gershman, Robert Wilson:
The Neural Costs of Optimal Control. 712-720 - Mohammad Ghavamzadeh, Alessandro Lazaric, Odalric-Ambrym Maillard, Rémi Munos:
LSTD with Random Projections. 721-729 - Bryan R. Gibson, Xiaojin Zhu, Timothy T. Rogers, Chuck Kalish, Joseph Harrison:
Humans Learn Using Manifolds, Reluctantly. 730-738 - Tobias Glasmachers:
Universal Consistency of Multi-Class Support Vector Classification. 739-747 - Vibhav Gogate, William Austin Webb, Pedro M. Domingos:
Learning Efficient Markov Networks. 748-756 - Andrew B. Goldberg, Xiaojin Zhu, Ben Recht, Jun-Ming Xu, Robert D. Nowak:
Transduction with Matrix Completion: Three Birds with One Stone. 757-765 - Daniel Golovin, Andreas Krause, Debajyoti Ray:
Near-Optimal Bayesian Active Learning with Noisy Observations. 766-774 - Ryan Gomes, Andreas Krause, Pietro Perona:
Discriminative Clustering by Regularized Information Maximization. 775-783 - Dan F. M. Goodman, Romain Brette:
Learning to localise sounds with spiking neural networks. 784-792 - David Grangier, Iain Melvin:
Feature Set Embedding for Incomplete Data. 793-801 - Yuhong Guo:
Active Instance Sampling via Matrix Partition. 802-810 - Yanjun Han, Qing Tao, Jue Wang:
Avoiding False Positive in Multi-Instance Learning. 811-819 - Lauren Hannah, Warren B. Powell, David M. Blei:
Nonparametric Density Estimation for Stochastic Optimization with an Observable State Variable. 820-828 - Stefan Harmeling, Michael Hirsch, Bernhard Schölkopf:
Space-Variant Single-Image Blind Deconvolution for Removing Camera Shake. 829-837 - Hado van Hasselt:
Double Q-learning. 2613-2621 - Tamir Hazan, Raquel Urtasun:
A Primal-Dual Message-Passing Algorithm for Approximated Large Scale Structured Prediction. 838-846 - Matthias Hein, Thomas Bühler:
An Inverse Power Method for Nonlinear Eigenproblems with Applications in 1-Spectral Clustering and Sparse PCA. 847-855 - Matthew D. Hoffman, David M. Blei, Francis R. Bach:
Online Learning for Latent Dirichlet Allocation. 856-864 - Diane Hu, Laurens van der Maaten, Youngmin Cho, Lawrence K. Saul, Sorin Lerner:
Latent Variable Models for Predicting File Dependencies in Large-Scale Software Development. 865-873 - Ling Huang, Jinzhu Jia, Bin Yu, Byung-Gon Chun, Petros Maniatis, Mayur Naik:
Predicting Execution Time of Computer Programs Using Sparse Polynomial Regression. 883-891 - Sheng-Jun Huang, Rong Jin, Zhi-Hua Zhou:
Active Learning by Querying Informative and Representative Examples. 892-900 - Jim C. Huang, Nebojsa Jojic, Christopher Meek:
Exact inference and learning for cumulative distribution functions on loopy graphs. 874-882 - Dirk Husmeier, Frank Dondelinger, Sophie Lèbre:
Inter-time segment information sharing for non-homogeneous dynamic Bayesian networks. 901-909 - Hal Daumé III, Abhishek Kumar, Avishek Saha:
Co-regularization Based Semi-supervised Domain Adaptation. 478-486 - Guy Isley, Christopher J. Hillar, Friedrich T. Sommer:
Deciphering subsampled data: adaptive compressive sampling as a principle of brain communication. 910-918 - Katsuhiko Ishiguro, Tomoharu Iwata, Naonori Ueda, Joshua B. Tenenbaum:
Dynamic Infinite Relational Model for Time-varying Relational Data Analysis. 919-927 - Prateek Jain, Brian Kulis, Inderjit S. Dhillon:
Inductive Regularized Learning of Kernel Functions. 946-954 - Prateek Jain, Sudheendra Vijayanarasimhan, Kristen Grauman:
Hashing Hyperplane Queries to Near Points with Applications to Large-Scale Active Learning. 928-936 - Prateek Jain, Raghu Meka, Inderjit S. Dhillon:
Guaranteed Rank Minimization via Singular Value Projection. 937-945 - Ali Jalali, Pradeep Ravikumar, Sujay Sanghavi, Chao Ruan:
A Dirty Model for Multi-task Learning. 964-972 - Abhay Kumar Jha, Vibhav Gogate, Alexandra Meliou, Dan Suciu:
Lifted Inference Seen from the Other Side : The Tractable Features. 973-981 - Yangqing Jia, Mathieu Salzmann, Trevor Darrell:
Factorized Latent Spaces with Structured Sparsity. 982-990 - Albert Xin Jiang, Kevin Leyton-Brown:
Bayesian Action-Graph Games. 991-999 - Jie Tang, Pieter Abbeel:
On a Connection between Importance Sampling and the Likelihood Ratio Policy Gradient. 1000-1008 - Jeffrey Johns, Christopher Painter-Wakefield, Ronald Parr:
Linear Complementarity for Regularized Policy Evaluation and Improvement. 1009-1017 - Mark Johnson, Katherine Demuth, Michael C. Frank, Bevan K. Jones:
Synergies in learning words and their referents. 1018-1026 - Nebojsa Jojic, Alessandro Perina, Vittorio Murino:
Structural epitome: a way to summarize one's visual experience. 1027-1035 - Peter B. Jones, Venkatesh Saligrama, Sanjoy K. Mitter:
Probabilistic Belief Revision with Structural Constraints. 1036-1044 - Armand Joulin, Francis R. Bach, Jean Ponce:
Efficient Optimization for Discriminative Latent Class Models. 1045-1053 - Satyen Kale, Lev Reyzin, Robert E. Schapire:
Non-Stochastic Bandit Slate Problems. 1054-1062 - Nikos Karampatziakis:
Static Analysis of Binary Executables Using Structural SVMs. 1063-1071 - Leonid Karlinsky, Michael Dinerstein, Shimon Ullman:
Using body-anchored priors for identifying actions in single images. 1072-1080 - Kentaro Katahira, Kazuo Okanoya, Masato Okada:
Effects of Synaptic Weight Diffusion on Learning in Decision Making Networks. 1081-1089 - Koray Kavukcuoglu, Pierre Sermanet, Y-Lan Boureau, Karol Gregor, Michaël Mathieu, Yann LeCun:
Learning Convolutional Feature Hierarchies for Visual Recognition. 1090-1098 - Ryan C. Kelly, Matthew A. Smith, Robert E. Kass, Tai Sing Lee:
Accounting for network effects in neuronal responses using L1 regularized point process models. 1099-1107