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ORCID22. NIPS 2009: Vancouver, British Columbia, Canada
- Yoshua Bengio, Dale Schuurmans, John D. Lafferty, Christopher K. I. Williams, Aron Culotta:
Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, Vancouver, British Columbia, Canada. Curran Associates, Inc. 2009, ISBN 9781615679119 - Alekh Agarwal, Peter L. Bartlett, Pradeep Ravikumar, Martin J. Wainwright:
Information-theoretic lower bounds on the oracle complexity of convex optimization. 1-9 - Nir Ailon, Ragesh Jaiswal, Claire Monteleoni:
Streaming k-means approximation. 10-18 - Martin Allen, Shlomo Zilberstein:
Complexity of Decentralized Control: Special Cases. 19-27 - Massih-Reza Amini, Nicolas Usunier, Cyril Goutte:
Learning from Multiple Partially Observed Views - an Application to Multilingual Text Categorization. 28-36 - Roy Anati, Kostas Daniilidis:
Constructing Topological Maps using Markov Random Fields and Loop-Closure Detection. 37-45 - Sylvain Arlot, Francis R. Bach:
Data-driven calibration of linear estimators with minimal penalties. 46-54 - Raman Arora:
On Learning Rotations. 55-63 - Bing Bai, Jason Weston, David Grangier, Ronan Collobert, Kunihiko Sadamasa, Yanjun Qi, Corinna Cortes, Mehryar Mohri:
Polynomial Semantic Indexing. 64-72 - Cosmin Adrian Bejan, Matthew Titsworth, Andrew Hickl, Sanda M. Harabagiu:
Nonparametric Bayesian Models for Unsupervised Event Coreference Resolution. 73-81 - Samy Bengio, Fernando C. N. Pereira, Yoram Singer, Dennis Strelow:
Group Sparse Coding. 82-89 - Philipp Berens, Sebastian Gerwinn, Alexander S. Ecker, Matthias Bethge:
Neurometric function analysis of population codes. 90-98 - James Bergstra, Yoshua Bengio:
Slow, Decorrelated Features for Pretraining Complex Cell-like Networks. 99-107 - Pietro Berkes
, Ben White, József Fiser:
No evidence for active sparsification in the visual cortex. 108-116 - Wei Bian, Dacheng Tao:
Manifold Regularization for SIR with Rate Root-n Convergence. 117-125 - Matthew B. Blaschko, Jacquelyn A. Shelton, Andreas M. Bartels:
Augmenting Feature-driven fMRI Analyses: Semi-supervised learning and resting state activity. 126-134 - Liefeng Bo, Cristian Sminchisescu:
Efficient Match Kernel between Sets of Features for Visual Recognition. 135-143 - Alexandre Bouchard-Côté, Slav Petrov, Dan Klein:
Randomized Pruning: Efficiently Calculating Expectations in Large Dynamic Programs. 144-152 - Christos Boutsidis, Michael W. Mahoney, Petros Drineas:
Unsupervised Feature Selection for the $k$-means Clustering Problem. 153-161 - Jake V. Bouvrie, Lorenzo Rosasco, Tomaso A. Poggio:
On Invariance in Hierarchical Models. 162-170 - Romain Brasselet, Roland S. Johansson, Angelo Arleo:
Optimal context separation of spiking haptic signals by second-order somatosensory neurons. 180-188 - Michael Brückner, Tobias Scheffer:
Nash Equilibria of Static Prediction Games. 171-179 - Samuel Rota Bulò, Marcello Pelillo:
A Game-Theoretic Approach to Hypergraph Clustering. 1571-1579 - Keith Bush, Joelle Pineau:
Manifold Embeddings for Model-Based Reinforcement Learning under Partial Observability. 189-197 - Chenghui Cai, Xuejun Liao, Lawrence Carin:
Learning to Explore and Exploit in POMDPs. 198-206 - William M. Campbell, Zahi N. Karam, Douglas E. Sturim:
Speaker Comparison with Inner Product Discriminant Functions. 207-215 - Peter Carbonetto, Matthew King, Firas Hamze:
A Stochastic approximation method for inference in probabilistic graphical models. 216-224 - Francois Caron, Arnaud Doucet:
Bayesian Nonparametric Models on Decomposable Graphs. 225-233 - Daniel R. Cavagnaro, Mark A. Pitt, Jay I. Myung:
Adaptive Design Optimization in Experiments with People. 234-242 - Lawrence Cayton:
Efficient Bregman Range Search. 243-251 - Guillermo A. Cecchi, Irina Rish, Benjamin Thyreau, Bertrand Thirion, Marion Plaze, Marie-Laure Paillère-Martinot, Catherine Martelli, Jean-Luc Martinot, Jean-Baptiste Poline:
Discriminative Network Models of Schizophrenia. 252-260 - Volkan Cevher:
Learning with Compressible Priors. 261-269 - Kian Ming Adam Chai:
Generalization Errors and Learning Curves for Regression with Multi-task Gaussian Processes. 279-287 - Barry Chai, Dirk B. Walther, Diane M. Beck, Li Fei-Fei:
Exploring Functional Connectivities of the Human Brain using Multivariate Information Analysis. 270-278 - Jonathan D. Chang, Jordan L. Boyd-Graber, Sean Gerrish, Chong Wang, David M. Blei:
Reading Tea Leaves: How Humans Interpret Topic Models. 288-296 - Kamalika Chaudhuri, Yoav Freund, Daniel J. Hsu:
A Parameter-free Hedging Algorithm. 297-305 - Gal Chechik, Uri Shalit, Varun Sharma, Samy Bengio:
An Online Algorithm for Large Scale Image Similarity Learning. 306-314 - Ye Chen, Michael Kapralov, Dmitry Pavlov, John F. Canny:
Factor Modeling for Advertisement Targeting. 324-332 - Wei Chen, Tie-Yan Liu, Yanyan Lan, Zhiming Ma, Hang Li:
Ranking Measures and Loss Functions in Learning to Rank. 315-323 - Tat-Jun Chin, Hanzi Wang, David Suter:
The Ordered Residual Kernel for Robust Motion Subspace Clustering. 333-341 - Youngmin Cho, Lawrence K. Saul:
Kernel Methods for Deep Learning. 342-350 - Arthur Choi, Adnan Darwiche:
Approximating MAP by Compensating for Structural Relaxations. 351-359 - Stéphan Clémençon, Nicolas Vayatis, Marine Depecker:
AUC optimization and the two-sample problem. 360-368 - Ruben Coen Cagli, Peter Dayan, Odelia Schwartz:
Statistical Models of Linear and Nonlinear Contextual Interactions in Early Visual Processing. 369-377 - Bryan R. Conroy, Benjamin D. Singer, James V. Haxby, Peter J. Ramadge:
fMRI-Based Inter-Subject Cortical Alignment Using Functional Connectivity. 378-386 - Pierre-Arnaud Coquelin, Romain Deguest, Rémi Munos:
Sensitivity analysis in HMMs with application to likelihood maximization. 387-395 - Corinna Cortes, Mehryar Mohri, Afshin Rostamizadeh:
Learning Non-Linear Combinations of Kernels. 396-404 - Aaron C. Courville, Douglas Eck, Yoshua Bengio:
An Infinite Factor Model Hierarchy Via a Noisy-Or Mechanism. 405-413 - Koby Crammer, Alex Kulesza, Mark Dredze:
Adaptive Regularization of Weight Vectors. 414-422 - Benjamin J. Culpepper, Bruno A. Olshausen:
Learning transport operators for image manifolds. 423-431 - Marco Cuturi, Jean-Philippe Vert, Alexandre d'Aspremont:
White Functionals for Anomaly Detection in Dynamical Systems. 432-440 - Arnak S. Dalalyan, Renaud Keriven:
L1-Penalized Robust Estimation for a Class of Inverse Problems Arising in Multiview Geometry. 441-449 - Ofer Dekel:
Distribution-Calibrated Hierarchical Classification. 450-458 - Liam Mac Dermed, Charles L. Isbell Jr.:
Solving Stochastic Games. 1186-1194 - Vijay V. Desai, Vivek F. Farias, Ciamac Cyrus Moallemi:
A Smoothed Approximate Linear Program. 459-467 - Laura Dietz, Valentin Dallmeier, Andreas Zeller, Tobias Scheffer:
Localizing Bugs in Program Executions with Graphical Models. 468-476 - Finale Doshi-Velez:
The Infinite Partially Observable Markov Decision Process. 477-485 - Lan Du, Lu Ren, David B. Dunson, Lawrence Carin:
A Bayesian Model for Simultaneous Image Clustering, Annotation and Object Segmentation. 486-494 - John C. Duchi, Yoram Singer:
Efficient Learning using Forward-Backward Splitting. 495-503 - Benjamin Van Durme, Ashwin Lall:
Streaming Pointwise Mutual Information. 1892-1900 - Vivek F. Farias, Srikanth Jagabathula, Devavrat Shah:
A Data-Driven Approach to Modeling Choice. 504-512 - Siamac Fazli, Cristian Grozea, Márton Danóczy, Benjamin Blankertz, Florin Popescu, Klaus-Robert Müller:
Subject independent EEG-based BCI decoding. 513-521 - Rob Fergus, Yair Weiss, Antonio Torralba:
Semi-Supervised Learning in Gigantic Image Collections. 522-530 - Sanja Fidler, Marko Boben, Ales Leonardis:
Evaluating multi-class learning strategies in a generative hierarchical framework for object detection. 531-539 - Alyson K. Fletcher, Sundeep Rangan:
Orthogonal Matching Pursuit From Noisy Random Measurements: A New Analysis. 540-548 - Emily B. Fox, Erik B. Sudderth, Michael I. Jordan, Alan S. Willsky:
Sharing Features among Dynamical Systems with Beta Processes. 549-557 - Mario Fritz, Michael J. Black, Gary R. Bradski, Sergey Karayev, Trevor Darrell:
An Additive Latent Feature Model for Transparent Object Recognition. 558-566 - Menachem Fromer, Amir Globerson:
An LP View of the M-best MAP problem. 567-575 - Yusuke Fujiwara, Yoichi Miyawaki, Yukiyasu Kamitani:
Estimating image bases for visual image reconstruction from human brain activity. 576-584 - Jing Gao, Feng Liang, Wei Fan, Yizhou Sun, Jiawei Han:
Graph-based Consensus Maximization among Multiple Supervised and Unsupervised Models. 585-593 - Eric K. Garcia, Maya R. Gupta:
Lattice Regression. 594-602 - Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand, Sara Shanian:
From PAC-Bayes Bounds to KL Regularization. 603-610 - Samuel Gershman, Ed Vul, Joshua B. Tenenbaum:
Perceptual Multistability as Markov Chain Monte Carlo Inference. 611-619 - Marcel van Gerven, Botond Cseke, Robert Oostenveld, Tom Heskes:
Bayesian Source Localization with the Multivariate Laplace Prior. 1901-1909 - Sebastian Gerwinn, Philipp Berens, Matthias Bethge:
A joint maximum-entropy model for binary neural population patterns and continuous signals. 620-628 - Sennay Ghebreab, H. Steven Scholte, Victor A. F. Lamme, Arnold W. M. Smeulders:
A Biologically Plausible Model for Rapid Natural Scene Identification. 629-637 - Jacob Goldberger, Amir Leshem:
A Gaussian Tree Approximation for Integer Least-Squares. 638-645 - Ian J. Goodfellow, Quoc V. Le, Andrew M. Saxe, Honglak Lee, Andrew Y. Ng:
Measuring Invariances in Deep Networks. 646-654 - Stephen Gould, Tianshi Gao, Daphne Koller:
Region-based Segmentation and Object Detection. 655-663 - João Graça, Kuzman Ganchev, Ben Taskar, Fernando C. N. Pereira:
Posterior vs Parameter Sparsity in Latent Variable Models. 664-672 - Arthur Gretton, Kenji Fukumizu, Zaïd Harchaoui, Bharath K. Sriperumbudur:
A Fast, Consistent Kernel Two-Sample Test. 673-681 - Marco Grzegorczyk, Dirk Husmeier:
Non-stationary continuous dynamic Bayesian networks. 682-690 - Andrew Guillory, Jeff A. Bilmes:
Label Selection on Graphs. 691-699 - Elad Hazan, Satyen Kale:
Beyond Convexity: Online Submodular Minimization. 700-708 - Elad Hazan, Satyen Kale:
On Stochastic and Worst-case Models for Investing. 709-717 - Matthias Hein:
Robust Nonparametric Regression with Metric-Space Valued Output. 718-726 - Katherine A. Heller, Adam Sanborn, Nick Chater:
Hierarchical Learning of Dimensional Biases in Human Categorization. 727-735 - Ricardo Henao, Ole Winther:
Bayesian Sparse Factor Models and DAGs Inference and Comparison. 736-744 - Jean Honorio, Luis E. Ortiz, Dimitris Samaras, Nikos Paragios, Rita Z. Goldstein:
Sparse and Locally Constant Gaussian Graphical Models. 745-753 - Chun-Nan Hsu, Yu-Ming Chang, Han-Shen Huang, Yuh-Jye Lee:
Periodic Step Size Adaptation for Single Pass On-line Learning. 763-771 - Daniel J. Hsu, Sham M. Kakade, John Langford, Tong Zhang:
Multi-Label Prediction via Compressed Sensing. 772-780 - Anne S. Hsu, Thomas L. Griffiths:
Differential Use of Implicit Negative Evidence in Generative and Discriminative Language Learning. 754-762 - Chonghai Hu, James T. Kwok, Weike Pan:
Accelerated Gradient Methods for Stochastic Optimization and Online Learning. 781-789 - Tao Hu, Anthony M. Leonardo, Dmitri B. Chklovskii:
Reconstruction of Sparse Circuits Using Multi-neuronal Excitation (RESCUME). 790-798 - Jonathan Huang, Carlos Guestrin:
Riffled Independence for Ranked Data. 799-807 - Shuai Huang, Jing Li, Liang Sun, Jun Liu, Teresa Wu, Kewei Chen, Adam Fleisher, Eric Reiman, Jieping Ye:
Learning Brain Connectivity of Alzheimer's Disease from Neuroimaging Data. 808-816 - Marcus Hutter:
Discrete MDL Predicts in Total Variation. 817-825 - Alexander Ihler, Andrew J. Frank, Padhraic Smyth:
Particle-based Variational Inference for Continuous Systems. 826-834 - Tomoharu Iwata, Takeshi Yamada, Naonori Ueda:
Modeling Social Annotation Data with Content Relevance using a Topic Model. 835-843 - Jagarlapudi Saketha Nath, G. Dinesh, Raman Sankaran, Chiranjib Bhattacharyya, Aharon Ben-Tal, K. R. Ramakrishnan:
On the Algorithmics and Applications of a Mixed-norm based Kernel Learning Formulation. 844-852 - Alan Jern, Kai-min Chang, Charles Kemp:
Bayesian Belief Polarization. 853-861 - Rong Jin, Shijun Wang, Yang Zhou:
Regularized Distance Metric Learning: Theory and Algorithm. 862-870 - Kyomin Jung, Pushmeet Kohli, Devavrat Shah:
Local Rules for Global MAP: When Do They Work ? 871-879 - Adam Kalai, Varun Kanade:
Potential-Based Agnostic Boosting. 880-888 - Yi-Hao Kao, Benjamin Van Roy, Xiang Yan:
Directed Regression. 889-897 - Ashish Kapoor, Eric Horvitz:
Breaking Boundaries Between Induction Time and Diagnosis Time Active Information Acquisition. 898-906 - Masayuki Karasuyama, Ichiro Takeuchi:
Multiple Incremental Decremental Learning of Support Vector Machines. 907-915 - Yoshinobu Kawahara, Kiyohito Nagano, Koji Tsuda, Jeff A. Bilmes:
Submodularity Cuts and Applications. 916-924 - Charles Kemp, Alan Jern, Fei Xu:
Individuation, Identification and Object Discovery. 925-933 - Charles Kemp, Alan Jern:
Abstraction and Relational learning. 934-942 - Charles Kemp:
Quantification and the language of thought. 943-951 - Raghunandan H. Keshavan, Andrea Montanari, Sewoong Oh:
Matrix Completion from Noisy Entries. 952-960 - Kwang In Kim, Florian Steinke, Matthias Hein:
Semi-supervised Regression using Hessian energy with an application to semi-supervised dimensionality reduction. 979-987 - Gunhee Kim, Antonio Torralba:
Unsupervised Detection of Regions of Interest Using Iterative Link Analysis. 961-969 - Jong Kyoung Kim, Seungjin Choi:
Clustering sequence sets for motif discovery. 970-978 - Stefan Klampfl, Wolfgang Maass:
Replacing supervised classification learning by Slow Feature Analysis in spiking neural networks. 988-996 - Marius Kloft, Ulf Brefeld, Sören Sonnenburg, Pavel Laskov, Klaus-Robert Müller, Alexander Zien:
Efficient and Accurate Lp-Norm Multiple Kernel Learning. 997-1005 - Mladen Kolar, Le Song, Eric P. Xing:
Sparsistent Learning of Varying-coefficient Models with Structural Changes. 1006-1014 - George Dimitri Konidaris, Andrew G. Barto:
Skill Discovery in Continuous Reinforcement Learning Domains using Skill Chaining. 1015-1023 - Samory Kpotufe:
Fast, smooth and adaptive regression in metric spaces. 1024-1032 - Dilip Krishnan, Rob Fergus:
Fast Image Deconvolution using Hyper-Laplacian Priors. 1033-1041 - Brian Kulis, Trevor Darrell:
Learning to Hash with Binary Reconstructive Embeddings. 1042-1050