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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 - M. Pawan Kumar, Daphne Koller:
Learning a Small Mixture of Trees. 1051-1059 - Sanjiv Kumar, Mehryar Mohri, Ameet Talwalkar:
Ensemble Nystrom Method. 1060-1068 - Marc Lanctot, Kevin Waugh, Martin Zinkevich, Michael H. Bowling:
Monte Carlo Sampling for Regret Minimization in Extensive Games. 1078-1086 - Miguel Lázaro-Gredilla, Aníbal R. Figueiras-Vidal:
Inter-domain Gaussian Processes for Sparse Inference using Inducing Features. 1087-1095 - Honglak Lee, Peter T. Pham, Yan Largman, Andrew Y. Ng:
Unsupervised feature learning for audio classification using convolutional deep belief networks. 1096-1104 - Robert Legenstein, Steven M. Chase, Andrew B. Schwartz, Wolfgang Maass:
Functional network reorganization in motor cortex can be explained by reward-modulated Hebbian learning. 1105-1113 - Marius Leordeanu, Martial Hebert, Rahul Sukthankar:
An Integer Projected Fixed Point Method for Graph Matching and MAP Inference. 1114-1122 - Wu-Jun Li, Dit-Yan Yeung, Zhihua Zhang:
Probabilistic Relational PCA. 1123-1131 - Percy Liang, Francis R. Bach, Guillaume Bouchard, Michael I. Jordan:
Asymptotically Optimal Regularization in Smooth Parametric Models. 1132-1140 - Han Liu, Xi Chen:
Nonparametric Greedy Algorithms for the Sparse Learning Problem. 1141-1149 - Aurélie C. Lozano, Grzegorz Swirszcz, Naoki Abe:
Grouped Orthogonal Matching Pursuit for Variable Selection and Prediction. 1150-1158 - Hongjing Lu, Matthew Weiden, Alan L. Yuille:
Modeling the spacing effect in sequential category learning. 1159-1167 - Jie Luo, Barbara Caputo, Vittorio Ferrari:
Who's Doing What: Joint Modeling of Names and Verbs for Simultaneous Face and Pose Annotation. 1168-1176 - Jaakko Luttinen, Alexander Ilin:
Variational Gaussian-process factor analysis for modeling spatio-temporal data. 1177-1185 - Jörg Lücke, Richard E. Turner, Maneesh Sahani, Marc Henniges:
Occlusive Components Analysis. 1069-1077 - Jakob H. Macke, Sebastian Gerwinn, Leonard E. White, Matthias Kaschube, Matthias Bethge:
Bayesian estimation of orientation preference maps. 1195-1203 - Hamid Reza Maei, Csaba Szepesvári, Shalabh Bhatnagar, Doina Precup, David Silver, Richard S. Sutton:
Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation. 1204-1212 - Odalric-Ambrym Maillard, Rémi Munos:
Compressed Least-Squares Regression. 1213-1221 - Tomasz Malisiewicz, Alexei A. Efros:
Beyond Categories: The Visual Memex Model for Reasoning About Object Relationships. 1222-1230 - Gideon Mann, Ryan T. McDonald, Mehryar Mohri, Nathan Silberman, Dan Walker:
Efficient Large-Scale Distributed Training of Conditional Maximum Entropy Models. 1231-1239 - Dimitris Margaritis:
Toward Provably Correct Feature Selection in Arbitrary Domains. 1240-1248 - Andrew McCallum, Karl Schultz, Sameer Singh:
FACTORIE: Probabilistic Programming via Imperatively Defined Factor Graphs. 1249-1257 - Raghu Meka, Prateek Jain, Inderjit S. Dhillon:
Matrix Completion from Power-Law Distributed Samples. 1258-1266 - Yicong Meng, Bertram E. Shi:
Extending Phase Mechanism to Differential Motion Opponency for Motion Pop-out. 1267-1275 - Kurt T. Miller, Thomas L. Griffiths, Michael I. Jordan:
Nonparametric Latent Feature Models for Link Prediction. 1276-1284 - Baback Moghaddam, Benjamin M. Marlin, Mohammad Emtiyaz Khan, Kevin P. Murphy:
Accelerating Bayesian Structural Inference for Non-Decomposable Gaussian Graphical Models. 1285-1293 - Finale Doshi-Velez, David A. Knowles, Shakir Mohamed, Zoubin Ghahramani:
Large Scale Nonparametric Bayesian Inference: Data Parallelisation in the Indian Buffet Process. 1294-1302 - Andrea Montanari, Jose Ayres Pereira:
Which graphical models are difficult to learn? 1303-1311 - Tetsuro Morimura, Eiji Uchibe, Junichiro Yoshimoto, Kenji Doya:
A Generalized Natural Actor-Critic Algorithm. 1312-1320 - Michael Mozer, Harold Pashler, Nicholas Cepeda, Robert V. Lindsey, Ed Vul:
Predicting the Optimal Spacing of Study: A Multiscale Context Model of Memory. 1321-1329 - Boaz Nadler, Nathan Srebro, Xueyuan Zhou:
Statistical Analysis of Semi-Supervised Learning: The Limit of Infinite Unlabelled Data. 1330-1338 - Vinod Nair, Geoffrey E. Hinton:
3D Object Recognition with Deep Belief Nets. 1339-1347 - Sahand N. Negahban, Pradeep Ravikumar, Martin J. Wainwright, Bin Yu:
A unified framework for high-dimensional analysis of $M$-estimators with decomposable regularizers. 1348-1356 - Bernhard Nessler, Michael Pfeiffer, Wolfgang Maass:
STDP enables spiking neurons to detect hidden causes of their inputs. 1357-1365 - Robert D. Nowak:
Noisy Generalized Binary Search. 1366-1374 - Arno Onken, Steffen Grünewälder, Klaus Obermayer:
Correlation Coefficients are Insufficient for Analyzing Spike Count Dependencies. 1383-1391 - Peter Orbanz:
Construction of Nonparametric Bayesian Models from Parametric Bayes Equations. 1392-1400 - Tom Y. Ouyang, Randall Davis:
Learning from Neighboring Strokes: Combining Appearance and Context for Multi-Domain Sketch Recognition. 1401-1409 - Arkadas Ozakin, Alexander G. Gray:
Submanifold density estimation. 1375-1382 - Mark Palatucci, Dean Pomerleau, Geoffrey E. Hinton, Tom M. Mitchell:
Zero-shot Learning with Semantic Output Codes. 1410-1418 - Jian Peng, Liefeng Bo, Jinbo Xu:
Conditional Neural Fields. 1419-1427 - Alessandro Perina, Marco Cristani, Umberto Castellani, Vittorio Murino, Nebojsa Jojic:
Free energy score space. 1428-1436 - Theodore J. Perkins:
Maximum likelihood trajectories for continuous-time Markov chains. 1437-1445 - Marek Petrik, Shlomo Zilberstein:
Robust Value Function Approximation Using Bilinear Programming. 1446-1454 - James Petterson, Tibério S. Caetano, Julian J. McAuley, Jin Yu:
Exponential Family Graph Matching and Ranking. 1455-1463 - Jean-Pascal Pfister, Peter Dayan, Máté Lengyel:
Know Thy Neighbour: A Normative Theory of Synaptic Depression. 1464-1472 - Jonathan W. Pillow:
Time-rescaling methods for the estimation and assessment of non-Poisson neural encoding models. 1473-1481 - Hamed Pirsiavash, Deva Ramanan, Charless C. Fowlkes:
Bilinear classifiers for visual recognition. 1482-1490 - Novi Quadrianto, James Petterson, Alexander J. Smola:
Distribution Matching for Transduction. 1500-1508 - Novi Quadrianto, Tibério S. Caetano, John Lim, Dale Schuurmans:
Convex Relaxation of Mixture Regression with Efficient Algorithms. 1491-1499 - Maxim Raginsky, Svetlana Lazebnik:
Locality-sensitive binary codes from shift-invariant kernels. 1509-1517 - Piyush Rai, Hal Daumé III:
Multi-Label Prediction via Sparse Infinite CCA. 1518-1526 - Parikshit Ram, Dongryeol Lee, Hua Ouyang, Alexander G. Gray:
Rank-Approximate Nearest Neighbor Search: Retaining Meaning and Speed in High Dimensions. 1536-1544 - Parikshit Ram, Dongryeol Lee, William B. March, Alexander G. Gray:
Linear-time Algorithms for Pairwise Statistical Problems. 1527-1535 - Sundeep Rangan, Alyson K. Fletcher, Vivek K. Goyal:
Asymptotic Analysis of MAP Estimation via the Replica Method and Compressed Sensing. 1545-1553 - Vinayak A. Rao, Yee Whye Teh:
Spatial Normalized Gamma Processes. 1554-1562 - Garvesh Raskutti, Martin J. Wainwright, Bin Yu:
Lower bounds on minimax rates for nonparametric regression with additive sparsity and smoothness. 1563-1570 - Bryan C. Russell, Alexei A. Efros, Josef Sivic, Bill Freeman, Andrew Zisserman:
Segmenting Scenes by Matching Image Composites. 1580-1588 - Kate Saenko, Trevor Darrell:
Filtering Abstract Senses From Image Search Results. 1589-1597 - Ruslan Salakhutdinov, Geoffrey E. Hinton:
Replicated Softmax: an Undirected Topic Model. 1607-1614 - Ruslan Salakhutdinov:
Learning in Markov Random Fields using Tempered Transitions. 1598-1606 - Joseph Schlecht, Kobus Barnard:
Learning models of object structure. 1615-1623 - Mikkel N. Schmidt:
Linearly constrained Bayesian matrix factorization for blind source separation. 1624-1632 - Matthias W. Seeger:
Speeding up Magnetic Resonance Image Acquisition by Bayesian Multi-Slice Adaptive Compressed Sensing. 1633-1641 - Guy Shani, Christopher Meek:
Improving Existing Fault Recovery Policies. 1642-1650 - Chunhua Shen, Junae Kim, Lei Wang, Anton van den Hengel:
Positive Semidefinite Metric Learning with Boosting. 1651-1659 - Nino Shervashidze, Karsten M. Borgwardt:
Fast subtree kernels on graphs. 1660-1668 - Lei Shi, Thomas L. Griffiths:
Neural Implementation of Hierarchical Bayesian Inference by Importance Sampling. 1669-1677 - Natasha Singh-Miller, Michael Collins:
Learning Label Embeddings for Nearest-Neighbor Multi-class Classification with an Application to Speech Recognition. 1678-1686 - Kaushik Sinha, Mikhail Belkin:
Semi-supervised Learning using Sparse Eigenfunction Bases. 1687-1695 - Fabian H. Sinz, Eero P. Simoncelli, Matthias Bethge:
Hierarchical Modeling of Local Image Features through $L_p$-Nested Symmetric Distributions. 1696-1704 - Paris Smaragdis, Madhusudana V. S. Shashanka, Bhiksha Raj:
A Sparse Non-Parametric Approach for Single Channel Separation of Known Sounds. 1705-1713 - Richard Socher, Samuel Gershman, Adler J. Perotte, Per B. Sederberg, David M. Blei, Kenneth A. Norman:
A Bayesian Analysis of Dynamics in Free Recall. 1714-1722 - Peter Sollich, Matthew Urry, Camille Coti:
Kernels and learning curves for Gaussian process regression on random graphs. 1723-1731 - Le Song, Mladen Kolar, Eric P. Xing:
Time-Varying Dynamic Bayesian Networks. 1732-1740 - Henning Sprekeler, Guillaume Hennequin, Wulfram Gerstner:
Code-specific policy gradient rules for spiking neurons. 1741-1749 - Bharath K. Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Gert R. G. Lanckriet, Bernhard Schölkopf:
Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions. 1750-1758 - Bharath K. Sriperumbudur, Gert R. G. Lanckriet:
On the Convergence of the Concave-Convex Procedure. 1759-1767 - Ingo Steinwart, Andreas Christmann:
Fast Learning from Non-i.i.d. Observations. 1768-1776 - Ian H. Stevenson, Konrad P. Körding:
Structural inference affects depth perception in the context of potential occlusion. 1777-1784 - Mark Steyvers, Michael D. Lee, Brent Miller, Pernille Hemmer:
The Wisdom of Crowds in the Recollection of Order Information. 1785-1793 - Matthew J. Streeter, Daniel Golovin, Andreas Krause:
Online Learning of Assignments. 1794-1802 - Amarnag Subramanya, Jeff A. Bilmes:
Entropic Graph Regularization in Non-Parametric Semi-Supervised Classification. 1803-1811 - Liang Sun, Jun Liu, Jianhui Chen, Jieping Ye:
Efficient Recovery of Jointly Sparse Vectors. 1812-1820 - Ilya Sutskever, Ruslan Salakhutdinov, Joshua B. Tenenbaum:
Modelling Relational Data using Bayesian Clustered Tensor Factorization. 1821-1828 - Umar Syed, Aleksandrs Slivkins, Nina Mishra:
Adapting to the Shifting Intent of Search Queries. 1829-1837 - Yee Whye Teh, Dilan Görür:
Indian Buffet Processes with Power-law Behavior. 1838-1846 - Robert E. Tillman, Arthur Gretton, Peter Spirtes:
Nonlinear directed acyclic structure learning with weakly additive noise models. 1847-1855 - Emanuel Todorov:
Compositionality of optimal control laws. 1856-1864 - Srinivas C. Turaga, Kevin L. Briggman, Moritz Helmstaedter, Winfried Denk, H. Sebastian Seung:
Maximin affinity learning of image segmentation. 1865-1873 - Tomer D. Ullman, Chris L. Baker, Owen Macindoe, Owain Evans, Noah D. Goodman, Joshua B. Tenenbaum:
Help or Hinder: Bayesian Models of Social Goal Inference. 1874-1882 - Hamed Valizadegan, Rong Jin, Ruofei Zhang, Jianchang Mao:
Learning to Rank by Optimizing NDCG Measure. 1883-1891 - Jarno Vanhatalo, Pasi Jylänki, Aki Vehtari:
Gaussian process regression with Student-t likelihood. 1910-1918 - Wolf Vanpaemel:
Measuring model complexity with the prior predictive. 1919-1927 - Andrea Vedaldi, Andrew Zisserman:
Structured output regression for detection with partial truncation. 1928-1936 - Joel Veness, David Silver, William T. B. Uther, Alan Blair:
Bootstrapping from Game Tree Search. 1937-1945 - Shobha Venkataraman, Avrim Blum, Dawn Song, Subhabrata Sen, Oliver Spatscheck:
Tracking Dynamic Sources of Malicious Activity at Internet Scale. 1946-1954 - Ed Vul, Michael C. Frank, George A. Alvarez, Joshua B. Tenenbaum:
Explaining human multiple object tracking as resource-constrained approximate inference in a dynamic probabilistic model. 1955-1963 - Hanna M. Wallach, David M. Mimno, Andrew McCallum:
Rethinking LDA: Why Priors Matter. 1973-1981 - Chong Wang, David M. Blei:
Variational Inference for the Nested Chinese Restaurant Process. 1990-1998 - Yang Wang, Gholamreza Haffari, Shaojun Wang, Greg Mori:
A Rate Distortion Approach for Semi-Supervised Conditional Random Fields. 2008-2016 - Chong Wang, David M. Blei:
Decoupling Sparsity and Smoothness in the Discrete Hierarchical Dirichlet Process. 1982-1989 - Liwei Wang:
Sufficient Conditions for Agnostic Active Learnable. 1999-2007 - Yusuke Watanabe, Kenji Fukumizu:
Graph Zeta Function in the Bethe Free Energy and Loopy Belief Propagation. 2017-2025 - Kevin Waugh, Nolan Bard, Michael H. Bowling:
Strategy Grafting in Extensive Games. 2026-2034 - Jacob Whitehill, Paul Ruvolo, Tingfan Wu, Jacob Bergsma, Javier R. Movellan:
Whose Vote Should Count More: Optimal Integration of Labels from Labelers of Unknown Expertise. 2035-2043 - Michael L. Wick, Khashayar Rohanimanesh, Sameer Singh, Andrew McCallum:
Training Factor Graphs with Reinforcement Learning for Efficient MAP Inference. 2044-2052 - Matthew H. Wilder, Matt Jones, Michael Mozer:
Sequential effects reflect parallel learning of multiple environmental regularities. 2053-2061 - Robert C. Wilson, Leif H. Finkel:
A Neural Implementation of the Kalman Filter. 2062-2070 - David P. Wipf, Srikantan S. Nagarajan:
Sparse Estimation Using General Likelihoods and Non-Factorial Priors. 2071-2079 - John Wright, Arvind Ganesh, Shankar R. Rao, YiGang Peng, Yi Ma:
Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Matrices via Convex Optimization. 2080-2088 - Xiao-Ming Wu, Anthony Man-Cho So, Zhenguo Li, Shuo-Yen Robert Li:
Fast Graph Laplacian Regularized Kernel Learning via Semidefinite-Quadratic-Linear Programming. 1964-1972 - Lei Wu, Rong Jin, Steven C. H. Hoi, Jianke Zhu, Nenghai Yu:
Learning Bregman Distance Functions and Its Application for Semi-Supervised Clustering. 2089-2097 - Fen Xia, Tie-Yan Liu, Hang Li:
Statistical Consistency of Top-k Ranking. 2098-2106 - Zhen James Xiang, Yongxin Taylor Xi, Uri Hasson, Peter J. Ramadge:
Boosting with Spatial Regularization. 2107-2115 - Lin Xiao:
Dual Averaging Method for Regularized Stochastic Learning and Online Optimization. 2116-2124 - Zenglin Xu, Rong Jin, Jianke Zhu, Irwin King, Michael R. Lyu, Zhirong Yang:
Adaptive Regularization for Transductive Support Vector Machine. 2125-2133 - Feng Yan, Ningyi Xu, Yuan (Alan) Qi:
Parallel Inference for Latent Dirichlet Allocation on Graphics Processing Units. 2134-2142 - Zhi Yang, Qi Zhao, Edward W. Keefer, Wentai Liu:
Noise Characterization, Modeling, and Reduction for In Vivo Neural Recording. 2160-2168 - Shuang-Hong Yang, Hongyuan Zha, Bao-Gang Hu:
Dirichlet-Bernoulli Alignment: A Generative Model for Multi-Class Multi-Label Multi-Instance Corpora. 2143-2150 - Xiaolin Yang, Seyoung Kim, Eric P. Xing:
Heterogeneous multitask learning with joint sparsity constraints. 2151-2159 - Zhirong Yang, Irwin King, Zenglin Xu, Erkki Oja:
Heavy-Tailed Symmetric Stochastic Neighbor Embedding. 2169-2177 - Hengshuai Yao, Richard S. Sutton, Shalabh Bhatnagar, Diao Dongcui, Csaba Szepesvári:
Multi-Step Dyna Planning for Policy Evaluation and Control. 2187-2195 - Bangpeng Yao, Dirk B. Walther, Diane M. Beck, Li Fei-Fei:
Hierarchical Mixture of Classification Experts Uncovers Interactions between Brain Regions. 2178-2186 - Nan Ye, Wee Sun Lee, Hai Leong Chieu, Dan Wu:
Conditional Random Fields with High-Order Features for Sequence Labeling. 2196-2204 - Yiming Ying, Kaizhu Huang, Colin Campbell:
Sparse Metric Learning via Smooth Optimization. 2214-2222 - Yiming Ying, Colin Campbell, Mark A. Girolami:
Analysis of SVM with Indefinite Kernels. 2205-2213 - Kai Yu, Tong Zhang, Yihong Gong:
Nonlinear Learning using Local Coordinate Coding. 2223-2231 - Yaoliang Yu, Yuxi Li, Dale Schuurmans, Csaba Szepesvári:
A General Projection Property for Distribution Families. 2232-2240 - Zhihua Zhang, Guang Dai:
Optimal Scoring for Unsupervised Learning. 2241-2249 - Manqi Zhao, Venkatesh Saligrama:
Anomaly Detection with Score functions based on Nearest Neighbor Graphs. 2250-2258 - Peilin Zhao, Steven C. H. Hoi, Rong Jin:
DUOL: A Double Updating Approach for Online Learning. 2259-2267 - Wenming Zheng, Zhouchen Lin:
Optimizing Multi-Class Spatio-Spectral Filters via Bayes Error Estimation for EEG Classification. 2268-2276 - Shuheng Zhou:
Thresholding Procedures for High Dimensional Variable Selection and Statistical Estimation. 2304-2312 - Mingyuan Zhou, Haojun Chen, John W. Paisley, Lu Ren, Guillermo Sapiro, Lawrence Carin:
Non-Parametric Bayesian Dictionary Learning for Sparse Image Representations. 2295-2303 - Chunxiao Zhou, Huixia Judy Wang, Yongmei Michelle Wang:
Efficient Moments-based Permutation Tests. 2277-2285 - Feng Zhou, Fernando De la Torre:
Canonical Time Warping for Alignment of Human Behavior. 2286-2294 - Xiaojin Zhu, Timothy T. Rogers, Bryan R. Gibson:
Human Rademacher Complexity. 2322-2330 - Long Zhu, Yuanhao Chen, Bill Freeman, Antonio Torralba:
Nonparametric Bayesian Texture Learning and Synthesis. 2313-2321 - Martin Zinkevich, Alexander J. Smola, John Langford:
Slow Learners are Fast. 2331-2339 - Daniel Zoran, Yair Weiss:
The 'tree-dependent components' of natural scenes are edge filters. 2340-2348
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