22. NIPS 2009: Vancouver, British Columbia, Canada

• Alekh Agarwal, Peter L. Bartlett, Pradeep D. 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 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 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 Paillere-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, Fei-Fei Li:
  Exploring Functional Connectivities of the Human Brain using Multivariate Information Analysis. 270-278
  
• Jonathan 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 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 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, Ben 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:
  $L_1$-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:
  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 Hsu, Sham 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 T. 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, Sankaran Raman, 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 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 A. 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
  
• Aurelie 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 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 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 A. 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 Negahban, Pradeep D. 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 John 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 Pillow:
  Time-rescaling methods for the estimation and assessment of non-Poisson neural encoding models. 1473-1481
  
• Hamed Pirsiavash, Deva Ramanan, Charless Fowlkes:
  Bilinear classifiers for visual recognition. 1482-1490
  
• Novi Quadrianto, James Petterson, Alex 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 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 Koerding:
  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 Ullman, Chris 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 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 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 Qi:
  Parallel Inference for Latent Dirichlet Allocation on Graphics Processing Units. 2134-2142
  
• Zhi Yang, Qi Zhao, Edward 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, Fei-Fei Li:
  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 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 William 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, Alex 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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