Journal of Machine Learning Research - Proceedings Track, Volume 5

Volume 5, April 2009

• David A. Van Dyk, Max Welling:
  Preface.
  
• Margareta Ackerman, Shai Ben-David:
  Clusterability: A Theoretical Study. 1-8
  
• Mauricio A. Álvarez, David Luengo, Neil D. Lawrence:
  Latent Force Models. 9-16
  
• Artin Armagan:
  Variational Bridge Regression. 17-24
  
• Shai Ben-David, Tyler Lu, Dávid Pál, Miroslava Sotáková:
  Learning Low Density Separators. 25-32
  
• Liefeng Bo, Cristian Sminchisescu:
  Supervised Spectral Latent Variable Models. 33-40
  
• Héctor Corrada Bravo, Stephen J. Wright, Kevin Eng, Sunduz Keles, Grace Wahba:
  Estimating Tree-Structured Covariance Matrices via Mixed-Integer Programming. 41-48
  
• Gavin Brown:
  A New Perspective for Information Theoretic Feature Selection. 49-56
  
• Alberto Giovanni Busetto, Joachim M. Buhmann:
  Structure Identification by Optimized Interventions. 57-64
  
• Kevin Robert Canini, Lei Shi, Thomas L. Griffiths:
  Online Inference of Topics with Latent Dirichlet Allocation. 65-72
  
• Carlos M. Carvalho, Nicholas G. Polson, James G. Scott:
  Handling Sparsity via the Horseshoe. 73-80
  
• Jonathan Chang, David M. Blei:
  Relational Topic Models for Document Networks. 81-88
  
• Wei Chu, Zoubin Ghahramani:
  Probabilistic Models for Incomplete Multi-dimensional Arrays. 89-96
  
• Stéphan Clémençon, Nicolas Vayatis:
  On Partitioning Rules for Bipartite Ranking. 97-104
  
• Koby Crammer, Mehryar Mohri, Fernando Pereira:
  Gaussian Margin Machines. 105-112
  
• Dafna Shahaf, Carlos Guestrin:
  Learning Thin Junction Trees via Graph Cuts. 113-120
  
• Tom Diethe, Zakria Hussain, David R. Hardoon, John Shawe-Taylor:
  Matching Pursuit Kernel Fisher Discriminant Analysis. 121-128
  
• Joshua V. Dillon, Guy Lebanon:
  Statistical and Computational Tradeoffs in Stochastic Composite Likelihood. 129-136
  
• Finale Doshi, Kurt Miller, Jurgen Van Gael, Yee Whye Teh:
  Variational Inference for the Indian Buffet Process. 137-144
  
• Frederik Eaton, Zoubin Ghahramani:
  Choosing a Variable to Clamp. 145-152
  
• Dumitru Erhan, Pierre-Antoine Manzagol, Yoshua Bengio, Samy Bengio, Pascal Vincent:
  The Difficulty of Training Deep Architectures and the Effect of Unsupervised Pre-Training. 153-160
  
• Inmar E. Givoni, Brendan J. Frey:
  Semi-Supervised Affinity Propagation with Instance-Level Constraints. 161-168
  
• Andrew B. Goldberg, Xiaojin Zhu, Aarti Singh, Zhiting Xu, Robert Nowak:
  Multi-Manifold Semi-Supervised Learning. 169-176
  
• Joseph Gonzalez, Yucheng Low, Carlos Guestrin:
  Residual Splash for Optimally Parallelizing Belief Propagation. 177-184
  
• Yue Guan, Jennifer G. Dy:
  Sparse Probabilistic Principal Component Analysis. 185-192
  
• Saptarshi Guha, Paul Kidwell, Ryan Hafen, William S. Cleveland:
  Visualization Databases for the Analysis of Large Complex Datasets. 193-200
  
• Andrew Guillory, Erick Chastain, Jeff Bilmes:
  Active Learning as Non-Convex Optimization. 201-208
  
• Steve Hanneke, Eric P. Xing:
  Network Completion and Survey Sampling. 209-215
  
• Jarvis Haupt, Rui Castro, Robert Nowak:
  Distilled sensing: selective sampling for sparse signal recovery. 216-223
  
• Katherine A. Heller, Yee Whye Teh, Dilan Görür:
  Infinite Hierarchical Hidden Markov Models. 224-231
  
• Matthew D. Hoffman, Nando de Freitas, Arnaud Doucet, Jan Peters:
  An Expectation Maximization Algorithm for Continuous Markov Decision Processes with Arbitrary Reward. 232-239
  
• Bert C. Huang, Ansaf Salleb-Aouissi:
  Maximum Entropy Density Estimation with Incomplete Presence-Only Data. 240-247
  
• Jonathan Huang, Carlos Guestrin, Xiaoye Jiang, Leonidas J. Guibas:
  Exploiting Probabilistic Independence for Permutations. 248-255
  
• Alexander T. Ihler, David A. McAllester:
  Particle Belief Propagation. 256-263
  
• Michael Johanson, Michael H. Bowling:
  Data Biased Robust Counter Strategies. 264-271
  
• Varun Kanade, H. Brendan McMahan, Brent Bryan:
  Sleeping Experts and Bandits with Stochastic Action Availability and Adversarial Rewards. 272-279
  
• Minyoung Kim, Vladimir Pavlovic:
  Covariance Operator Based Dimensionality Reduction with Extension to Semi-Supervised Settings. 280-287
  
• Nicole Krämer, Masashi Sugiyama, Mikio L. Braun:
  Lanczos Approximations for the Speedup of Kernel Partial Least Squares Regression. 288-295
  
• Brian Kulis, Suvrit Sra, Inderjit S. Dhillon:
  Convex Perturbations for Scalable Semidefinite Programming. 296-303
  
• Sanjiv Kumar, Mehryar Mohri, Ameet Talwalkar:
  Sampling Techniques for the Nystrom Method. 304-311
  
• Hugo Larochelle, Dumitru Erhan, Pascal Vincent:
  Deep Learning using Robust Interdependent Codes. 312-319
  
• Hyekyoung Lee, Seungjin Choi:
  Group Nonnegative Matrix Factorization for EEG Classification. 320-327
  
• Fuxin Li, Yun-Shan Fu, Yu-Hong Dai, Cristian Sminchisescu, Jue Wang:
  Kernel Learning by Unconstrained Optimization. 328-335
  
• Wu-Jun Li, Zhihua Zhang, Dit-Yan Yeung:
  Latent Wishart Processes for Relational Kernel Learning. 336-343
  
• Yu-Feng Li, Ivor W. Tsang, James Tin-Yau Kwok, Zhi-Hua Zhou:
  Tighter and Convex Maximum Margin Clustering. 344-351
  
• Yuxi Li, Csaba Szepesvári, Dale Schuurmans:
  Learning Exercise Policies for American Options. 352-359
  
• Yuanqing Lin, Shenghuo Zhu, Daniel D. Lee, Ben Taskar:
  Learning Sparse Markov Network Structure via Ensemble-of-Trees Models. 360-367
  
• Christoph Lippert, Oliver Stegle, Zoubin Ghahramani, Karsten M. Borgwardt:
  A kernel method for unsupervised structured network inference. 368-375
  
• Han Liu, Jian Zhang:
  Estimation Consistency of the Group Lasso and its Applications. 376-383
  
• Laurens van der Maaten:
  Learning a Parametric Embedding by Preserving Local Structure. 384-391
  
• Bhushan Mandhani, Marina Meila:
  Tractable Search for Learning Exponential Models of Rankings. 392-399
  
• Vikash K. Mansinghka, Daniel M. Roy, Eric Jonas, Joshua B. Tenenbaum:
  Exact and Approximate Sampling by Systematic Stochastic Search. 400-407
  
• Patrick Pletscher, Cheng Soon Ong, Joachim M. Buhmann:
  Spanning Tree Approximations for Conditional Random Fields. 408-415
  
• Liva Ralaivola, Marie Szafranski, Guillaume Stempfel:
  Chromatic PAC-Bayes Bounds for Non-IID Data. 416-423
  
• Nathan D. Ratliff, Brian D. Ziebart, Kevin Peterson, J. Andrew Bagnell, Martial Hebert, Anind K. Dey, Siddhartha S. Srinivasa:
  Inverse Optimal Heuristic Control for Imitation Learning. 424-431
  
• Steven de Rooij, Tim van Erven:
  Learning the Switching Rate by Discretising Bernoulli Sources Online. 432-439
  
• Dan Roth, Kevin Small, Ivan Titov:
  Sequential Learning of Classifiers for Structured Prediction Problems. 440-447
  
• Ruslan Salakhutdinov, Geoffrey E. Hinton:
  Deep Boltzmann Machines. 448-455
  
• Mark W. Schmidt, Ewout van den Berg, Michael P. Friedlander, Kevin P. Murphy:
  Optimizing Costly Functions with Simple Constraints: A Limited-Memory Projected Quasi-Newton Algorithm. 456-463
  
• Clayton Scott, Gilles Blanchard:
  Novelty detection: Unlabeled data definitely help. 464-471
  
• Yevgeny Seldin, Naftali Tishby:
  PAC-Bayesian Generalization Bound for Density Estimation with Application to Co-clustering. 472-479
  
• John Shawe-Taylor, David R. Hardoon:
  PAC-Bayes Analysis Of Maximum Entropy Classification. 480-487
  
• Nino Shervashidze, S. V. N. Vishwanathan, Tobias Petri, Kurt Mehlhorn, Karsten M. Borgwardt:
  Efficient graphlet kernels for large graph comparison. 488-495
  
• Qinfeng Shi, James Petterson, Gideon Dror, John Langford, Alexander J. Smola, Alexander L. Strehl, Vishy Vishwanathan:
  Hash Kernels. 496-503
  
• Tomi Silander, Teemu Roos, Petri Myllymäki:
  Locally Minimax Optimal Predictive Modeling with Bayesian Networks. 504-511
  
• Ricardo Silva, Robert B. Gramacy:
  MCMC Methods for Bayesian Mixtures of Copulas. 512-519
  
• Ricardo Silva, Zoubin Ghahramani:
  Factorial Mixture of Gaussians and the Marginal Independence Model. 520-527
  
• Michael Siracusa, John W. Fisher III:
  Tractable Bayesian Inference of Time-Series Dependence Structure. 528-535
  
• Alexander J. Smola, Le Song, Choon Hui Teo:
  Relative Novelty Detection. 536-543
  
• David Sontag, Tommi Jaakkola:
  Tree Block Coordinate Descent for MAP in Graphical Models. 544-551
  
• Thomas S. Stepleton, Zoubin Ghahramani, Geoffrey J. Gordon, Tai Sing Lee:
  The Block Diagonal Infinite Hidden Markov Model. 552-559
  
• Peter Sunehag, Jochen Trumpf, S. V. N. Vishwanathan, Nicol N. Schraudolph:
  Variable Metric Stochastic Approximation Theory. 560-566
  
• Michalis K. Titsias:
  Variational Learning of Inducing Variables in Sparse Gaussian Processes. 567-574
  
• Changhu Wang, Shuicheng Yan, Lei Zhang, HongJiang Zhang:
  Non-Negative Semi-Supervised Learning. 575-582
  
• Chong Wang, Bo Thiesson, Christopher Meek, David M. Blei:
  Markov Topic Models. 583-590
  
• Shijun Wang, Rong Jin:
  An Information Geometry Approach for Distance Metric Learning. 591-598
  
• Zhuoran Wang, John Shawe-Taylor:
  Large-Margin Structured Prediction via Linear Programming. 599-606
  
• Frank Wood, Yee Whye Teh:
  A Hierarchical Nonparametric Bayesian Approach to Statistical Language Model Domain Adaptation. 607-614
  
• Yongxin Taylor Xi, Zhen James Xiang, Peter J. Ramadge, Robert E. Schapire:
  Speed and Sparsity of Regularized Boosting. 615-622
  
• Yang Xu, Katherine A. Heller, Zoubin Ghahramani:
  Tree-Based Inference for Dirichlet Process Mixtures. 623-630
  
• Min Yang, Yuxi Li, Dale Schuurmans:
  Dual Temporal Difference Learning. 631-638
  
• Shipeng Yu, Balaji Krishnapuram, Rómer Rosales, R. Bharat Rao:
  Active Sensing. 639-646
  
• Zhihua Zhang, Michael I. Jordan, Wu-Jun Li, Dit-Yan Yeung:
  Coherence Functions for Multicategory Margin-based Classification Methods. 647-654
  
• Zhihua Zhang, Michael I. Jordan:
  Latent Variable Models for Dimensionality Reduction. 655-662
  
• Mingjun Zhong, Mark Girolami:
  Reversible Jump MCMC for Non-Negative Matrix Factorization. 663-670
  

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