Volume 2, October 2007

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  Marina Meila, Xiaotong Shen: Preface. 1-2
• 
  Douglas Aberdeen, Olivier Buffet, Owen Thomas: Policy-Gradients for PSRs and POMDPs. 3-10
• 
  Sameer Agarwal, Josh Wills, Lawrence Cayton, Gert R. G. Lanckriet, David J. Kriegman, Serge Belongie: Generalized Non-metric Multidimensional Scaling. 11-18
• 
  Amr Ahmed, Eric P. Xing: Seeking The Truly Correlated Topic Posterior - on tight approximate inference of logistic-normal admixture model. 19-26
• 
  Yonatan Amit, Ofer Dekel, Yoram Singer: A Boosting Algorithm for Label Covering in Multilabel Problems. 27-34
• 
  Avleen Singh Bijral, Markus Breitenbach, Gregory Z. Grudic: Mixture of Watson Distributions: A Generative Model for Hyperspherical Embeddings. 35-42
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  Edwin V. Bonilla, Felix V. Agakov, Christopher K. I. Williams: Kernel Multi-task Learning using Task-specific Features. 43-50
• 
  Julie Carreau, Yoshua Bengio: A Hybrid Pareto Model for Conditional Density Estimation of Asymmetric Fat-Tail Data. 51-58
• 
  Miguel Á. Carreira-Perpiñán, Zhengdong Lu: The Laplacian Eigenmaps Latent Variable Model. 59-66
• 
  James Cook, Ilya Sutskever, Andriy Mnih, Geoffrey E. Hinton: Visualizing Similarity Data with a Mixture of Maps. 67-74
• 
  Timothée Cour, Jianbo Shi: Solving Markov Random Fields with Spectral Relaxation. 75-82
• 
  Hal Daumé III: Fast search for Dirichlet process mixture models. 83-90
• 
  Ricky Der, Daniel Lee: Large-Margin Classification in Banach Spaces. 91-98
• 
  Gregory Druck, Mukund Narasimhan, Paul A. Viola: Learning A* underestimates: Using inference to guide inference. 99-106
• 
  Daniel Eaton, Kevin P. Murphy: Exact Bayesian structure learning from uncertain interventions. 107-114
• 
  Michael Fink: Online Learning of Search Heuristics. 114-122
• 
  Peter V. Gehler, Olivier Chapelle: Deterministic Annealing for Multiple-Instance Learning. 123-130
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  Amir Globerson, Tommi Jaakkola: Approximate inference using conditional entropy decompositions. 130-138
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  Amir Globerson, Sam T. Roweis: Visualizing pairwise similarity via semidefinite programming. 139-146
• 
  Vibhav Gogate, Rina Dechter: SampleSearch: A Scheme that Searches for Consistent Samples. 147-154
• 
  Andrew B. Goldberg, Xiaojin Zhu, Stephen J. Wright: Dissimilarity in Graph-Based Semi-Supervised Classification. 155-162
• 
  Amit Gruber, Yair Weiss, Michal Rosen-Zvi: Hidden Topic Markov Models. 163-170
• 
  Sudipto Guha, Andrew McGregor: Space-Efficient Sampling. 171-178
• 
  David R. Hardoon, John Shawe-Taylor, Antti Ajanki, Kai Puolamäki, Samuel Kaski: Information Retrieval by Inferring Implicit Queries from Eye Movements. 179-186
• 
  Katherine A. Heller, Zoubin Ghahramani: A Nonparametric Bayesian Approach to Modeling Overlapping Clusters. 187-194
• 
  Bert C. Huang, Tony Jebara: Loopy Belief Propagation for Bipartite Maximum Weight b-Matching. 195-202
• 
  Jason K. Johnson, Venkat Chandrasekaran, Alan S. Willsky: Learning Markov Structure by Maximum Entropy Relaxation. 203-210
• 
  Risi Kondor, Andrew Howard, Tony Jebara: Multi-object tracking with representations of the symmetric group. 211-218
• 
  Petri Kontkanen, Petri Myllymäki: MDL Histogram Density Estimation. 219-226
• 
  Eyal Krupka, Naftali Tishby: Incorporating Prior Knowledge on Features into Learning. 227-234
• 
  Brian Kulis, Arun C. Surendran, John C. Platt: Fast Low-Rank Semidefinite Programming for Embedding and Clustering. 235-242
• 
  Neil D. Lawrence: Learning for Larger Datasets with the Gaussian Process Latent Variable Model. 243-250
• 
  Svetlana Lazebnik, Maxim Raginsky: Learning Nearest-Neighbor Quantizers from Labeled Data by Information Loss Minimization. 251-258
• 
  Ann B. Lee, Boaz Nadler: Treelets | A Tool for Dimensionality Reduction and Multi-Scale Analysis of Unstructured Data. 259-266
• 
  Jeremy Lewi, Robert J. Butera, Liam Paninski: Efficient active learning with generalized linear models. 267-274
• 
  Xiao Li, Jeff Bilmes: A Bayesian Divergence Prior for Classiffier Adaptation. 275-282
• 
  Han Liu, John D. Lafferty, Larry A. Wasserman: Sparse Nonparametric Density Estimation in High Dimensions Using the Rodeo. 283-290
• 
  Yufeng Liu: Fisher Consistency of Multicategory Support Vector Machines. 291-298
• 
  Zhengdong Lu: Semi-supervised Clustering with Pairwise Constraints: A Discriminative Approach. 299-306
• 
  Omid Madani, Wiley Greiner, David Kempe, Mohammad R. Salavatipour: Recall Systems: Effcient Learning and Use of Category Indices. 307-314
• 
  Vikash K. Mansinghka, Daniel M. Roy, Ryan Rifkin, Joshua B. Tenenbaum: AClass: A simple, online, parallelizable algorithm for probabilistic classification. 315-322
• 
  H. Brendan McMahan, Geoffrey J. Gordon: A Fast Bundle-based Anytime Algorithm for Poker and other Convex Games. 323-330
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  Joris M. Mooij, Bastian Wemmenhove, Bert Kappen, Tommaso Rizzo: Loop Corrected Belief Propagation. 331-338
• 
  Alexandru Niculescu-Mizil, Rich Caruana: Inductive Transfer for Bayesian Network Structure Learning. 339-346
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  Luis E. Ortiz, Robert E. Schapire, Sham M. Kakade: Maximum Entropy Correlated Equilibria. 347-354
• 
  Jose M. Peña: Approximate Counting of Graphical Models Via MCMC. 355-362
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  Kristiaan Pelckmans, John Shawe-Taylor, Johan A. K. Suykens, Bart De Moor: Margin based Transductive Graph Cuts using Linear Programming. 363-370
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  Marc'Aurelio Ranzato, Y-Lan Boureau, Sumit Chopra, Yann LeCun: A Unified Energy-Based Framework for Unsupervised Learning. 371-379
• 
  Nathan D. Ratliff, J. Andrew Bagnell, Martin Zinkevich: (Approximate) Subgradient Methods for Structured Prediction. 380-387
• 
  Vikas C. Raykar, Ramani Duraiswami, Balaji Krishnapuram: A fast algorithm for learning large scale preference relations. 388-395
• 
  David S. Rosenberg, Peter L. Bartlett: The Rademacher Complexity of Co-Regularized Kernel Classes. 396-403
• 
  Nicolas Le Roux, Yoshua Bengio: Continuous Neural Networks. 404-411
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  Ruslan Salakhutdinov, Geoffrey E. Hinton: Learning a Nonlinear Embedding by Preserving Class Neighbourhood Structure. 412-419
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  Purnamrita Sarkar, Sajid M. Siddiqi, Geoffrey J. Gordon: A Latent Space Approach to Dynamic Embedding of Co-occurrence Data. 420-427
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  Martin Schafföner, Edin Andelic, Marcel Katz, Sven E. Krüger, Andreas Wendemuth: Memory-Effcient Orthogonal Least Squares Kernel Density Estimation using Enhanced Empirical Cumulative Distribution Functions. 428-435
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  Nicol N. Schraudolph, Jin Yu, Simon Günter: A Stochastic Quasi-Newton Method for Online Convex Optimization. 436-443
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  Matthias Seeger, Florian Steinke, Koji Tsuda: Bayesian Inference and Optimal Design in the Sparse Linear Model. 444-451
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  Shai Shalev-Shwartz, Yoram Singer: A Unified Algorithmic Approach for Efficient Online Label Ranking. 452-459
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  Blake Shaw, Tony Jebara: Minimum Volume Embedding. 460-467
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  John Shawe-Taylor, Alexander N. Dolia: A Framework for Probability Density Estimation. 468-475
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  Hao Shen, Stefanie Jegelka, Arthur Gretton: Fast Kernel ICA using an Approximate Newton Method. 476-483
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  Pannagadatta K. Shivaswamy, Tony Jebara: Ellipsoidal Machines. 484-491
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  Sajid M. Siddiqi, Geoffrey J. Gordon, Andrew W. Moore: Fast State Discovery for HMM Model Selection and Learning. 492-499
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  Ricardo Silva, Katherine A. Heller, Zoubin Ghahramani: Analogical Reasoning with Relational Bayesian Sets. 500-507
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  Michael Siracusa, John W. Fisher III: Dynamic Factorization Tests: Applications to Multi-modal Data Association. 508-515
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  Cristian Sminchisescu, Max Welling: Generalized Darting Monte Carlo. 516-523
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  Edward Snelson, Zoubin Ghahramani: Local and global sparse Gaussian process approximations. 524-531
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  Harald Steck, Tommi Jaakkola: Predictive Discretization during Model Selection. 532-539
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  Peter Sunehag: Emerge and spread models and word burstiness. 540-547
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  Ilya Sutskever, Geoffrey E. Hinton: Learning Multilevel Distributed Representations for High-Dimensional Sequences. 548-555
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  Yee Whye Teh, Dilan Görür, Zoubin Ghahramani: Stick-breaking Construction for the Indian Buffet Process. 556-563
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  Romain Thibaux, Michael I. Jordan: Hierarchical Beta Processes and the Indian Buffet Process. 564-571
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  Jarkko Venna, Samuel Kaski: Nonlinear Dimensionality Reduction as Information Retrieval. 572-579
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  Gang Wang, Dit-Yan Yeung, Frederick H. Lochovsky: The Kernel Path in Kernelized LASSO. 580-587
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  Junhui Wang: Efficient large margin semisupervised learning. 588-595
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  Fei Wang, Shijun Wang, Changshui Zhang, Ole Winther: Semi-Supervised Mean Fields. 596-603
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  Ping Wang, Dongryeol Lee, Alexander G. Gray, James M. Rehg: Fast Mean Shift with Accurate and Stable Convergence. 604-611
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  Kilian Q. Weinberger, Gerald Tesauro: Metric Learning for Kernel Regression. 612-619
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  Jason L. Williams, John W. Fisher III, Alan S. Willsky: Performance Guarantees for Information Theoretic Active Inference. 620-627
• 
  Mingrui Wu, Bernhard Schölkopf: Transductive Classification via Local Learning Regularization. 628-635
• 
  Yuhong Yang: How Powerful Can Any Regression Learning Procedure Be?. 636-643
• 
  Jieping Ye, Tao Xiong: SVM versus Least Squares SVM. 644-651
• 
  Changhe Yuan, Marek J. Druzdzel: Importance Sampling for General Hybrid Bayesian Networks. 652-659
• 
  Ming Yuan: Nonnegative Garrote Component Selection in Functional ANOVA models. 660-666
• 
  Jiji Zhang: Generalized Do-Calculus with Testable Causal Assumptions. 667-674
• 
  Hui Zou: An Improved 1-norm SVM for Simultaneous Classification and Variable Selection. 675-681

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