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JMLR W&CP: Volume 2: AISTATS 2007

JMLR Workshop and Conference Proceedings
Volume 2: AISTATS 2007

Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics
March 21-24, 2007, San Juan, Puerto Rico

Editors: Marina Meila and Xiaotong Shen
Organizers Invited Speakers Program Committee Author Index Search
Preface
Marina Meila and Xiaotong Shen; 2:1-2, 2007.
[pdf]

Policy-Gradients for PSRs and POMDPs
Douglas Aberdeen, Olivier Buffet, Owen Thomas; 2:3-10, 2007.
[abs] [pdf]

Generalized Non-metric Multidimensional Scaling
Sameer Agarwal, Josh Wills, Lawrence Cayton, Gert Lanckriet, David Kriegman, Serge Belongie; 2:11-18, 2007.
[abs] [pdf]

Seeking The Truly Correlated Topic Posterior - on tight approximate inference of logistic-normal admixture model
Amr Ahmed, Eric P. Xing; 2:19-26, 2007.
[abs] [pdf]

A Boosting Algorithm for Label Covering in Multilabel Problems
Yonatan Amit, Ofer Dekel, Yoram Singer; 2:27-34, 2007.
[abs] [pdf]

Mixture of Watson Distributions: A Generative Model for Hyperspherical Embeddings
Avleen S. Bijral, Markus Breitenbach, Greg Grudic; 2:35-42, 2007.
[abs] [pdf]

Kernel Multi-task Learning using Task-specific Features
Edwin V. Bonilla, Felix V. Agakov, Christopher K. I. Williams; 2:43-50, 2007.
[abs] [pdf]

A Hybrid Pareto Model for Conditional Density Estimation of Asymmetric Fat-Tail Data
Julie Carreau, Yoshua Bengio; 2:51-58, 2007.
[abs] [pdf]

The Laplacian Eigenmaps Latent Variable Model
Miguel A. Carreira-Perpiñan, Zhengdong Lu; 2:59-66, 2007.
[abs] [pdf]

Visualizing Similarity Data with a Mixture of Maps
James Cook, Ilya Sutskever, Andriy Mnih, Geoffrey Hinton; 2:67-74, 2007.
[abs] [pdf]

Solving Markov Random Fields with Spectral Relaxation
Timothee Cour, Jianbo Shi; 2:75-82, 2007.
[abs] [pdf]

Fast search for Dirichlet process mixture models
Hal Daume III; 2:83-90, 2007.
[abs] [pdf]

Large-Margin Classification in Banach Spaces
Ricky Der, Daniel Lee; 2:91-98, 2007.
[abs] [pdf]

Learning A* underestimates : Using inference to guide inference
Gregory Druck, Mukund Narasimhan, Paul Viola; 2:99-106, 2007.
[abs] [pdf]

Exact Bayesian structure learning from uncertain interventions
Daniel Eaton, Kevin Murphy; 2:107-114, 2007.
[abs] [pdf]

Online Learning of Search Heuristics
Michael Fink; 2:114-122, 2007.
[abs] [pdf]

Deterministic Annealing for Multiple-Instance Learning
Peter V. Gehler, Olivier Chapelle; 2:123-130, 2007.
[abs] [pdf]

Approximate inference using conditional entropy decompositions
Amir Globerson, Tommi Jaakkola; 2:130-138, 2007.
[abs] [pdf]

Visualizing pairwise similarity via semidefinite programming
Amir Globerson, Sam Roweis; 2:139-146, 2007.
[abs] [pdf]

SampleSearch: A Scheme that Searches for Consistent Samples
Vibhav Gogate, Rina Dechter; 2:147-154, 2007.
[abs] [pdf]

Dissimilarity in Graph-Based Semi-Supervised Classification
Andrew B. Goldberg, Xiaojin Zhu, Stephen Wright; 2:155-162, 2007.
[abs] [pdf]

Hidden Topic Markov Models
Amit Gruber, Yair Weiss, Michal Rosen-Zvi; 2:163-170, 2007.
[abs] [pdf]

Space-Efficient Sampling
Sudipto Guha, Andrew McGregor; 2:171-178, 2007.
[abs] [pdf]

Information Retrieval by Inferring Implicit Queries from Eye Movements
David R. Hardoon, John Shawe-Taylor, Antti Ajanki, Kai Puolamäki, Samuel Kaski; 2:179-186, 2007.
[abs] [pdf]

A Nonparametric Bayesian Approach to Modeling Overlapping Clusters
Katherine A. Heller, Zoubin Ghahramani; 2:187-194, 2007.
[abs] [pdf]

Loopy Belief Propagation for Bipartite Maximum Weight b-Matching
Bert Huang, Tony Jebara; 2:195-202, 2007.
[abs] [pdf]

Learning Markov Structure by Maximum Entropy Relaxation
Jason K. Johnson, Venkat Chandrasekaran, Alan S. Willsky; 2:203-210, 2007.
[abs] [pdf]

Multi-object tracking with representations of the symmetric group
Risi Kondor, Andrew Howard, Tony Jebara; 2:211-218, 2007.
[abs] [pdf]

MDL Histogram Density Estimation
Petri Kontkanen, Petri Myllymäki; 2:219-226, 2007.
[abs] [pdf]

Incorporating Prior Knowledge on Features into Learning
Eyal Krupka, Naftali Tishby; 2:227-234, 2007.
[abs] [pdf]

Fast Low-Rank Semidefinite Programming for Embedding and Clustering
Brian Kulis, Arun C. Surendran, John C. Platt; 2:235-242, 2007.
[abs] [pdf]

Learning for Larger Datasets with the Gaussian Process Latent Variable Model
Neil D. Lawrence; 2:243-250, 2007.
[abs] [pdf]

Learning Nearest-Neighbor Quantizers from Labeled Data by Information Loss Minimization
Svetlana Lazebnik, Maxim Raginsky; 2:251-258, 2007.
[abs] [pdf]

Treelets | A Tool for Dimensionality Reduction and Multi-Scale Analysis of Unstructured Data
Ann B. Lee, Boaz Nadler; 2:259-266, 2007.
[abs] [pdf]

Efficient active learning with generalized linear models
Jeremy Lewi, Robert Butera, Liam Paninski; 2:267-274, 2007.
[abs] [pdf]

A Bayesian Divergence Prior for Classiffier Adaptation
Xiao Li, Jeff Bilmes; 2:275-282, 2007.
[abs] [pdf]

Sparse Nonparametric Density Estimation in High Dimensions Using the Rodeo
Han Liu, John Lafferty, Larry Wasserman; 2:283-290, 2007.
[abs] [pdf]

Fisher Consistency of Multicategory Support Vector Machines
Yufeng Liu; 2:291-298, 2007.
[abs] [pdf]

Semi-supervised Clustering with Pairwise Constraints: A Discriminative Approach
Zhengdong Lu; 2:299-306, 2007.
[abs] [pdf]

Recall Systems: Effcient Learning and Use of Category Indices
Omid Madani, Wiley Greiner, David Kempe, Mohammad R. Salavatipour; 2:307-314, 2007.
[abs] [pdf]

AClass: A simple, online, parallelizable algorithm for probabilistic classification
Vikash K. Mansinghka, Daniel M. Roy, Ryan Rifkin, Josh Tenenbaum; 2:315-322, 2007.
[abs] [pdf]

A Fast Bundle-based Anytime Algorithm for Poker and other Convex Games
H. Brendan McMahan, Geoffrey J. Gordony; 2:323-330, 2007.
[abs] [pdf]

Loop Corrected Belief Propagation
Joris Mooij, Bastian Wemmenhove, Bert Kappen, Tommaso Rizzo; 2:331-338, 2007.
[abs] [pdf]

Inductive Transfer for Bayesian Network Structure Learning
Alexandru Niculescu-Mizil, Rich Caruana; 2:339-346, 2007.
[abs] [pdf]

Maximum Entropy Correlated Equilibria
Luis E. Ortiz, Robert E. Schapire, Sham M. Kakade; 2:347-354, 2007.
[abs] [pdf]

Approximate Counting of Graphical Models Via MCMC
Jose M. Peña; 2:355-362, 2007.
[abs] [pdf]

Margin based Transductive Graph Cuts using Linear Programming
K. Pelckmans, J. Shawe-Taylor, J.A.K. Suykens, B. De Moor; 2:363-370, 2007.
[abs] [pdf]

A Unified Energy-Based Framework for Unsupervised Learning
Marc'Aurelio Ranzato, Y-Lan Boureau, Sumit Chopra, Yann LeCun; 2:371-379, 2007.
[abs] [pdf]

(Approximate) Subgradient Methods for Structured Prediction
Nathan D. Ratliff, J. Andrew Bagnell, Martin A. Zinkevich; 2:380-387, 2007.
[abs] [pdf]

A fast algorithm for learning large scale preference relations
Vikas C. Raykar, Ramani Duraiswami, Balaji Krishnapuram; 2:388-395, 2007.
[abs] [pdf]

The Rademacher Complexity of Co-Regularized Kernel Classes
David S. Rosenberg, Peter L. Bartlett; 2:396-403, 2007.
[abs] [pdf]

Continuous Neural Networks
Nicolas Le Roux, Yoshua Bengio; 2:404-411, 2007.
[abs] [pdf]

Learning a Nonlinear Embedding by Preserving Class Neighbourhood Structure
Ruslan Salakhutdinov, Geoff Hinton; 2:412-419, 2007.
[abs] [pdf]

A Latent Space Approach to Dynamic Embedding of Co-occurrence Data
Purnamrita Sarkar, Sajid M. Siddiqi, Geogrey J. Gordon; 2:420-427, 2007.
[abs] [pdf]

Memory-Effcient Orthogonal Least Squares Kernel Density Estimation using Enhanced Empirical Cumulative Distribution Functions
Martin Schaffoner, Edin Andelic, Marcel Katz, Sven E. Krüger, Andreas Wendemuth; 2:428-435, 2007.
[abs] [pdf]

A Stochastic Quasi-Newton Method for Online Convex Optimization
Nicol N. Schraudolph, Jin Yu, Simon Günter; 2:436-443, 2007.
[abs] [pdf]

Bayesian Inference and Optimal Design in the Sparse Linear Model
Matthias Seeger, Florian Steinke, Koji Tsuda; 2:444-451, 2007.
[abs] [pdf]

A Unified Algorithmic Approach for Efficient Online Label Ranking
Shai Shalev-Shwartz, Yoram Singer; 2:452-459, 2007.
[abs] [pdf]

Minimum Volume Embedding
Blake Shaw, Tony Jebara; 2:460-467, 2007.
[abs] [pdf]

A Framework for Probability Density Estimation
John Shawe-Taylor, Alex Dolia; 2:468-475, 2007.
[abs] [pdf]

Fast Kernel ICA using an Approximate Newton Method
Hao Shen, Stefanie Jegelka, Arthur Gretton; 2:476-483, 2007.
[abs] [pdf]

Ellipsoidal Machines
Pannagadatta K. Shivaswamy, Tony Jebara; 2:484-491, 2007.
[abs] [pdf]

Fast State Discovery for HMM Model Selection and Learning
Sajid M. Siddiqi, Geogrey J. Gordon, Andrew W. Moore; 2:492-499, 2007.
[abs] [pdf]

Analogical Reasoning with Relational Bayesian Sets
Ricardo Silva, Katherine A. Heller, Zoubin Ghahramani; 2:500-507, 2007.
[abs] [pdf]

Dynamic Factorization Tests: Applications to Multi-modal Data Association
Michael R. Siracusa, John W. Fisher III; 2:508-515, 2007.
[abs] [pdf]

Generalized Darting Monte Carlo
Cristian Sminchisescu, Max Welling; 2:516-523, 2007.
[abs] [pdf]

Local and global sparse Gaussian process approximations
Edward Snelson, Zoubin Ghahramani; 2:524-531, 2007.
[abs] [pdf]

Predictive Discretization during Model Selection
Harald Steck, Tommi S. Jaakkola; 2:532-539, 2007.
[abs] [pdf]

Emerge and spread models and word burstiness
Peter Sunehag; 2:540-547, 2007.
[abs] [pdf]

Learning Multilevel Distributed Representations for High-Dimensional Sequences
Ilya Sutskever, Geoffrey Hinton; 2:548-555, 2007.
[abs] [pdf]

Stick-breaking Construction for the Indian Buffet Process
Yee Whye Teh, Dilan Grür, Zoubin Ghahramani; 2:556-563, 2007.
[abs] [pdf]

Hierarchical Beta Processes and the Indian Buffet Process
Romain Thibaux, Michael I. Jordan; 2:564-571, 2007.
[abs] [pdf]

Nonlinear Dimensionality Reduction as Information Retrieval
Jarkko Venna, Samuel Kaski; 2:572-579, 2007.
[abs] [pdf]

The Kernel Path in Kernelized LASSO
Gang Wang, Dit-Yan Yeung, Frederick H. Lochovsky; 2:580-587, 2007.
[abs] [pdf]

Efficient large margin semisupervised learning
Junhui Wang; 2:588-595, 2007.
[abs] [pdf]

Semi-Supervised Mean Fields
Fei Wang, Shijun Wang, Changshui Zhang, Ole Winther; 2:596-603, 2007.
[abs] [pdf]

Fast Mean Shift with Accurate and Stable Convergence
Ping Wang, Dongryeol Lee, Alexander Gray, James M. Rehg; 2:604-611, 2007.
[abs] [pdf]

Metric Learning for Kernel Regression
Kilian Q. Weinberger, Gerald Tesauro; 2:612-619, 2007.
[abs] [pdf]

Performance Guarantees for Information Theoretic Active Inference
Jason L. Williams, John W. Fisher III, Alan S. Willsky; 2:620-627, 2007.
[abs] [pdf]

Transductive Classification via Local Learning Regularization
Mingrui Wu, Bernhard Scholkopf; 2:628-635, 2007.
[abs] [pdf]

How Powerful Can Any Regression Learning Procedure Be?
Yuhong Yang; 2:636-643, 2007.
[abs] [pdf]

SVM versus Least Squares SVM
Jieping Ye, Tao Xiong; 2:644-651, 2007.
[abs] [pdf]

Importance Sampling for General Hybrid Bayesian Networks
Changhe Yuan, Marek J. Druzdzel; 2:652-659, 2007.
[abs] [pdf]

Nonnegative Garrote Component Selection in Functional ANOVA models
Ming Yuan; 2:660-666, 2007.
[abs] [pdf]

Generalized Do-Calculus with Testable Causal Assumptions
Jiji Zhang; 2:667-674, 2007.
[abs] [pdf]

An Improved 1-norm SVM for Simultaneous Classification and Variable Selection
Hui Zou; 2:675-681, 2007.
[abs] [pdf]

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