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ORCID11. AISTATS 2007: San Juan, Puerto Rico
- Marina Meila, Xiaotong Shen:
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, AISTATS 2007, San Juan, Puerto Rico, March 21-24, 2007. JMLR Proceedings 2, JMLR.org 2007 - 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 J. 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 - 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 - Amir Globerson, Tommi S. Jaakkola:
Approximate inference using conditional entropy decompositions. 130-138 - 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 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 G. 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 A. 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 - 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 - Luis E. Ortiz, Robert E. Schapire, Sham M. Kakade:
Maximum Entropy Correlated Equilibria. 347-354 - José M. Peña:
Approximate Counting of Graphical Models Via MCMC. 355-362 - Kristiaan Pelckmans, John Shawe-Taylor, Johan A. K. Suykens, Bart De Moor:
Margin based Transductive Graph Cuts using Linear Programming. 363-370 - 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 - Ruslan Salakhutdinov, Geoffrey E. Hinton:
Learning a Nonlinear Embedding by Preserving Class Neighbourhood Structure. 412-419 - Purnamrita Sarkar, Sajid M. Siddiqi, Geoffrey J. Gordon:
A Latent Space Approach to Dynamic Embedding of Co-occurrence Data. 420-427 - 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 - Nicol N. Schraudolph, Jin Yu, Simon Günter:
A Stochastic Quasi-Newton Method for Online Convex Optimization. 436-443 - Matthias W. Seeger, Florian Steinke, Koji Tsuda:
Bayesian Inference and Optimal Design in the Sparse Linear Model. 444-451 - Shai Shalev-Shwartz, Yoram Singer:
A Unified Algorithmic Approach for Efficient Online Label Ranking. 452-459 - Blake Shaw, Tony Jebara:
Minimum Volume Embedding. 460-467 - John Shawe-Taylor, Alexander N. Dolia:
A Framework for Probability Density Estimation. 468-475 - Hao Shen, Stefanie Jegelka, Arthur Gretton:
Fast Kernel ICA using an Approximate Newton Method. 476-483 - Pannagadatta K. Shivaswamy, Tony Jebara:
Ellipsoidal Machines. 484-491 - Sajid M. Siddiqi, Geoffrey J. Gordon, Andrew W. Moore:
Fast State Discovery for HMM Model Selection and Learning. 492-499 - Ricardo Bezerra de Andrade e Silva, Katherine A. Heller, Zoubin Ghahramani:
Analogical Reasoning with Relational Bayesian Sets. 500-507 - Michael Siracusa, John W. Fisher III:
Dynamic Factorization Tests: Applications to Multi-modal Data Association. 508-515 - Cristian Sminchisescu, Max Welling:
Generalized Darting Monte Carlo. 516-523 - Edward Lloyd Snelson, Zoubin Ghahramani:
Local and global sparse Gaussian process approximations. 524-531 - Harald Steck, Tommi S. Jaakkola:
Predictive Discretization during Model Selection. 532-539 - Peter Sunehag:
Emerge and spread models and word burstiness. 540-547 - Ilya Sutskever, Geoffrey E. Hinton:
Learning Multilevel Distributed Representations for High-Dimensional Sequences. 548-555 - Yee Whye Teh, Dilan Görür, Zoubin Ghahramani:
Stick-breaking Construction for the Indian Buffet Process. 556-563 - Romain Thibaux, Michael I. Jordan:
Hierarchical Beta Processes and the Indian Buffet Process. 564-571 - Jarkko Venna, Samuel Kaski:
Nonlinear Dimensionality Reduction as Information Retrieval. 572-579 - Gang Wang, Dit-Yan Yeung, Frederick H. Lochovsky:
The Kernel Path in Kernelized LASSO. 580-587 - Junhui Wang:
Efficient large margin semisupervised learning. 588-595 - Fei Wang, Shijun Wang, Changshui Zhang, Ole Winther:
Semi-Supervised Mean Fields. 596-603 - Ping Wang, Dongryeol Lee, Alexander G. Gray, James M. Rehg:
Fast Mean Shift with Accurate and Stable Convergence. 604-611 - Kilian Q. Weinberger, Gerald Tesauro:
Metric Learning for Kernel Regression. 612-619 - 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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