AISTATS 2009
84 papers
A Kernel Method for Unsupervised Structured Network Inference
Christoph Lippert, Oliver Stegle, Zoubin Ghahramani, Karsten Borgwardt Active Learning as Non-Convex Optimization
Andrew Guillory, Erick Chastain, Jeff Bilmes Active Sensing
Shipeng Yu, Balaji Krishnapuram, Romer Rosales, R. Bharat Rao Choosing a Variable to Clamp
Frederik Eaton, Zoubin Ghahramani Chromatic PAC-Bayes Bounds for Non-IID Data
Liva Ralaivola, Marie Szafranski, Guillaume Stempfel Clusterability: A Theoretical Study
Margareta Ackerman, Shai Ben-David Deep Boltzmann Machines
Ruslan Salakhutdinov, Geoffrey Hinton Deep Learning Using Robust Interdependent Codes
Hugo Larochelle, Dumitru Erhan, Pascal Vincent Dual Temporal Difference Learning
Min Yang, Yuxi Li, Dale Schuurmans Efficient Graphlet Kernels for Large Graph Comparison
Nino Shervashidze, Svn Vishwanathan, Tobias Petri, Kurt Mehlhorn, Karsten Borgwardt Exploiting Probabilistic Independence for Permutations
Jonathan Huang, Carlos Guestrin, Xiaoye Jiang, Leonidas Guibas Gaussian Margin Machines
Koby Crammer, Mehryar Mohri, Fernando Pereira Handling Sparsity via the Horseshoe
Carlos M. Carvalho, Nicholas G. Polson, James G. Scott Hash Kernels
Qinfeng Shi, James Petterson, Gideon Dror, John Langford, Alex Smola, Alex Strehl, S. V. N. Vishwanathan Infinite Hierarchical Hidden Markov Models
Katherine Heller, Yee Whye Teh, Dilan Gorur Inverse Optimal Heuristic Control for Imitation Learning
Nathan Ratliff, Brian Ziebart, Kevin Peterson, J. Andrew Bagnell, Martial Hebert, Anind K. Dey, Siddhartha Srinivasa Kernel Learning by Unconstrained Optimization
Fuxin Li, Yunshan Fu, Yu-Hong Dai, Cristian Sminchisescu, Jue Wang Latent Force Models
Mauricio Álvarez, David Luengo, Neil D. Lawrence Learning Low Density Separators
Shai Ben-David, Tyler Lu, David Pal, Miroslava Sotakova Markov Topic Models
Chong Wang, Bo Thiesson, Chris Meek, David Blei Matching Pursuit Kernel Fisher Discriminant Analysis
Tom Diethe, Zakria Hussain, David Hardoon, John Shawe-Taylor Multi-Manifold Semi-Supervised Learning
Andrew Goldberg, Xiaojin Zhu, Aarti Singh, Zhiting Xu, Robert Nowak Non-Negative Semi-Supervised Learning
Changhu Wang, Shuicheng Yan, Lei Zhang, Hongjiang Zhang Particle Belief Propagation
Alexander Ihler, David McAllester Relative Novelty Detection
Alex Smola, Le Song, Choon Hui Teo Sampling Techniques for the Nystrom Method
Sanjiv Kumar, Mehryar Mohri, Ameet Talwalkar Speed and Sparsity of Regularized Boosting
Yongxin Xi, Zhen Xiang, Peter Ramadge, Robert Schapire The Block Diagonal Infinite Hidden Markov Model
Thomas Stepleton, Zoubin Ghahramani, Geoffrey Gordon, Tai-Sing Lee Tighter and Convex Maximum Margin Clustering
Yu-Feng Li, Ivor W. Tsang, Jame Kwok, Zhi-Hua Zhou Variable Metric Stochastic Approximation Theory
Peter Sunehag, Jochen Trumpf, S.V.N. Vishwanathan, Nicol Schraudolph Variational Inference for the Indian Buffet Process
Finale Doshi, Kurt Miller, Jurgen Van Gael, Yee Whye Teh Visualization Databases for the Analysis of Large Complex Datasets
Saptarshi Guha, Paul Kidwell, Ryan P. Hafen, William S. Cleveland