Papers
Universal Kernels
Charles A. Micchelli, Yuesheng Xu, Haizhang Zhang
A Robust Procedure For Gaussian Graphical Model Search From Microarray Data With p Larger Than n
Robert Castelo, Alberto Roverato
Linear State-Space Models for Blind Source Separation
Rasmus Kongsgaard Olsson, Lars Kai Hansen
Stability Properties of Empirical Risk Minimization over Donsker Classes
Andrea Caponnetto, Alexander Rakhlin
On Model Selection Consistency of Lasso
Peng Zhao, Bin Yu
Estimation of Gradients and Coordinate Covariation in Classification
Sayan Mukherjee, Qiang Wu
Bounds for the Loss in Probability of Correct Classification Under Model Based Approximation
Magnus Ekdahl, Timo Koski
Consistency of Multiclass Empirical Risk Minimization Methods Based on Convex Loss
Di-Rong Chen, Tao Sun
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
Mikhail Belkin, Partha Niyogi, Vikas Sindhwani
Learning Parts-Based Representations of Data
David A. Ross, Richard S. Zemel
Point-Based Value Iteration for Continuous POMDPs
Josep M. Porta, Nikos Vlassis, Matthijs T.J. Spaan et al.
Causal Graph Based Decomposition of Factored MDPs
Anders Jonsson, Andrew Barto
An Efficient Implementation of an Active Set Method for SVMs
Katya Scheinberg
Learning a Hidden Hypergraph
Dana Angluin, Jiang Chen
Adaptive Prototype Learning Algorithms: Theoretical and Experimental Studies
Fu Chang, Chin-Chin Lin, Chi-Jen Lu
A Hierarchy of Support Vector Machines for Pattern Detection
Hichem Sahbi, Donald Geman
Distance Patterns in Structural Similarity
Thomas Kä
Walk-Sums and Belief Propagation in Gaussian Graphical Models
Dmitry M. Malioutov, Jason K. Johnson, Alan S. Willsky
A Linear Non-Gaussian Acyclic Model for Causal Discovery
Shohei Shimizu, Patrik O. Hoyer, Aapo Hyvä
Learning Spectral Clustering, With Application To Speech Separation
Francis R. Bach, Michael I. Jordan