Statistical Learning

Estimation of Information Theoretic Measures for Continuous Random Variables NeurIPS 2008 Fast Rates for Regularized Objectives NeurIPS 2008 From Online to Batch Learning with Cutoff-Averaging NeurIPS 2008 Generalization from Observed to Unobserved Features by Clustering JMLR 2008 Graphical Methods for Efficient Likelihood Inference in Gaussian Covariance Models JMLR 2008 High-dimensional support union recovery in multivariate regression NeurIPS 2008 Inferring rankings under constrained sensing NeurIPS 2008 Kernel Change-point Analysis NeurIPS 2008 Learning with Consistency between Inductive Functions and Kernels NeurIPS 2008 Localized Sliced Inverse Regression NeurIPS 2008 Model Selection in Gaussian Graphical Models: High-Dimensional Consistency of \boldmath$\ell_1$-regularized MLE NeurIPS 2008 Near-minimax recursive density estimation on the binary hypercube NeurIPS 2008 Nonparametric sparse hierarchical models describe V1 fMRI responses to natural images NeurIPS 2008 On the Complexity of Linear Prediction: Risk Bounds, Margin Bounds, and Regularization NeurIPS 2008 On the Design of Loss Functions for Classification: theory, robustness to outliers, and SavageBoost NeurIPS 2008 On the Generalization Ability of Online Strongly Convex Programming Algorithms NeurIPS 2008 On the Reliability of Clustering Stability in the Large Sample Regime NeurIPS 2008 Overlaying classifiers: a practical approach for optimal ranking NeurIPS 2008 Performance analysis for L\_2 kernel classification NeurIPS 2008 Phase transitions for high-dimensional joint support recovery NeurIPS 2008 Rademacher Complexity Bounds for Non-I.I.D. Processes NeurIPS 2008 Regularized Policy Iteration NeurIPS 2008 Risk Bounds for Randomized Sample Compressed Classifiers NeurIPS 2008 Signal-to-Noise Ratio Analysis of Policy Gradient Algorithms NeurIPS 2008 Sparsity of SVMs that use the epsilon-insensitive loss NeurIPS 2008