Statistical Learning

Computing Maximum Likelihood Estimates in Recursive Linear Models with Correlated Errors JMLR 2009 Consistency and Localizability JMLR 2009 Correlation Coefficients are Insufficient for Analyzing Spike Count Dependencies NeurIPS 2009 Data-driven calibration of linear estimators with minimal penalties NeurIPS 2009 Differential Use of Implicit Negative Evidence in Generative and Discriminative Language Learning NeurIPS 2009 Discrete MDL Predicts in Total Variation NeurIPS 2009 Distribution-Calibrated Hierarchical Classification NeurIPS 2009 Distribution Matching for Transduction NeurIPS 2009 Efficient Moments-based Permutation Tests NeurIPS 2009 Entropy Inference and the James-Stein Estimator, with Application to Nonlinear Gene Association Networks JMLR 2009 Fast Learning from Non-i.i.d. Observations NeurIPS 2009 Fast, smooth and adaptive regression in metric spaces NeurIPS 2009 From PAC-Bayes Bounds to KL Regularization NeurIPS 2009 Hierarchical Modeling of Local Image Features through L_p-Nested Symmetric Distributions NeurIPS 2009 Human Rademacher Complexity NeurIPS 2009 Information-theoretic lower bounds on the oracle complexity of convex optimization NeurIPS 2009 Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions NeurIPS 2009 Kernels and learning curves for Gaussian process regression on random graphs NeurIPS 2009 Learning Approximate Sequential Patterns for Classification JMLR 2009 Local Rules for Global MAP: When Do They Work ? NeurIPS 2009 Lower bounds on minimax rates for nonparametric regression with additive sparsity and smoothness NeurIPS 2009 Matrix Completion from Noisy Entries NeurIPS 2009 Maximum Entropy Discrimination Markov Networks JMLR 2009 Nearest Neighbor Clustering: A Baseline Method for Consistent Clustering with Arbitrary Objective Functions JMLR 2009 On Uniform Deviations of General Empirical Risks with Unboundedness, Dependence, and High Dimensionality JMLR 2009