Statistical Learning

Learning Multiple Tasks with a Sparse Matrix-Normal Penalty NeurIPS 2010 Learning with Blocks: Composite Likelihood and Contrastive Divergence AISTATS 2010 Linear readout from a neural population with partial correlation data NeurIPS 2010 LSTD with Random Projections NeurIPS 2010 Maximum Likelihood in Cost-Sensitive Learning: Model Specification, Approximations, and Upper Bounds JMLR 2010 Model Selection: Beyond the Bayesian/Frequentist Divide JMLR 2010 Multi-Stage Dantzig Selector NeurIPS 2010 Multivariate Dyadic Regression Trees for Sparse Learning Problems NeurIPS 2010 Near-Optimal Bayesian Active Learning with Noisy Observations NeurIPS 2010 On Finding Predictors for Arbitrary Families of Processes JMLR 2010 Online Learning: Random Averages, Combinatorial Parameters, and Learnability NeurIPS 2010 On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation JMLR 2010 On the Rate of Convergence of the Bagged Nearest Neighbor Estimate JMLR 2010 On the Theory of Learnining with Privileged Information NeurIPS 2010 Optimal learning rates for Kernel Conjugate Gradient regression NeurIPS 2010 Pairwise Measures of Causal Direction in Linear Non-Gaussian Acyclic Models ACML 2010 Permutation Complexity Bound on Out-Sample Error NeurIPS 2010 Permutation Tests for Studying Classifier Performance JMLR 2010 Practical Approaches to Principal Component Analysis in the Presence of Missing Values JMLR 2010 Predictive State Temporal Difference Learning NeurIPS 2010 Rademacher Complexities and Bounding the Excess Risk in Active Learning JMLR 2010 Rate Minimaxity of the Lasso and Dantzig Selector for the Loss in Balls JMLR 2010 Rates of convergence for the cluster tree NeurIPS 2010 Reduced-Rank Hidden Markov Models AISTATS 2010 Regularized estimation of image statistics by Score Matching NeurIPS 2010