statistical learning theory

Complexity Theoretic Lower Bounds for Sparse Principal Component Detection COLT 2013 Estimation of Extreme Values and Associated Level Sets of a Regression Function via Selective Sampling COLT 2013 Honest Compressions and Their Application to Compression Schemes COLT 2013 Information-theoretic lower bounds for distributed statistical estimation with communication constraints NeurIPS 2013 Local Privacy and Minimax Bounds: Sharp Rates for Probability Estimation NeurIPS 2013 Moment-based Uniform Deviation Bounds for k-means and Friends NeurIPS 2013 Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima NeurIPS 2013 Mixability in Statistical Learning NeurIPS 2012 Learning with the weighted trace-norm under arbitrary sampling distributions NeurIPS 2011 Sequential Event Prediction with Association Rules COLT 2011 Learnability, Stability and Uniform Convergence JMLR 2010 On Herding and the Perceptron Cycling Theorem NeurIPS 2010 On the Theory of Learnining with Privileged Information NeurIPS 2010 Permutation Complexity Bound on Out-Sample Error NeurIPS 2010 Human Active Learning NeurIPS 2008 Performance analysis for L\_2 kernel classification NeurIPS 2008 Sparsity of SVMs that use the epsilon-insensitive loss NeurIPS 2008 Consistent Minimization of Clustering Objective Functions NeurIPS 2007 Progressive mixture rules are deviation suboptimal NeurIPS 2007 An Oracle Inequality for Clipped Regularized Risk Minimizers NeurIPS 2006 Consistency of Multiclass Empirical Risk Minimization Methods Based on Convex Loss JMLR 2006 Learning from Multiple Sources NeurIPS 2006 PAC-Bayes Bounds for the Risk of the Majority Vote and the Variance of the Gibbs Classifier NeurIPS 2006 Tighter PAC-Bayes Bounds NeurIPS 2006 A Compression Approach to Support Vector Model Selection JMLR 2004