statistical learning

Domain Adaptation: A Small Sample Statistical Approach AISTATS 2012 Graphical Models via Generalized Linear Models NeurIPS 2012 Locating Changes in Highly Dependent Data with Unknown Number of Change Points NeurIPS 2012 Mixability in Statistical Learning NeurIPS 2012 On Ranking and Generalization Bounds JMLR 2012 Statistical Optimization in High Dimensions AISTATS 2012 Stochastic optimization and sparse statistical recovery: Optimal algorithms for high dimensions NeurIPS 2012 Estimating beta-mixing coefficients AISTATS 2011 How biased are maximum entropy models? NeurIPS 2011 Learning mixtures of Gaussians with maximum-a-posteriori oracle AISTATS 2011 On U-processes and clustering performance NeurIPS 2011 Learning Bounds for Importance Weighting NeurIPS 2010 Model Selection: Beyond the Bayesian/Frequentist Divide JMLR 2010 Rates of convergence for the cluster tree NeurIPS 2010 Regularized Discriminant Analysis, Ridge Regression and Beyond JMLR 2010 Sample Complexity of Testing the Manifold Hypothesis NeurIPS 2010 Scrambled Objects for Least-Squares Regression NeurIPS 2010 Sufficient covariates and linear propensity analysis AISTATS 2010 Tight Sample Complexity of Large-Margin Learning NeurIPS 2010 A Data-Driven Approach to Modeling Choice NeurIPS 2009 Asymptotically Optimal Regularization in Smooth Parametric Models NeurIPS 2009 AUC optimization and the two-sample problem NeurIPS 2009 Consistency and Localizability JMLR 2009 Controlling the False Discovery Rate of the Association/Causality Structure Learned with the PC Algorithm JMLR 2009 Fast Learning from Non-i.i.d. Observations NeurIPS 2009