Statistical Learning

Unlabeled data: Now it helps, now it doesn't NeurIPS 2008 Weighted Sums of Random Kitchen Sinks: Replacing minimization with randomization in learning NeurIPS 2008 AdaBoost is Consistent JMLR 2007 A general agnostic active learning algorithm NeurIPS 2007 A Kernel Statistical Test of Independence NeurIPS 2007 An online Hebbian learning rule that performs Independent Component Analysis NeurIPS 2007 A Probabilistic Analysis of EM for Mixtures of Separated, Spherical Gaussians JMLR 2007 A Risk Minimization Principle for a Class of Parzen Estimators NeurIPS 2007 A Unified Near-Optimal Estimator For Dimension Reduction in l_α (0<α≤ 2) Using Stable Random Projections NeurIPS 2007 Bayesian binning beats approximate alternatives: estimating peri-stimulus time histograms NeurIPS 2007 Bayesian Quadratic Discriminant Analysis JMLR 2007 Catching Up Faster in Bayesian Model Selection and Model Averaging NeurIPS 2007 Cluster Stability for Finite Samples NeurIPS 2007 Combining PAC-Bayesian and Generic Chaining Bounds JMLR 2007 Consistent Minimization of Clustering Objective Functions NeurIPS 2007 Cooled and Relaxed Survey Propagation for MRFs NeurIPS 2007 Covariate Shift Adaptation by Importance Weighted Cross Validation JMLR 2007 Density Estimation under Independent Similarly Distributed Sampling Assumptions NeurIPS 2007 Estimating disparity with confidence from energy neurons NeurIPS 2007 Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization NeurIPS 2007 Expectation Maximization and Posterior Constraints NeurIPS 2007 Generalization Error Bounds in Semi-supervised Classification Under the Cluster Assumption JMLR 2007 Kernel Measures of Conditional Dependence NeurIPS 2007 Learning with Transformation Invariant Kernels NeurIPS 2007 Learning with Tree-Averaged Densities and Distributions NeurIPS 2007