learning theory

AdaBoost is Consistent NeurIPS 2006 Fundamental Limitations of Spectral Clustering NeurIPS 2006 Learnability and the doubling dimension NeurIPS 2006 On Transductive Regression NeurIPS 2006 PAC-Bayes Bounds for the Risk of the Majority Vote and the Variance of the Gibbs Classifier NeurIPS 2006 Shifting, One-Inclusion Mistake Bounds and Tight Multiclass Expected Risk Bounds NeurIPS 2006 Stability of K-Means Clustering NeurIPS 2006 Stability Properties of Empirical Risk Minimization over Donsker Classes JMLR 2006 Tighter PAC-Bayes Bounds NeurIPS 2006 Asymptotics in Empirical Risk Minimization JMLR 2005 Learning from Examples as an Inverse Problem JMLR 2005 Universal Algorithms for Learning Theory Part I : Piecewise Constant Functions JMLR 2005 Generalization Error Bounds for Threshold Decision Lists JMLR 2004 Optimality of Universal Bayesian Sequence Prediction for General Loss and Alphabet JMLR 2003 On the Size of Convex Hulls of Small Sets JMLR 2001