Learning Theory

Sparseness vs Estimating Conditional Probabilities: Some Asymptotic Results JMLR 2007 Stability Bounds for Non-i.i.d. Processes NeurIPS 2007 Statistical Analysis of Semi-Supervised Regression NeurIPS 2007 Structured Learning with Approximate Inference NeurIPS 2007 The Epoch-Greedy Algorithm for Multi-armed Bandits with Side Information NeurIPS 2007 The Tradeoffs of Large Scale Learning NeurIPS 2007 The Value of Labeled and Unlabeled Examples when the Model is Imperfect NeurIPS 2007 Transfer Learning using Kolmogorov Complexity: Basic Theory and Empirical Evaluations NeurIPS 2007 Unlabeled Compression Schemes for Maximum Classes JMLR 2007 VC Theory of Large Margin Multi-Category Classifiers JMLR 2007 Active learning for misspecified generalized linear models NeurIPS 2006 AdaBoost is Consistent NeurIPS 2006 An Oracle Inequality for Clipped Regularized Risk Minimizers NeurIPS 2006 A PAC-Bayes Risk Bound for General Loss Functions NeurIPS 2006 A Robust Procedure For Gaussian Graphical Model Search From Microarray Data With p Larger Than n JMLR 2006 Attribute-efficient learning of decision lists and linear threshold functions under unconcentrated distributions NeurIPS 2006 Consistency and Convergence Rates of One-Class SVMs and Related Algorithms JMLR 2006 Consistency of Multiclass Empirical Risk Minimization Methods Based on Convex Loss JMLR 2006 Estimation of Gradients and Coordinate Covariation in Classification JMLR 2006 Fundamental Limitations of Spectral Clustering NeurIPS 2006 iLSTD: Eligibility Traces and Convergence Analysis NeurIPS 2006 implicit Online Learning with Kernels NeurIPS 2006 Inductive Synthesis of Functional Programs: An Explanation Based Generalization Approach JMLR 2006 Learnability and the doubling dimension NeurIPS 2006 Learning a Hidden Hypergraph JMLR 2006