Learning Theory

Near-optimal Regret Bounds for Reinforcement Learning JMLR 2010 Negative Results for Active Learning with Convex Losses AISTATS 2010 On Herding and the Perceptron Cycling Theorem NeurIPS 2010 On Learning with Integral Operators JMLR 2010 Online Learning: Random Averages, Combinatorial Parameters, and Learnability NeurIPS 2010 Online Markov Decision Processes under Bandit Feedback NeurIPS 2010 On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation JMLR 2010 On the Impact of Kernel Approximation on Learning Accuracy AISTATS 2010 On the Theory of Learnining with Privileged Information NeurIPS 2010 Optimal learning rates for Kernel Conjugate Gradient regression NeurIPS 2010 PAC-Bayesian Model Selection for Reinforcement Learning NeurIPS 2010 Parametric Bandits: The Generalized Linear Case NeurIPS 2010 Permutation Complexity Bound on Out-Sample Error NeurIPS 2010 Rademacher Complexities and Bounding the Excess Risk in Active Learning JMLR 2010 Random Projection Trees Revisited NeurIPS 2010 Rate Minimaxity of the Lasso and Dantzig Selector for the Loss in Balls JMLR 2010 Rates of convergence for the cluster tree NeurIPS 2010 Reduced-Rank Hidden Markov Models AISTATS 2010 Regret Bounds and Minimax Policies under Partial Monitoring JMLR 2010 Regret Bounds for Gaussian Process Bandit Problems AISTATS 2010 Relating Function Class Complexity and Cluster Structure in the Function Domain with Applications to Transduction AISTATS 2010 Risk Bounds for Levy Processes in the PAC-Learning Framework AISTATS 2010 Sample Complexity of Testing the Manifold Hypothesis NeurIPS 2010 Scrambled Objects for Least-Squares Regression NeurIPS 2010 Semi-Supervised Learning with Adversarially Missing Label Information NeurIPS 2010