Learning Theory

The Sample Complexity of Exploration in the Multi-Armed Bandit Problem JMLR 2004 An Introduction to Variable and Feature Selection JMLR 2003 Extensions to Metric-Based Model Selection JMLR 2003 Generalization Error Bounds for Bayesian Mixture Algorithms JMLR 2003 Greedy Algorithms for Classification -- Consistency, Convergence Rates, and Adaptivity JMLR 2003 Learning Rates for Q-learning JMLR 2003 On Inclusion-Driven Learning of Bayesian Networks JMLR 2003 On the Performance of Kernel Classes JMLR 2003 On the Proper Learning of Axis-Parallel Concepts JMLR 2003 On the Rate of Convergence of Regularized Boosting Classifiers JMLR 2003 Optimality of Universal Bayesian Sequence Prediction for General Loss and Alphabet JMLR 2003 Optimally-Smooth Adaptive Boosting and Application to Agnostic Learning JMLR 2003 Relational Learning as Search in a Critical Region JMLR 2003 Smooth Boosting and Learning with Malicious Noise JMLR 2003 Sparseness of Support Vector Machines JMLR 2003 Ultraconservative Online Algorithms for Multiclass Problems JMLR 2003 Variable Selection Using SVM-based Criteria JMLR 2003 Algorithmic Luckiness JMLR 2002 Covering Number Bounds of Certain Regularized Linear Function Classes JMLR 2002 Data-dependent margin-based generalization bounds for classification JMLR 2002 Finding the Most Interesting Patterns in a Database Quickly by Using Sequential Sampling JMLR 2002 Learning Monotone DNF from a Teacher that Almost Does Not Answer Membership Queries JMLR 2002 Limitations of Learning Via Embeddings in Euclidean Half Spaces JMLR 2002 Machine Learning with Data Dependent Hypothesis Classes JMLR 2002 On Boosting with Polynomially Bounded Distributions JMLR 2002