Learning Theory

A Risk Minimization Principle for a Class of Parzen Estimators NeurIPS 2007 Attribute-Efficient and Non-adaptive Learning of Parities and DNF Expressions JMLR 2007 Boosting the Area under the ROC Curve NeurIPS 2007 Bundle Methods for Machine Learning NeurIPS 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 Computational Equivalence of Fixed Points and No Regret Algorithms, and Convergence to Equilibria NeurIPS 2007 Convex Learning with Invariances NeurIPS 2007 Dynamics and Generalization Ability of LVQ Algorithms JMLR 2007 FilterBoost: Regression and Classification on Large Datasets NeurIPS 2007 Fitted Q-iteration in continuous action-space MDPs NeurIPS 2007 From External to Internal Regret JMLR 2007 Learnability of Gaussians with Flexible Variances JMLR 2007 Learning Bounds for Domain Adaptation NeurIPS 2007 Online Linear Regression and Its Application to Model-Based Reinforcement Learning NeurIPS 2007 On Ranking in Survival Analysis: Bounds on the Concordance Index NeurIPS 2007 On the Effectiveness of Laplacian Normalization for Graph Semi-supervised Learning JMLR 2007 Optimistic Linear Programming gives Logarithmic Regret for Irreducible MDPs NeurIPS 2007 Polynomial Identification in the Limit of Substitutable Context-free Languages JMLR 2007 Progressive mixture rules are deviation suboptimal NeurIPS 2007 Regret Minimization in Games with Incomplete Information NeurIPS 2007 Revised Loss Bounds for the Set Covering Machine and Sample-Compression Loss Bounds for Imbalanced Data JMLR 2007 Selecting Observations against Adversarial Objectives NeurIPS 2007 Separating Models of Learning from Correlated and Uncorrelated Data JMLR 2007