Algorithmic Learning Theory

Invited Papers.- The Two Faces of Active Learning.- Inference and Learning in Planning.- Mining Heterogeneous Information Networks by Exploring the Power of Links.- Learning and Domain Adaptation.- Learning on the Web.- Regular Contributions.- Prediction with Expert Evaluators’ Advice.- Pure Exploration in Multi-armed Bandits Problems.- The Follow Perturbed Leader Algorithm Protected from Unbounded One-Step Losses.- Computable Bayesian Compression for Uniformly Discretizable Statistical Models.- Calibration and Internal No-Regret with Random Signals.- St. Petersburg Portfolio Games.- Reconstructing Weighted Graphs with Minimal Query Complexity.- Learning Unknown Graphs.- Completing Networks Using Observed Data.- Average-Case Active Learning with Costs.- Canonical Horn Representations and Query Learning.- Learning Finite Automata Using Label Queries.- Characterizing Statistical Query Learning: Simplified Notions and Proofs.- An Algebraic Perspective on Boolean Function Learning.- Adaptive Estimation of the Optimal ROC Curve and a Bipartite Ranking Algorithm.- Complexity versus Agreement for Many Views.- Error-Correcting Tournaments.- Difficulties in Forcing Fairness of Polynomial Time Inductive Inference.- Learning Mildly Context-Sensitive Languages with Multidimensional Substitutability from Positive Data.- Uncountable Automatic Classes and Learning.- Iterative Learning from Texts and Counterexamples Using Additional Information.- Incremental Learning with Ordinal Bounded Example Memory.- Learning from Streams.- Smart PAC-Learners.- Approximation Algorithms for Tensor Clustering.- Agnostic Clustering.