Algorithmic Learning Theory
26th International Conference, ALT 2015, Banff, AB, Canada, October 4-6, 2015, Proceedings
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Beschreibung
This book constitutes the proceedings of the 26th International Conference on Algorithmic Learning Theory, ALT 2015, held in Banff, AB, Canada, in October 2015, and co-located with the 18th International Conference on Discovery Science, DS 2015. The 23 full papers presented in this volume were carefully reviewed and selected from 44 submissions. In addition the book contains 2 full papers summarizing the invited talks and 2 abstracts of invited talks. The papers are organized in topical sections named: inductive inference; learning from queries, teaching complexity; computational learning theory and algorithms; statistical learning theory and sample complexity; online learning, stochastic optimization; and Kolmogorov complexity, algorithmic information theory.
Autor*in
Kamalika Chaudhuri
Themen in »Algorithmic Learning Theory«
Active learning Boolean function learning Inductive inference Machine learning theory Markov Decision Processes Models of learning Online learning theory Query learning Regret bounds Reinforcement learning Unsupervised learning sample complexity generalization bounds algorithmic learning theory computational complexity