Bandit Algorithms

Lattimore, Tor/ Szepesva ri, Csaba

ISBN 10: 1108486827 / ISBN 13: 9781108486828

Verlag: Cambridge Univ Pr, 2020

Beschreibung

517 pages. 9.50x7.00x1.25 inches. In Stock. This item is printed on demand. Bestandsnummer des Verkäufers __1108486827

Bibliografische Details

Titel: Bandit Algorithms

Verlag: Cambridge Univ Pr

Erscheinungsdatum: 2020

Einband: Hardcover

Zustand: Brand New

Über diesen Titel

Inhaltsangabe:

Decision-making in the face of uncertainty is a significant challenge in machine learning, and the multi-armed bandit model is a commonly used framework to address it. This comprehensive and rigorous introduction to the multi-armed bandit problem examines all the major settings, including stochastic, adversarial, and Bayesian frameworks. A focus on both mathematical intuition and carefully worked proofs makes this an excellent reference for established researchers and a helpful resource for graduate students in computer science, engineering, statistics, applied mathematics and economics. Linear bandits receive special attention as one of the most useful models in applications, while other chapters are dedicated to combinatorial bandits, ranking, non-stationary problems, Thompson sampling and pure exploration. The book ends with a peek into the world beyond bandits with an introduction to partial monitoring and learning in Markov decision processes.

Über die Autorinnen und Autoren:

Tor Lattimore is a research scientist at DeepMind. His research is focused on decision making in the face of uncertainty, including bandit algorithms and reinforcement learning. Before joining DeepMind he was an assistant professor at Indiana University and a postdoctoral fellow at the University of Alberta.

Csaba Szepesvári is a Professor in the Department of Computing Science at the University of Alberta and a Principal Investigator of the Alberta Machine Intelligence Institute. He also leads the 'Foundations' team at DeepMind. He has co-authored a book on nonlinear approximate adaptive controllers and authored a book on reinforcement learning, in addition to publishing over 200 journal and conference papers. He is an action editor of the Journal of Machine Learning Research.