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Bandit algorithms. (English) Zbl 1439.68002

Cambridge: Cambridge University Press (ISBN 978-1-108-48682-8/hbk; 978-1-108-57140-1/ebook). xviii, 518 p. (2020).
Publisher’s description: 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.

MSC:

68-02 Research exposition (monographs, survey articles) pertaining to computer science
60G40 Stopping times; optimal stopping problems; gambling theory
62L15 Optimal stopping in statistics
68T05 Learning and adaptive systems in artificial intelligence
68T10 Pattern recognition, speech recognition
90C40 Markov and semi-Markov decision processes
91A60 Probabilistic games; gambling
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