Bandit Algorithms (Hardcover) (ISBN-13: 9781108486828)

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.

Author: Tor Lattimore, Csaba Szepesv疵i
ISBN-10: 1108486827
ISBN-13: 9781108486828
Publisher: Cambridge University Press
Language: English
Published: 07/16/2020
Pages: 536
Format: Hardcover
Weight: 2.30lbs
Size: 9.80h x 7.70w x 1.30d