Bandit algorithms /

Bandit algorithms /

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Bibliographic Details
Author / Creator:	Lattimore, Tor, 1987- author.
Imprint:	Cambridge, United Kingdom ; New York, NY : Cambridge University Press, 2020. ©2020
Description:	xviii, 518 pages : illustrations ; 26 cm
Language:	English
Subject:	Mathematical optimization. Probabilities. Decision making -- Mathematical models. Resource allocation -- Mathematical models. Algorithms. Computer algorithms. Algorithms. Decision making -- Mathematical models. Mathematical optimization. Probabilities. Resource allocation -- Mathematical models.
Format:	Print Book
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/13141483

Hidden Bibliographic Details
Other authors / contributors:	Szepesvári, Csaba, author.
ISBN:	9781108486828 1108486827 9781108571401
Notes:	Includes bibliographical references and index.
Summary:	"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"--
Other form:	Online version: Lattimore, Tor, 1987- Bandit algorithms Cambridge ; New York, NY : Cambridge University Press, 2020 9781108571401

Eckhart

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Holdings details from Eckhart
Call Number:	QA402.5.L367 2020
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