Bandit Algorithms

Name: Bandit Algorithms
Brand: Atlantic Books
SKU: 9781108486828
Price: 5967.0 INR
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by Tor Lattimore , Csaba Szepesvári

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ISBN13: 9781108486828
Binding: Hardcover
Subject: N/A
Publisher: Cambridge University Press
Publisher Imprint: Cambridge University Press
Publication Date: July 16, 2020
Pages: 536
Original Price: USD 59.0
Language: English
Edition: N/A
Item Weight: 1044 grams
BISAC Subject(s): Artificial Intelligence / Computer Vision & Pattern Recognition

About The Book

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.

About The Author

Lattimore, Tor: - 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.

Szepesvári, Csaba: - 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.

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