Bandit Algorithms

·
· Cambridge University Press
E-Book
538
Seiten

Über dieses E-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.
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 stochas...

Mehr entdecken

Autoren-Profil

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.

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...

Dieses E-Book bewerten

Deine Meinung ist gefragt!

Informationen zum Lesen

Smartphones und Tablets
Nachdem du die Google Play Bücher App für Android und iPad/iPhone installiert hast, wird diese automatisch mit deinem Konto synchronisiert, sodass du auch unterwegs online und offline lesen kannst.
Laptops und Computer
Im Webbrowser auf deinem Computer kannst du dir Hörbucher anhören, die du bei Google Play gekauft hast.
E-Reader und andere Geräte
Wenn du Bücher auf E-Ink-Geräten lesen möchtest, beispielsweise auf einem Kobo eReader, lade eine Datei herunter und übertrage sie auf dein Gerät. Eine ausführliche Anleitung zum Übertragen der Dateien auf unterstützte E-Reader findest du in der Hilfe.