Multi-Armed Bandits : Theory and Applications to Online Learning in Networks

Qing Zhao

ISBN 10: 3031792882 / ISBN 13: 9783031792885

Editore: Springer International Publishing, 2019

Riguardo questo articolo

Druck auf Anfrage Neuware - Printed after ordering - Multi-armed bandit problems pertain to optimal sequential decision making and learning in unknown environments. Since the first bandit problem posed by Thompson in 1933 for the application of clinical trials, bandit problems have enjoyed lasting attention from multiple research communities and have found a wide range of applications across diverse domains. This book covers classic results and recent development on both Bayesian and frequentist bandit problems. We start in Chapter 1 with a brief overview on the history of bandit problems, contrasting the two schools-Bayesian and frequentist-of approaches and highlighting foundational results and key applications. Chapters 2 and 4 cover, respectively, the canonical Bayesian and frequentist bandit models. In Chapters 3 and 5, we discuss major variants of the canonical bandit models that lead to new directions, bring in new techniques, and broaden the applications of this classical problem. In Chapter 6, we present several representative application examples in communication networks and social-economic systems, aiming to illuminate the connections between the Bayesian and the frequentist formulations of bandit problems and how structural results pertaining to one may be leveraged to obtain solutions under the other.

Codice articolo 9783031792885

Dati bibliografici

• Titolo: Multi-Armed Bandits : Theory and Applications to Online Learning in Networks
• Editore: Springer International Publishing
• Data di pubblicazione: 2019
• Lingua: Inglese
• ISBN 10: 3031792882
• ISBN 13: 9783031792885
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 235 L x 191 A x 10 P (millimetri) 235 millimetri di larghezza per 191 millimetri di altezza per 10 millimetri di profondità
• Peso: 327 grammi

Descrizione articolo

Riassunto

Multi-armed bandit problems pertain to optimal sequential decision making and learning in unknown environments. Since the first bandit problem posed by Thompson in 1933 for the application of clinical trials, bandit problems have enjoyed lasting attention from multiple research communities and have found a wide range of applications across diverse domains. This book covers classic results and recent development on both Bayesian and frequentist bandit problems. We start in Chapter 1 with a brief overview on the history of bandit problems, contrasting the two schools—Bayesian and frequentist—of approaches and highlighting foundational results and key applications. Chapters 2 and 4 cover, respectively, the canonical Bayesian and frequentist bandit models. In Chapters 3 and 5, we discuss major variants of the canonical bandit models that lead to new directions, bring in new techniques, and broaden the applications of this classical problem. In Chapter 6, we present several representative application examples in communication networks and social-economic systems, aiming to illuminate the connections between the Bayesian and the frequentist formulations of bandit problems and how structural results pertaining to one may be leveraged to obtain solutions under the other.

Informazioni sull?autore

Qing Zhao is a Joseph C. Ford Professor of Engineering at Cornell University. Prior to that, she was a Professor in the ECE Department at University of California, Davis. She received a Ph.D. in Electrical Engineering from Cornell in 2001. Her research interests include sequential decision theory, stochastic optimization, machine learning, and algorithmic theory with applications in infrastructure, communications, and social-economic networks. She is a Fellow of IEEE, a Distinguished Lecturer of the IEEE Signal Processing Society, a Marie Skłodowska-Curie Fellow of the European Union Research and Innovation program, and a Jubilee Chair Professor of Chalmers University during her 2018-2019 sabbatical leave. She received the 2010 IEEE Signal Processing Magazine Best Paper Award and the 2000 Young Author Best Paper Award from IEEE Signal Processing Society.