Introduction to Multi-Armed Bandits

now publishers Inc
  • erschienen am 7. November 2019
  • Buch
  • |
  • Softcover
  • |
  • 308 Seiten
978-1-68083-620-2 (ISBN)
Multi-armed bandits is a rich, multi-disciplinary area that has been studied since 1933, with a surge of activity in the past 10-15 years. This is the first monograph to provide a textbook like treatment of the subject.
The work on multi-armed bandits can be partitioned into a dozen or so directions. Each chapter tackles one line of work, providing a self-contained introduction and pointers for further reading. Introduction to Multi-Armed Bandits concentrates on fundamental ideas and elementary, teachable proofs over the strongest possible results. It emphasizes accessibility of the material; while exposure to machine learning and probability/statistics would certainly help, a standard undergraduate course on algorithms should suffice for background.
The first four chapters are devoted IID rewards with adversarial rewards being covered in the next 3 chapters. Contextual bandits are discussed in a separate chapter before the monograph concludes with connections to economics. Each chapter contains a section on bibliographic notes and further directions. Many of the chapters conclude with some exercises.
Introduction to Multi-Armed Bandits provides an accessible treatment for students of a topic that has gained importance in the last decade. Lecturers can use it as a text for an introductory course on the subject.
  • Englisch
  • Hanover
  • |
  • USA
  • Für Beruf und Forschung
  • Höhe: 234 mm
  • |
  • Breite: 156 mm
  • |
  • Dicke: 16 mm
  • 471 gr
978-1-68083-620-2 (9781680836202)
weitere Ausgaben werden ermittelt
Introduction: Scope and Motivation
1. Stochastic Bandits
2. Lower Bounds
3. Bayesian Bandits and Thompson Sampling
4. Lipschitz Bandits
5. Full Feedback and Adversarial Costs
6. Adversarial Bandits
7. Linear Costs and Semi-bandits
8. Contextual Bandits
9. Bandits and Games
10. Bandits with Knapsacks
11. Bandits and Incentives

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