skip to main content
research-article

Book announcement: Introduction to Multi-Armed Bandits

Published:02 December 2020Publication History
Skip Abstract Section

Abstract

"Introduction to multi-armed bandits" is a broad and accessible textbook which emphasizes connections to economics and operations research.

References

  1. Berry, D. A. and Fristedt, B. 1985. Bandit problems: sequential allocation of experiments. Springer, Heidelberg, Germany.Google ScholarGoogle Scholar
  2. Bubeck, S. and Cesa-Bianchi, N. 2012. Regret Analysis of Stochastic and Non-stochastic Multi-armed Bandit Problems. Foundations and Trends in Machine Learning 5, 1, 1--122. Published with Now Publishers (Boston, MA, USA). Also available at https://arxiv.org/abs/1204.5721.Google ScholarGoogle ScholarCross RefCross Ref
  3. Cesa-Bianchi, N. and Lugosi, G. 2006. Prediction, learning, and games. Cambridge University Press, Cambridge, UK. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Gittins, J., Glazebrook, K., and Weber, R. 2011. Multi-Armed Bandit Allocation Indices, 2nd ed. John Wiley & Sons, Hoboken, NJ, USA. The first edition, single-authored by John Gittins, has been published in 1989.Google ScholarGoogle Scholar
  5. Hazan, E. 2015. Introduction to Online Convex Optimization. Foundations and Trends? in Optimization 2, 3--4, 157--325. Published with Now Publishers (Boston, MA, USA). Also available at https://arxiv.org/abs/1909.05207. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Lattimore, T. and Szepesvari, C. 2020. Bandit Algorithms. Cambridge University Press, Cambridge, UK. Preprint, to be published in 2020. Versions available at https://banditalgs.com/ since 2018.Google ScholarGoogle Scholar
  7. Russo, D., Roy, B. V., Kazerouni, A., Osband, I., and Wen, Z. 2018. A tutorial on thompson sampling. Foundations and Trends in Machine Learning 11, 1, 1--96. Published with Now Publishers (Boston, MA, USA). Also available at https://arxiv.org/abs/1707.02038. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Slivkins, A. 2019. Introduction to multi-armed bandits. Foundations and Trends@ in Machine Learning 12, 1--2 (Nov.), 1--286. Published with Now Publishers (Boston, MA, USA). Also available at https://arxiv.org/abs/1904.07272.Google ScholarGoogle Scholar

Recommendations

Comments

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Sign in

Full Access

  • Published in

    cover image ACM SIGecom Exchanges
    ACM SIGecom Exchanges  Volume 18, Issue 1
    July 2020
    40 pages
    EISSN:1551-9031
    DOI:10.1145/3440959
    Issue’s Table of Contents

    Copyright © 2020 Copyright is held by the owner/author(s)

    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 2 December 2020

    Check for updates

    Qualifiers

    • research-article

PDF Format

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader