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The Budgeted Multi-armed Bandit Problem

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Learning Theory (COLT 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3120))

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Abstract

The following coins problem is a version of a multi-armed bandit problem where one has to select from among a set of objects, say classifiers, after an experimentation phase that is constrained by a time or cost budget. The question is how to spend the budget. The problem involves pure exploration only, differentiating it from typical multi-armed bandit problems involving an exploration/exploitation tradeoff [BF85]. It is an abstraction of the following scenarios: choosing from among a set of alternative treatments after a fixed number of clinical trials, determining the best parameter settings for a program given a deadline that only allows a fixed number of runs; or choosing a life partner in the bachelor/bachelorette TV show where time is limited. We are interested in the computational complexity of the coins problem and/or efficient algorithms with approximation guarantees.

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References

  1. Berry, D., Fristedt, B.: Bandit Problems: Sequential Allocation of Experiments. Chapman and Hall, NewYork (1985)

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  2. Lizotte, D., Madani, O., Greiner, R.: Budgeted learning of Naive Bayes classifiers. In: UAI 2003 (2003)

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  3. Madani, O., Lizotte, D., Greiner, R.: Active model selection (submitted). Technical report, University of Alberta and AICML (2004), http://www.cs.ualberta.ca/~madani/budget.html

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Author information

Authors and Affiliations

  1. Yahoo! Research Labs, 74 N. Pasadena Ave, Pasadena, CA, 91101, USA

    Omid Madani

  2. Dept. of Computing Science, University of Alberta, Edmonton, T6J 2E8

    Daniel J. Lizotte & Russell Greiner

Authors
  1. Omid Madani
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  2. Daniel J. Lizotte
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  3. Russell Greiner
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Editor information

Editors and Affiliations

  1. The Centre for Computational Statistics and Machine Learning Department of Computer Science, University College London, Gower St., WC1E 6BT, London

    John Shawe-Taylor

  2. Google, 1600 Amphitheater Parkway, CA 94043, Mountain View, USA

    Yoram Singer

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© 2004 Springer-Verlag Berlin Heidelberg

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Madani, O., Lizotte, D.J., Greiner, R. (2004). The Budgeted Multi-armed Bandit Problem. In: Shawe-Taylor, J., Singer, Y. (eds) Learning Theory. COLT 2004. Lecture Notes in Computer Science(), vol 3120. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27819-1_46

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  • DOI: https://doi.org/10.1007/978-3-540-27819-1_46

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22282-8

  • Online ISBN: 978-3-540-27819-1

  • eBook Packages: Springer Book Archive

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