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Survey Bandits with Regret Guarantees

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  • Sanath Kumar Krishnamurthy
  • Susan Athey

Abstract

We consider a variant of the contextual bandit problem. In standard contextual bandits, when a user arrives we get the user's complete feature vector and then assign a treatment (arm) to that user. In a number of applications (like healthcare), collecting features from users can be costly. To address this issue, we propose algorithms that avoid needless feature collection while maintaining strong regret guarantees.

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  • Sanath Kumar Krishnamurthy & Susan Athey, 2020. "Survey Bandits with Regret Guarantees," Papers 2002.09814, arXiv.org.
    • Handle: RePEc:arx:papers:2002.09814
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    File URL: http://arxiv.org/pdf/2002.09814
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    Cited by:

    1. Danielle Li & Lindsey R. Raymond & Peter Bergman, 2020. "Hiring as Exploration," NBER Working Papers 27736, National Bureau of Economic Research, Inc.
    2. Claudio Cardoso Flores & Marcelo Cunha Medeiros, 2020. "Online Action Learning in High Dimensions: A Conservative Perspective," Papers 2009.13961, arXiv.org, revised Mar 2024.

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