Survey Bandits with Regret Guarantees

Sanath Kumar Krishnamurthy and Susan Athey

Papers from arXiv.org

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.

Date: 2020-02
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Citations: View citations in EconPapers (2)

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