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Zero-Inflated Bandits

Author

Listed:
  • Haoyu Wei
  • Runzhe Wan
  • Lei Shi
  • Rui Song

Abstract

Many real-world bandit applications are characterized by sparse rewards, which can significantly hinder learning efficiency. Leveraging problem-specific structures for careful distribution modeling is recognized as essential for improving estimation efficiency in statistics. However, this approach remains under-explored in the context of bandits. To address this gap, we initiate the study of zero-inflated bandits, where the reward is modeled using a classic semi-parametric distribution known as the zero-inflated distribution. We develop algorithms based on the Upper Confidence Bound and Thompson Sampling frameworks for this specific structure. The superior empirical performance of these methods is demonstrated through extensive numerical studies.

Suggested Citation

  • Haoyu Wei & Runzhe Wan & Lei Shi & Rui Song, 2023. "Zero-Inflated Bandits," Papers 2312.15595, arXiv.org, revised Jan 2025.
    • Handle: RePEc:arx:papers:2312.15595
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    References listed on IDEAS

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