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Zero-Inflated Bandits

arXiv 2312.15595 ↗First posted 25 Dec 2023stat.ML cs.LG econ.EMcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 18 of its 60 references that are in the map. First posted on arXiv 25 Dec 2023.

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18 of 60 references.

References · 18

  1. 2018Thompson Sampling for Combinatorial Semi-Banditscited by 78
  2. 2019Randomized Exploration in Generalized Linear Banditscited by 70
  3. 2016Conservative Banditscited by 59
  4. 2019Bootstrapping Upper Confidence Boundcited by 36
  5. 2018Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Banditscited by 33
  6. 2021A Closer Look at the Worst-case Behavior of Multi-armed Bandit Algorithmscited by 30
  7. 2019Perturbed-History Exploration in Stochastic Linear Banditscited by 28
  8. 2020MOTS: Minimax Optimal Thompson Samplingcited by 28
  9. 2020Concentration Inequalities for Statistical Inferencecited by 24
  10. 2021Metadata-based Multi-Task Bandits with Bayesian Hierarchical Modelscited by 24
  11. 2022Langevin Monte Carlo for Contextual Banditscited by 22
  12. 2021Parallelizing Thompson Samplingcited by 20
6 more
  1. 2018Semiparametric Contextual Banditscited by 19
  2. 2022Towards Scalable and Robust Structured Bandits: A Meta-Learning Frameworkcited by 11
  3. 2019Contextual Multi-armed Bandit Algorithm for Semiparametric Reward Modelcited by 10
  4. 2022Residual Bootstrap Exploration for Stochastic Linear Banditcited by 7
  5. 2022Semi-Parametric Contextual Bandits with Graph-Laplacian Regularizationcited by 4
  6. 2023Tight Non-asymptotic Inference via Sub-Gaussian Intrinsic Moment Normcited by 1

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