Zero-Inflated Bandits

25 Dec 2023  ·  Haoyu Wei, Runzhe Wan, Lei Shi, Rui Song ·

Many real applications of bandits have sparse non-zero rewards, leading to slow learning rates. A careful distribution modeling that utilizes problem-specific structures is known as critical to estimation efficiency in the statistics literature, yet is under-explored in bandits. To fill the gap, we initiate the study of zero-inflated bandits, where the reward is modeled as a classic semi-parametric distribution called zero-inflated distribution. We carefully design Upper Confidence Bound (UCB) and Thompson Sampling (TS) algorithms for this specific structure. Our algorithms are suitable for a very general class of reward distributions, operating under tail assumptions that are considerably less stringent than the typical sub-Gaussian requirements. Theoretically, we derive the regret bounds for both the UCB and TS algorithms for multi-armed bandit, showing that they can achieve rate-optimal regret when the reward distribution is sub-Gaussian. The superior empirical performance of the proposed methods is shown via extensive numerical studies.

PDF Abstract
No code implementations yet. Submit your code now


  Add Datasets introduced or used in this paper

Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.