# Zero-Inflated Bandits

arXiv: https://arxiv.org/abs/2312.15595 · HTML: https://mlsanity.com/paper/2312.15595/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 60 references that are in the map.
- First posted on arXiv 25 Dec 2023.

## Builds on (most cited first, 18 of 18)

- [Thompson Sampling for Combinatorial Semi-Bandits](https://mlsanity.com/paper/1803.04623/) (2018)
- [Randomized Exploration in Generalized Linear Bandits](https://mlsanity.com/paper/1906.08947/) (2019)
- [Conservative Bandits](https://mlsanity.com/paper/1602.04282/) (2016)
- [Bootstrapping Upper Confidence Bound](https://mlsanity.com/paper/1906.05247/) (2019)
- [Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits](https://mlsanity.com/paper/1811.05154/) (2018)
- [A Closer Look at the Worst-case Behavior of Multi-armed Bandit Algorithms](https://mlsanity.com/paper/2106.02126/) (2021)
- [Perturbed-History Exploration in Stochastic Linear Bandits](https://mlsanity.com/paper/1903.09132/) (2019)
- [MOTS: Minimax Optimal Thompson Sampling](https://mlsanity.com/paper/2003.01803/) (2020)
- [Concentration Inequalities for Statistical Inference](https://mlsanity.com/paper/2011.02258/) (2020)
- [Metadata-based Multi-Task Bandits with Bayesian Hierarchical Models](https://mlsanity.com/paper/2108.06422/) (2021)
- [Langevin Monte Carlo for Contextual Bandits](https://mlsanity.com/paper/2206.11254/) (2022)
- [Parallelizing Thompson Sampling](https://mlsanity.com/paper/2106.01420/) (2021)
- [Semiparametric Contextual Bandits](https://mlsanity.com/paper/1803.04204/) (2018)
- [Towards Scalable and Robust Structured Bandits: A Meta-Learning Framework](https://mlsanity.com/paper/2202.13227/) (2022)
- [Contextual Multi-armed Bandit Algorithm for Semiparametric Reward Model](https://mlsanity.com/paper/1901.11221/) (2019)
- [Residual Bootstrap Exploration for Stochastic Linear Bandit](https://mlsanity.com/paper/2202.11474/) (2022)
- [Semi-Parametric Contextual Bandits with Graph-Laplacian Regularization](https://mlsanity.com/paper/2205.08295/) (2022)
- [Tight Non-asymptotic Inference via Sub-Gaussian Intrinsic Moment Norm](https://mlsanity.com/paper/2303.07287/) (2023)

## 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.