# Regret Analysis for Hierarchical Experts Bandit Problem

arXiv: https://arxiv.org/abs/2208.05622 · HTML: https://mlsanity.com/paper/2208.05622/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 17 references that are in the map.
- First posted on arXiv 11 Aug 2022.

## Built on by (most cited first, 1 of 1)

- [Hierarchical Upper Confidence Bounds for Constrained Online Learning](https://mlsanity.com/paper/2410.17216/) (2024)

## Abstract (arXiv)

We study an extension of standard bandit problem in which there are R layers of experts. Multi-layered experts make selections layer by layer and only the experts in the last layer can play arms. The goal of the learning policy is to minimize the total regret in this hierarchical experts setting. We first analyze the case that total regret grows linearly with the number of layers. Then we focus on the case that all experts are playing Upper Confidence Bound (UCB) strategy and give several sub-linear upper bounds for different circumstances. Finally, we design some experiments to help the regret analysis for the general case of hierarchical UCB structure and show the practical significance of our theoretical results. This article gives many insights about reasonable hierarchical decision structure.