# Unifying Clustered and Non-stationary Bandits

arXiv: https://arxiv.org/abs/2009.02463 · HTML: https://mlsanity.com/paper/2009.02463/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2020.

## Built on by (most cited first, 11 of 11)

- [Field Study in Deploying Restless Multi-Armed Bandits: Assisting Non-Profits in Improving Maternal and Child Health](https://mlsanity.com/paper/2109.08075/) (2021)
- [Asynchronous Upper Confidence Bound Algorithms for Federated Linear Bandits](https://mlsanity.com/paper/2110.01463/) (2021)
- [A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits](https://mlsanity.com/paper/2207.03106/) (2022)
- [When Are Linear Stochastic Bandits Attackable?](https://mlsanity.com/paper/2110.09008/) (2021)
- [Linear Bandits with Memory: from Rotting to Rising](https://mlsanity.com/paper/2302.08345/) (2023)
- [Context in Public Health for Underserved Communities: A Bayesian Approach to Online Restless Bandits](https://mlsanity.com/paper/2402.04933/) (2024)
- [Semi-Parametric Contextual Bandits with Graph-Laplacian Regularization](https://mlsanity.com/paper/2205.08295/) (2022)
- [Federated Linear Contextual Bandits with Heterogeneous Clients](https://mlsanity.com/paper/2403.00116/) (2024)
- [Constrained Feedback Learning for Non-Stationary Multi-Armed Bandits](https://mlsanity.com/paper/2509.15073/) (2025)
- [Clustered Linear Contextual Bandits with Knapsacks](https://mlsanity.com/paper/2308.10722/) (2023)
- [Designing an Interpretable Interface for Contextual Bandits](https://mlsanity.com/paper/2409.15143/) (2024)

## Abstract (arXiv)

Non-stationary bandits and online clustering of bandits lift the restrictive assumptions in contextual bandits and provide solutions to many important real-world scenarios. Though the essence in solving these two problems overlaps considerably, they have been studied independently. In this paper, we connect these two strands of bandit research under the notion of test of homogeneity, which seamlessly addresses change detection for non-stationary bandit and cluster identification for online clustering of bandit in a unified solution framework. Rigorous regret analysis and extensive empirical evaluations demonstrate the value of our proposed solution, especially its flexibility in handling various environment assumptions.