# Sequential Change Detection for Learning in Piecewise Stationary Bandit Environments

arXiv: https://arxiv.org/abs/2501.10974 · HTML: https://mlsanity.com/paper/2501.10974/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jan 2025.

## Built on by (most cited first, 5 of 5)

- [Online Clustering of Data Sequences with Bandit Information](https://mlsanity.com/paper/2501.11421/) (2025)
- [Finite-Horizon Quickest Change Detection Balancing Latency with False Alarm Probability](https://mlsanity.com/paper/2511.12803/) (2025)
- [DAL: A Practical Prior-Free Black-Box Framework for Piecewise Stationary Bandits](https://mlsanity.com/paper/2501.19401/) (2025)
- [The Cost of Learning Under Multiple Change Points](https://mlsanity.com/paper/2602.11406/) (2026)
- [DARLING: Detection Augmented Reinforcement Learning with Non-Stationary Guarantees](https://mlsanity.com/paper/2604.16684/) (2026)

## Abstract (arXiv)

A finite-horizon variant of the quickest change detection problem is investigated, which is motivated by a change detection problem that arises in piecewise stationary bandits. The goal is to minimize the latency, which is smallest threshold such that the probability that the detection delay exceeds the threshold is below a desired low level, while controlling the false alarm probability to a desired low level. When the pre- and post-change distributions are unknown, two tests are proposed as candidate solutions. These tests are shown to attain order optimality in terms of the horizon. Furthermore, the growth in their latencies with respect to the false alarm probability and late detection probability satisfies a property that is desirable in regret analysis for piecewise stationary bandits. Numerical results are provided to validate the theoretical performance results.