# A review on minimax rates in change point detection and localisation

arXiv: https://arxiv.org/abs/2011.01857 · HTML: https://mlsanity.com/paper/2011.01857/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2020.

## Built on by (most cited first, 6 of 6)

- [Segmenting Human-LLM Co-authored Text via Change Point Detection](https://mlsanity.com/paper/2605.03723/) (2026)
- [Optimal Online Change Detection via Random Fourier Features](https://mlsanity.com/paper/2505.17789/) (2025)
- [Denoising and change point localisation in piecewise-constant high-dimensional regression coefficients](https://mlsanity.com/paper/2110.14298/) (2021)
- [The Sample Complexity of Multiple Change Point Identification under Bandit Feedback](https://mlsanity.com/paper/2605.13252/) (2026)
- [On Non-Stationary Dynamic Pricing: Adaptivity and Optimality](https://mlsanity.com/paper/2607.24115/) (2026)
- [Beyond Sub-Gaussian Detector Scores: Robust Weighted Profile-Loss Change Point Detection for Human-LLM Text Segmentation](https://mlsanity.com/paper/2609.36888/) (2026)

## Abstract (arXiv)

This paper reviews recent developments in fundamental limits and optimal algorithms for change point analysis. We focus on minimax optimal rates in change point detection and localisation, in both parametric and nonparametric models. We start with the univariate mean change point analysis problem and review the state-of-the-art results in the literature. We then move on to more complex data types and investigate general principles behind the optimal procedures that lead to minimax rate-optimal results.