A review on minimax rates in change point detection and localisation
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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.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 3 Nov 2020.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 0; 2025: 1; 2026: 4.
6 papers.
- 2026Segmenting Human-LLM Co-authored Text via Change Point Detectioncited by 5
- 2025Optimal Online Change Detection via Random Fourier Featurescited by 2
- 2021Denoising and change point localisation in piecewise-constant high-dimensional regression coefficientscited by 0
- 2026The Sample Complexity of Multiple Change Point Identification under Bandit Feedbackcited by 0
- 2026On Non-Stationary Dynamic Pricing: Adaptivity and Optimalitycited by 0
- 2026Beyond Sub-Gaussian Detector Scores: Robust Weighted Profile-Loss Change Point Detection for Human-LLM Text Segmentationcited by 0
- 2026Beyond Sub-Gaussian Detector Scores: Robust Weighted Profile-Loss Change Point Detection for Human-LLM Text Segmentation29 Sep 2026
- 2026On Non-Stationary Dynamic Pricing: Adaptivity and Optimality27 Jul 2026
- 2026The Sample Complexity of Multiple Change Point Identification under Bandit Feedback13 May 2026
- 2026Segmenting Human-LLM Co-authored Text via Change Point Detection5 May 2026
- 2025Optimal Online Change Detection via Random Fourier Features23 May 2025
- 2021Denoising and change point localisation in piecewise-constant high-dimensional regression coefficients27 Oct 2021