[2206.10143] Noise-contrastive Online Change Point Detection

Statistics > Machine Learning arXiv:2206.10143 (stat) [Submitted on 21 Jun 2022 (v1), last revised 21 Mar 2026 (this version, v4)] Title:Noise-contrastive Online Change Point Detection Authors:Nikita Puchkin, Artur Goldman, Konstantin Yakovlev, Valeriia Dzis, Uliana Vinogradova View a PDF of the paper titled Noise-contrastive Online Change Point Detection, by Nikita Puchkin and 4 other authors View PDF HTML (experimental) Abstract:We suggest a novel procedure for online change point detection. Our approach expands an idea of maximizing a discrepancy measure between points from pre-change and post-change distributions. This leads to flexible algorithms suitable for both parametric and nonparametric scenarios. We prove non-asymptotic bounds on the average running length of the procedure and its expected detection delay. The efficiency of the algorithm is illustrated with numerical experiments on synthetic and real-world data sets. Comments: Subjects: Machine Learning (stat.ML); Machine Learning (cs.LG); Statistics Theory (math.ST); Methodology (stat.ME) Cite as: arXiv:2206.10143 [stat.ML]   (or arXiv:2206.10143v4 [stat.ML] for this version)   https://doi.org/10.48550/arXiv.2206.10143 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Nikita Puchkin [view email] [v1] Tue, 21 Jun 2022 07:01:36 UTC (850 KB) [v2] Fri, 10 Mar 2023 15:05:28 UTC (1,264 KB) [v3] Mon, 6 Nov 2023 12:11:28 UTC (12,677 KB) [v4] Sat, 21 Mar 2026 07:39:51 UTC (906 KB) Full-text lin...