Integral Probability Metric-Guided CUSUM-Net for Nonparametric Changepoint Detection
Authors
Research Topics
Paper Information
-
Journal:
Biometrika -
DOI:
10.1093/biomet/asag046 -
Published:
July 07, 2026 -
Added to Tracker:
Jul 08, 2026
Abstract
Abstract We propose CUSUM-Net, a nonparametric method for changepoint detection based on integral probability metrics and deep neural networks. Our approach learns a critic function by maximizing an aggregate CUSUM objective over candidate changepoints, thereby linking changepoint detection to two-sample integral probability metrics optimization. The learned critic induces a one-dimensional representation on which changepoints are localized by a classical CUSUM scan. Unlike parametric procedures, CUSUM-Net accommodates complex, high-dimensional distributional changes and applies to a range of data modalities, including Euclidean data, symmetric positive-definite matrices, images and graphs. We establish excess-risk bounds for the learned critic under Hölder smoothness assumptions, with faster rates when the data exhibit low-dimensional manifold structure, and we derive corresponding changepoint localization guarantees. Numerical experiments demonstrate the flexibility and effectiveness of the proposed method.
Author Details
Guanghui Wang
AuthorYunchen Li
AuthorZhou Yu
AuthorShuntuo Xu
AuthorResearch Topics & Keywords
Nonparametric Statistics
Research AreaCitation Information
APA Format
Guanghui Wang
,
Yunchen Li
,
Zhou Yu
&
Shuntuo Xu
(2026)
.
Integral Probability Metric-Guided CUSUM-Net for Nonparametric Changepoint Detection.
Biometrika
, 10.1093/biomet/asag046.
BibTeX Format
@article{paper1457,
title = { Integral Probability Metric-Guided CUSUM-Net for Nonparametric Changepoint Detection },
author = {
Guanghui Wang
and Yunchen Li
and Zhou Yu
and Shuntuo Xu
},
journal = { Biometrika },
year = { 2026 },
doi = { 10.1093/biomet/asag046 },
url = { https://doi.org/10.1093/biomet/asag046 }
}