AISTATS 2023poster5 citations
A Contrastive Approach to Online Change Point Detection
Nikita Puchkin, Valeriia Shcherbakova
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 a flexible procedure 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.
BibTeX
@InProceedings{pmlr-v206-puchkin23a,
title = {A Contrastive Approach to Online Change Point Detection},
author = {Puchkin, Nikita and Shcherbakova, Valeriia},
booktitle = {Proceedings of The 26th International Conference on Artificial Intelligence and Statistics},
pages = {5686--5713},
year = {2023},
editor = {Ruiz, Francisco and Dy, Jennifer and van de Meent, Jan-Willem},
volume = {206},
series = {Proceedings of Machine Learning Research},
month = {25--27 Apr},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v206/puchkin23a/puchkin23a.pdf},
url = {https://proceedings.mlr.press/v206/puchkin23a.html},
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 a flexible procedure 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.}
}