Robust Quickest Change Detection for Unnormalized Models
Abstract
Detecting an abrupt and persistent change in the underlying distribution of online data streams is an important problem in many applications. This paper proposes a new robust score-based algorithm called RSCUSUM, which can be applied to unnormalized models and addresses the issue of unknown post-change distributions. RSCUSUM replaces the Kullback-Leibler divergence with the Fisher divergence between pre- and post-change distributions for computational efficiency in unnormalized statistical models and introduces a notion of the ``least favorable'' distribution for robust change detection. The algorithm and its theoretical analysis are demonstrated through simulation studies.
Cite
@article{arxiv.2306.05091,
title = {Robust Quickest Change Detection for Unnormalized Models},
author = {Suya Wu and Enmao Diao and Taposh Banerjee and Jie Ding and Vahid Tarokh},
journal= {arXiv preprint arXiv:2306.05091},
year = {2023}
}
Comments
Accepted for the 39th Conference on Uncertainty in Artificial Intelligence (UAI 2023). arXiv admin note: text overlap with arXiv:2302.00250