Robust Quickest Change Detection for Unnormalized Models

Suya Wu, Enmao Diao, Taposh Banerjee, Jie Ding, Vahid Tarokh

Keywords: robust change detection, quickest change detection, SCUSUM, score matching, unnormalized models

TL;DR: We propose a new robust score-based quickest change detection algorithm that can be applied to 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.