Robust Quickest Change Detection for Unnormalized Models

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

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)2314-2323
Number of pages10
JournalProceedings of Machine Learning Research
Volume216
StatePublished - 2023
Event39th Conference on Uncertainty in Artificial Intelligence, UAI 2023 - Pittsburgh, United States
Duration: Jul 31 2023Aug 4 2023

Bibliographical note

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© UAI 2023. All rights reserved.

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