Robust Quickest Change Detection for Unnormalized Models

Suya Wu, Enmao Diao, Jie Ding, Taposh Banerjee, Vahid Tarokh
Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2314-2323, 2023.

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 this Paper

BibTeX
@InProceedings{pmlr-v216-wu23c, title = {Robust Quickest Change Detection for Unnormalized Models}, author = {Wu, Suya and Diao, Enmao and Ding, Jie and Banerjee, Taposh and Tarokh, Vahid}, booktitle = {Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence}, pages = {2314--2323}, year = {2023}, editor = {Evans, Robin J. and Shpitser, Ilya}, volume = {216}, series = {Proceedings of Machine Learning Research}, month = {31 Jul--04 Aug}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v216/wu23c/wu23c.pdf}, url = {https://proceedings.mlr.press/v216/wu23c.html}, 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.} }
Endnote
%0 Conference Paper %T Robust Quickest Change Detection for Unnormalized Models %A Suya Wu %A Enmao Diao %A Jie Ding %A Taposh Banerjee %A Vahid Tarokh %B Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2023 %E Robin J. Evans %E Ilya Shpitser %F pmlr-v216-wu23c %I PMLR %P 2314--2323 %U https://proceedings.mlr.press/v216/wu23c.html %V 216 %X 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.
APA
Wu, S., Diao, E., Ding, J., Banerjee, T. & Tarokh, V.. (2023). Robust Quickest Change Detection for Unnormalized Models. Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research 216:2314-2323 Available from https://proceedings.mlr.press/v216/wu23c.html.

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