Robust Quickest Change Detection for Unnormalized Models

@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.}
}