Robust Score-Based Quickest Change Detection

Methods in the field of quickest change detection rapidly detect in real-time a change in the data-generating distribution of an online data stream. Existing methods have been able to detect this change point when the densities of the pre- and post-change distributions are known. Recent work has extended these results to the case where the pre- and post-change distributions are known only by their score functions. This work considers the case where the pre- and post-change score functions are known only to correspond to distributions in two disjoint sets. This work employs a pair of "least-favorable" distributions to robustify the existing score-based quickest change detection algorithm, the properties of which are studied. This paper calculates the least-favorable distributions for specific model classes and provides methods of estimating the least-favorable distributions for common constructions. Simulation results are provided demonstrating the performance of our robust change detection algorithm.

@article{moushegian2025_2407.11094,
  title={ Robust Score-Based Quickest Change Detection },
  author={ Sean Moushegian and Suya Wu and Enmao Diao and Jie Ding and Taposh Banerjee and Vahid Tarokh },
  journal={arXiv preprint arXiv:2407.11094},
  year={ 2025 }
}