# Robust Quickest Change Detection for Unnormalized Models

arXiv: https://arxiv.org/abs/2306.05091 · HTML: https://mlsanity.com/paper/2306.05091/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2023.

## Built on by (most cited first, 1 of 1)

- [Jacobian-Velocity Bounds for Deployment Risk Under Covariate Drift](https://mlsanity.com/paper/2605.04932/) (2026)

## Abstract (arXiv)

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.