# Robust Score-Based Quickest Change Detection

arXiv: https://arxiv.org/abs/2407.11094 · HTML: https://mlsanity.com/paper/2407.11094/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 34 references that are in the map.
- First posted on arXiv 15 Jul 2024.

## Builds on (most cited first, 5 of 5)

- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [Gaussian-Bernoulli RBMs Without Tears](https://mlsanity.com/paper/2210.10318/) (2022)
- [Bayesian model comparison with the Hyv"arinen score: computation and consistency](https://mlsanity.com/paper/1711.00136/) (2017)
- [Quickest Change Detection for Unnormalized Statistical Models](https://mlsanity.com/paper/2302.00250/) (2023)

## Built on by (most cited first, 2 of 2)

- [Conditional Score Learning for Quickest Change Detection in Markov Transition Kernels](https://mlsanity.com/paper/2511.03953/) (2025)
- [Score-Based Change-Point Detection and Region Localization for Spatio-Temporal Point Processes](https://mlsanity.com/paper/2602.04798/) (2026)

## Abstract (arXiv)

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 selects a pair of least-favorable distributions from these sets 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.