Classical quickest change detection algorithms require modeling pre-change and post-change distributions. Such an approach may not be feasible for various machine learning models because of the complexity of computing the explicit distributions. Additionally, these methods may suffer from a lack of robustness to model mismatch and noise. This paper develops a new variant of the classical Cumulative Sum (CUSUM) algorithm for the quickest change detection. This variant is based on Fisher divergence and the Hyv\"arinen score and is called the Score-based CUSUM (SCUSUM) algorithm. The SCUSUM algorithm allows the applications of change detection for unnormalized statistical models, i.e., models for which the probability density function contains an unknown normalization constant. The asymptotic optimality of the proposed algorithm is investigated by deriving expressions for average detection delay and the mean running time to a false alarm. Numerical results are provided to demonstrate the performance of the proposed algorithm.

翻译：经典的最快变化检测算法需要建模变化前和变化后的分布。由于计算显式分布的复杂性，这种方法可能不适用于许多机器学习模型。此外，这些方法可能因对模型失配和噪声缺乏鲁棒性而受到影响。本文针对最快变化检测问题，提出了一种经典累积和（CUSUM）算法的新变体。该变体基于Fisher散度和Hyvärinen分数，称为基于分数的累积和（SCUSUM）算法。SCUSUM算法允许将变化检测应用于未归一化统计模型，即概率密度函数包含未知归一化常数的模型。通过推导平均检测延迟和平均虚警运行时间的表达式，研究了所提出算法的渐近最优性。数值结果展示了所提出算法的性能。