  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.
