  In contemporary data analysis, it is increasingly common to work with
non-stationary complex datasets. These datasets typically extend beyond the
classical low-dimensional Euclidean space, making it challenging to detect
shifts in their distribution without relying on strong structural assumptions.
This paper introduces a novel offline change-point detection method that
leverages modern classifiers developed in the machine-learning community. With
suitable data splitting, the test statistic is constructed through sequential
computation of the Area Under the Curve (AUC) of a classifier, which is trained
on data segments on both ends of the sequence. It is shown that the resulting
AUC process attains its maxima at the true change-point location, which
facilitates the change-point estimation. The proposed method is characterized
by its complete nonparametric nature, significant versatility, considerable
flexibility, and absence of stringent assumptions pertaining to the underlying
data or any distributional shifts. Theoretically, we derive the limiting
pivotal distribution of the proposed test statistic under null, as well as the
asymptotic behaviors under both local and fixed alternatives. The weak
consistency of the change-point estimator is provided. Extensive simulation
studies and the analysis of two real-world datasets illustrate the superior
performance of our approach compared to existing model-free change-point
detection methods.
