Classifier Two Sample Test

Classifier two-sample tests aim to determine whether two datasets originate from the same underlying distribution. Recent research focuses on improving the statistical power of these tests, particularly using deep neural networks and novel approaches like those based on E-values and neural tangent kernels. These advancements leverage sophisticated training strategies and data partitioning techniques to enhance the accuracy and efficiency of distinguishing between datasets with subtle distributional differences. The resulting improvements have significant implications for various fields requiring robust comparison of datasets, such as machine learning, bioinformatics, and anomaly detection.