arXiv:2507.17026v2 Announce Type: replace-cross Abstract: The two-sample testing problem, a fundamental task in statistics and machine learning, seeks to determine whether two sets of samples, drawn from underlying distributions $p$ and $q$, are in fact identically distributed (i.e. whether $p=q$). A popular and intuitive approach is the classifier two-sample test (C2ST), where a classifier is trained to distinguish between samples from $p$ and $q$. Yet despite simplicity of the C2ST, its reliability hinges on access to a near-Bayes-optimal classifier, a requirement that is rarely met and diff
This paper presents a new method for two-sample testing, a fundamental task in machine learning, offering improved reliability for distinguishing between data distributions.
Improved two-sample testing can enhance model evaluation, anomaly detection, and data quality assurance across various AI applications, making machine learning systems more robust.
The proposed 'Conformal C2ST' method provides a more reliable way to determine if two datasets come from the same distribution, especially when optimal classifiers are unavailable.
- · Machine learning researchers
- · AI developers
- · Data scientists
More accurate and reliable statistical tests for comparing data distributions will be available to practitioners.
Enhanced capabilities for model comparison, domain adaptation, and adversarial detection in AI will emerge.
This could contribute to the development of more trustworthy and less 'brittle' AI systems in critical applications.
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Read at arXiv cs.LG