AI RESEARCH
A Semi-Supervised Kernel Two-Sample Test
arXiv CS.LG
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ArXi:2605.01775v1 Announce Type: cross We consider the problem of two-sample testing in a semi-supervised setting with abundant unlabeled covariate data. Standard two-sample tests neglect covariate information, which has the potential to significantly boost performance. However, incorporating covariates potentially breaks the exchangeability assumption under the null, which further complicates a calibration procedure.