Prior shift estimation for positive unlabeled data through the lens of kernel embedding

ArXi:2502.21194v3 Announce Type: replace-cross We study estimation of a class prior for unlabeled target samples which possibly differs from that of source population. Moreover, it is assumed that the source data is partially observable: only samples from the positive class and from the whole population are available (PU learning scenario). We