arXiv paper proposes importance weighting for unlabeled-unlabeled learning under distribution shift

A new arXiv preprint addresses unlabeled-unlabeled (UU) learning, a setting where a binary classifier is trained from two unlabeled datasets that have differing class priors. The authors introduce importance weighting to handle distribution shift in this framework, which generalizes approaches such as positive-unlabeled learning. The work is listed as a cross-submission in the cs.AI category.