Learning from positive and unlabeled data (PU learning) is a weakly supervised variant of binary classification in which the learner receives labels only for (some) positively labeled instances, while all other examples remain unlabeled. Motivated by applications such as advertising and anomaly detection, we study an active PU learning setting where the learner can adaptively query instances from an unlabeled pool, but a queried label is revealed only when the instance is positive and an independent coin flip succeeds; otherwise the learner receives no information. In this paper, we provide the first theoretical analysis of the label complexity of active PU learning.
@article{arxiv.2602.02081,
title = {Active learning from positive and unlabeled examples},
author = {Farnam Mansouri and Sandra Zilles and Shai Ben-David},
journal= {arXiv preprint arXiv:2602.02081},
year = {2026}
}