Active learning from positive and unlabeled examples

Fuente: arXiv
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Main Authors: Mansouri, Farnam, Zilles, Sandra, Ben-David, Shai
Format: Preprint
Published: 2026
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author Mansouri, Farnam
Zilles, Sandra
Ben-David, Shai
author_facet Mansouri, Farnam
Zilles, Sandra
Ben-David, Shai
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2602_02081
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Active learning from positive and unlabeled examples
Mansouri, Farnam
Zilles, Sandra
Ben-David, Shai
Machine Learning
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.
title Active learning from positive and unlabeled examples
topic Machine Learning
url https://arxiv.org/abs/2602.02081