Active learning from positive and unlabeled examples
This addresses a weakly supervised learning challenge for applications like advertising and anomaly detection, but it is incremental as it builds on existing PU learning frameworks.
The paper tackles the problem of active learning from positive and unlabeled data, where labels are only revealed for positives under certain conditions, and provides the first theoretical analysis of its label complexity.
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.