# Active learning from positive and unlabeled examples

arXiv: https://arxiv.org/abs/2602.02081 · HTML: https://mlsanity.com/paper/2602.02081/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 34 references that are in the map.
- First posted on arXiv 2 Feb 2026.

## Builds on (most cited first, 6 of 6)

- [Learning from positive and unlabeled data: a survey](https://mlsanity.com/paper/1811.04820/) (2018)
- [Nuances in Margin Conditions Determine Gains in Active Learning](https://mlsanity.com/paper/2110.08418/) (2021)
- [An Adaptive Strategy for Active Learning with Smooth Decision Boundary](https://mlsanity.com/paper/1711.09294/) (2017)
- [Learning from positive and unlabeled examples -Finite size sample bounds](https://mlsanity.com/paper/2507.07354/) (2025)
- [Active Learning from Positive and Unlabeled Data](https://mlsanity.com/paper/1602.07495/) (2016)
- [Active Learning for One-Class Classification Using Two One-Class Classifiers](https://mlsanity.com/paper/1901.03124/) (2019)

## Abstract (arXiv)

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.