# Transfer Neyman-Pearson Algorithm for Outlier Detection

arXiv: https://arxiv.org/abs/2501.01525 · HTML: https://mlsanity.com/paper/2501.01525/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jan 2025.

## Built on by (most cited first, 4 of 4)

- [Neyman-Pearson Classification under Both Null and Alternative Distributions Shift](https://mlsanity.com/paper/2511.06641/) (2025)
- [When More Data Doesn't Help: Limits of Adaptation in Multitask Learning](https://mlsanity.com/paper/2601.20774/) (2026)
- [An Efficient Variant of One-Class SVM with Lifelong Online Learning Guarantees](https://mlsanity.com/paper/2512.11052/) (2025)
- [Classification with Abstention Under Class-Conditional Error Constraints](https://mlsanity.com/paper/2609.22632/) (2026)

## Abstract (arXiv)

We consider the problem of transfer learning in outlier detection where target abnormal data is rare. While transfer learning has been considered extensively in traditional balanced classification, the problem of transfer in outlier detection and more generally in imbalanced classification settings has received less attention. We propose a general meta-algorithm which is shown theoretically to yield strong guarantees w.r.t. to a range of changes in abnormal distribution, and at the same time amenable to practical implementation. We then investigate different instantiations of this general meta-algorithm, e.g., based on multi-layer neural networks, and show empirically that they outperform natural extensions of transfer methods for traditional balanced classification settings (which are the only solutions available at the moment).