Transfer Neyman-Pearson Algorithm for Outlier Detection

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 classification, the problem of transfer in outlier detection and more generally in imbalanced classification settings has received less attention. We propose a general algorithmic approach 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 algorithmic approach, e.g., based on multi-layer neural networks, and show empirically that they significantly outperform natural extensions of transfer methods from traditional classification (which are the only solutions available at the moment)