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 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).
Code (0)
등록된 구현이 없습니다.
Tasks
Classificationimbalanced classificationOutlier DetectionTransfer LearningSimilar Papers 제목 키워드 기반
Tight Rates in Supervised Outlier Transfer Learning
A critical barrier to learning an accurate decision rule for outlier detection is the scarcity of outlier data. As such, practitioners often turn to the use of similar but imperfect outlier data from which they might tra…
Outlier DetectionTransfer LearningMinimax and Neyman–Pearson Meta-Learning for Outlier Languages
Minimax and Neyman-Pearson Meta-Learning for Outlier Languages
Model-agnostic meta-learning (MAML) has been recently put forth as a strategy to learn resource-poor languages in a sample-efficient fashion. Nevertheless, the properties of these languages are often not well represented…
Meta-LearningPart-Of-Speech TaggingQuestion AnsweringNeyman-Pearson Classification under Both Null and Alternative Distributions Shift
We consider the problem of transfer learning in Neyman-Pearson classification, where the objective is to minimize the error w.r.t. a distribution $μ_1$, subject to the constraint that the error w.r.t. a distribution $μ_0…
Transfer LearningBounding Neyman-Pearson Region with $f$-Divergences
The Neyman-Pearson region of a simple binary hypothesis testing is the set of points whose coordinates represent the false positive rate and false negative rate of some test. The lower boundary of this region is given by…
LEMMA