Minimax and Neyman–Pearson Meta-Learning for Outlier Languages
Code (0)
등록된 구현이 없습니다.
Tasks
Meta-LearningSimilar Papers 제목 키워드 기반
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 AnsweringTransfer 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 transfe…
Classificationimbalanced classificationOutlier DetectionTransfer LearningDistribution-Free Rates in Neyman-Pearson Classification
We consider the problem of Neyman-Pearson classification which models unbalanced classification settings where error w.r.t. a distribution $\mu_1$ is to be minimized subject to low error w.r.t. a different distribution $…
ClassificationDensity Ratio Estimation and Neyman Pearson Classification with Missing Data
Density Ratio Estimation (DRE) is an important machine learning technique with many downstream applications. We consider the challenge of DRE with missing not at random (MNAR) data. In this setting, we show that using st…
Density Ratio EstimationBounding 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