Transfer Learning Under High-Dimensional Generalized Linear Models
Ye Tian, Yang Feng
Abstract
transferable source detection approach is introduced to detect informative sources. The detection consistency is proved under the high-dimensional GLM transfer learning setting. We also propose an algorithm to construct confidence intervals of each coefficient component, and the corresponding theories are provided. Extensive simulations and a real-data experiment verify the effectiveness of our algorithms. We implement the proposed GLM transfer learning algorithms in a new R package glmtrans, which is available on CRAN.
Topics & Concepts
Computer scienceEstimatorTransfer of learningConsistency (knowledge bases)Measure (data warehouse)Generalized linear modelAlgorithmConstruct (python library)Transfer (computing)Artificial intelligenceMachine learningMathematicsData miningStatisticsParallel computingProgramming languageDomain Adaptation and Few-Shot LearningSurvey Sampling and Estimation TechniquesBayesian Methods and Mixture Models