Transfer Learning Under High-Dimensional Generalized Linear Models
提出了一种可检测信息源的可迁移源检测方法，在高维GLM迁移学习设定下证明了检测一致性，并构造了各系数分量的置信区间算法，通过模拟和真实数据验证了有效性。
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.