Robust transfer learning of high-dimensional generalized linear model

Abstract

This paper studies transfer learning of a high-dimensional generalized linear model with the target model as well as source data from different but possibly related models. Both known and unknown transferable domain settings are considered. On the one hand, an improved two-step transfer learning algorithm is proposed and the optimal rate of convergence for estimation is proved when the set of transferable domain is known. On the other hand, when the set of transferable domain is unknown, we propose a data-driven procedure for transfer learning, called Stepwise Selection algorithm, and investigate its finite-sample performance through simulations studies. Experimental results on six datasets demonstrate that the proposed method can perform better.