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In this paper, we propose to estimate model parameters and identifyinformative source datasets simultaneously for high-dimensional transferlearning problems with the aid of a non-convex penalty, in contrast to theseparate useful dataset selection and transfer learning procedures in theexisting literature. To numerically solve the non-convex problem with respectto two specific statistical models, namely the sparse linear regression and thegeneralized low-rank trace regression models, we adopt the difference of convex(DC) programming with the alternating direction method of multipliers (ADMM)procedures. We theoretically justify the proposed algorithm from bothstatistical and computational perspectives. Extensive numerical results arereported alongside to validate the theoretical assertions. An \texttt{R}package \texttt{MHDTL} is developed to implement the proposed methods.

Simultaneous Estimation and Dataset Selection for Transfer Learning in High Dimensions by a Non-convex Penalty