Trans-Glasso: A Transfer Learning Approach to Precision Matrix Estimation

ArXi:2411.15624v2 Announce Type: replace-cross Precision matrix estimation is essential in various fields; yet it is challenging when samples for the target study are limited. Transfer learning can enhance estimation accuracy by leveraging data from related source studies. We propose Trans-Glasso, a two-step transfer learning method for precision matrix estimation. First, we obtain initial estimators using a multi-task learning objective that captures shared and unique features across studies.