On the Connections Among Three Transfer Learning Paradigms

We examine the solution paths of three transfer learning estimators within the context of linear models. Our analysis identifies a general solution path that characterizes how transfer learning estimators interpolate between the target and source estimators. This involves a change of basis and entry-wise weighting functions, with existing transfer learning methods identified as special cases. The proposed framework reveals connections and equivalences among transfer learning approaches, offering valuable insights for designing future estimators with improved control over the learning process. It extends beyond traditional penalized regression and gradient descent techniques and holds the potential for generalization to nonlinear cases. Extensive simulations validate the theoretically derived solution paths, and their practical utility is demonstrated by the improvement in risk prediction for end-stage renal disease in Hispanic populations.