On the Connections Among Three Transfer Learning Paradigms

Area of Science:

• Machine Learning
• Statistical Modeling
• Biostatistics

Background:

• Transfer learning (TL) is crucial for leveraging existing data to improve model performance on new tasks.
• Existing TL methods often lack a unified theoretical framework, hindering the design of novel estimators.
• Understanding the interpolation between source and target estimators is key to advancing TL.

Purpose of the Study:

• To analyze the solution paths of three transfer learning estimators in linear models.
• To develop a general framework characterizing the interpolation between source and target estimators.
• To demonstrate the practical utility of the proposed framework in a real-world application.

Main Methods:

• Mathematical analysis of transfer learning estimator solution paths.
• Identification of a general solution path involving basis changes and entry-wise weighting.
• Extensive simulations to validate theoretical findings.
• Application to end-stage renal disease risk prediction in Hispanic populations.

Main Results:

• A general solution path for TL estimators in linear models was identified.
• Existing TL methods were shown to be special cases of this general path.
• The framework reveals connections and equivalences among different TL approaches.
• Improved risk prediction for end-stage renal disease was achieved.

Conclusions:

• The proposed framework provides a unified understanding of TL estimators.
• It offers insights for designing new TL methods with enhanced control.
• The approach has potential for generalization to nonlinear models.
• The practical application demonstrated significant improvements in a health disparity context.