Augmented Transfer Regression Learning with Semi-non-parametric Nuisance Models

Understand

In contemporary statistical learning, covariate shift correction plays an important role in transfer learning when distribution of the testing data is shifted from the training data.

• Importance weighting, as a natural and principle strategy to adjust for covariate shift, has been commonly used in the field of transfer learning.
• However, this strategy is not robust to model misspecification or excessive estimation error.
• In this paper, we propose an augmented transfer regression learning (ATReL) approach that introduces an imputation model for the targeted response, and uses it to augment the importance weighting equation.