Transfer Learning with Distance Covariance for Random Forest: Error Bounds and an EHR Application

ArXi:2510.10870v2 Announce Type: replace-cross We propose a method for transfer learning in nonparametric regression using a random forest (RF) with distance covariance-based feature weights, assuming the unknown source and target regression functions are sparsely different. Our method obtains residuals from a source domain-trained Centered RF (CRF) in the target domain, then fits another CRF to these residuals with feature splitting probabilities proportional to feature-residual sample distance covariance.