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Transfer learning uses a data model, trained to make predictions orinferences on data from one population, to make reliable predictions orinferences on data from another population. Most existing transfer learningapproaches are based on fine-tuning pre-trained neural network models, and failto provide crucial uncertainty quantification. We develop a statisticalframework for model predictions based on transfer learning, called RECaST. Theprimary mechanism is a Cauchy random effect that recalibrates a source model toa target population; we mathematically and empirically demonstrate the validityof our RECaST approach for transfer learning between linear models, in thesense that prediction sets will achieve their nominal stated coverage, and wenumerically illustrate the method's robustness to asymptotic approximations fornonlinear models. Whereas many existing techniques are built on particularsource models, RECaST is agnostic to the choice of source model. For example,our RECaST transfer learning approach can be applied to a continuous ordiscrete data model with linear or logistic regression, deep neural networkarchitectures, etc. Furthermore, RECaST provides uncertainty quantification forpredictions, which is mostly absent in the literature. We examine our method'sperformance in a simulation study and in an application to real hospital data.

Transfer Learning with Uncertainty Quantification: Random Effect Calibration of Source to Target (RECaST)