# Transfer Learning for Classification under Decision Rule Drift with Application to Optimal Individualized Treatment Rule Estimation

## TL;DR

This paper introduces a transfer learning approach for classification under decision rule drift, focusing on modeling posterior drift via Bayes decision rules, with applications to personalized treatment strategies.

## Contribution

It extends transfer learning to decision rules, proposing a novel geometric transformation-based method with proven consistency and broad applicability to individualized treatment rule estimation.

## Key findings

- Method achieves superior performance in simulations.
- Approach demonstrates robustness on real-world data.
- Establishes theoretical risk bounds and estimator consistency.

## Abstract

In this paper, we extend the transfer learning classification framework from regression function-based methods to decision rules. We propose a novel methodology for modeling posterior drift through Bayes decision rules. By exploiting the geometric transformation of the Bayes decision boundary, our method reformulates the problem as a low-dimensional empirical risk minimization problem. Under mild regularity conditions, we establish the consistency of our estimators and derive the risk bounds. Moreover, we illustrate the broad applicability of our method by adapting it to the estimation of optimal individualized treatment rules. Extensive simulation studies and analyses of real-world data further demonstrate both superior performance and robustness of our approach.