Transfer learning is an essential tool for improving the performance of primary tasks by leveraging information from auxiliary data resources. In this work, we propose Adaptive Robust Transfer Learning (ART), a flexible pipeline of performing transfer learning with generic machine learning algorithms. We establish the non-asymptotic learning theory of ART, providing a provable theoretical guarantee for achieving adaptive transfer while preventing negative transfer. Additionally, we introduce an ART-integrated-aggregating machine that produces a single final model when multiple candidate algorithms are considered. We demonstrate the promising performance of ART through extensive empirical studies on regression, classification, and sparse learning. We further present a real-data analysis for a mortality study.
@article{arxiv.2305.00520,
title = {The ART of Transfer Learning: An Adaptive and Robust Pipeline},
author = {Boxiang Wang and Yunan Wu and Chenglong Ye},
journal= {arXiv preprint arXiv:2305.00520},
year = {2023}
}