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.

翻译：迁移学习通过利用辅助数据资源中的信息来提升主任务性能，是一种重要工具。本文提出自适应鲁棒迁移学习（ART），这是一种灵活的迁移学习流程，可与通用机器学习算法结合使用。我们建立了ART的非渐近学习理论，为在防止负迁移的同时实现自适应迁移提供了可证明的理论保证。此外，我们引入一种ART集成聚合机制，当考虑多种候选算法时，可生成唯一的最终模型。通过回归、分类和稀疏学习的大量实证研究，我们展示了ART的优越性能，并进一步将其应用于一项死亡率研究的真实数据分析中。