Many existing transfer learning methods rely on leveraging information from source data that closely resembles the target data. However, this approach often overlooks valuable knowledge that may be present in different yet potentially related auxiliary samples. When dealing with a limited amount of target data and a diverse range of source models, our paper introduces a novel approach, Distributionally Robust Optimization for Transfer Learning (TransDRO), that breaks free from strict similarity constraints. TransDRO is designed to optimize the most adversarial loss within an uncertainty set, defined as a collection of target populations generated as a convex combination of source distributions that guarantee excellent prediction performances for the target data. TransDRO effectively bridges the realms of transfer learning and distributional robustness prediction models. We establish the identifiability of TransDRO and its interpretation as a weighted average of source models closest to the baseline model. We also show that TransDRO achieves a faster convergence rate than the model fitted with the target data. Our comprehensive numerical studies and analysis of multi-institutional electronic health records data using TransDRO further substantiate the robustness and accuracy of TransDRO, highlighting its potential as a powerful tool in transfer learning applications.

翻译：许多现有的迁移学习方法依赖于利用与目标数据高度相似的源数据信息。然而，这种方法往往忽略了可能存在于不同但潜在相关的辅助样本中的宝贵知识。当面对有限的目标数据和多样化的源模型时，本文提出了一种新颖的方法——分布鲁棒优化迁移学习（TransDRO），该方法打破了严格的相似性约束。TransDRO旨在优化不确定性集内的最坏情况损失，该不确定性集定义为通过源分布的凸组合生成的目标总体集合，以确保目标数据具有优异的预测性能。TransDRO有效连接了迁移学习与分布鲁棒预测模型的领域。我们证明了TransDRO的可识别性，并将其解释为最接近基线模型的源模型的加权平均。我们还表明，TransDRO比仅使用目标数据拟合的模型具有更快的收敛速度。通过全面的数值研究以及使用TransDRO对多机构电子健康记录数据的分析，进一步证实了TransDRO的鲁棒性和准确性，凸显了其作为迁移学习应用中的强大工具的潜力。