This paper addresses challenges in robust transfer learning stemming from ambiguity in Bayes classifiers and weak transferable signals between the target and source distribution. We introduce a novel quantity called the ''ambiguity level'' that measures the discrepancy between the target and source regression functions, propose a simple transfer learning procedure, and establish a general theorem that shows how this new quantity is related to the transferability of learning in terms of risk improvements. Our proposed ''Transfer Around Boundary'' (TAB) model, with a threshold balancing the performance of target and source data, is shown to be both efficient and robust, improving classification while avoiding negative transfer. Moreover, we demonstrate the effectiveness of the TAB model on non-parametric classification and logistic regression tasks, achieving upper bounds which are optimal up to logarithmic factors. Simulation studies lend further support to the effectiveness of TAB. We also provide simple approaches to bound the excess misclassification error without the need for specialized knowledge in transfer learning.

翻译：本文针对贝叶斯分类器的模糊性以及目标分布与源分布之间弱迁移信号所引发的鲁棒迁移学习挑战展开研究。我们引入了一个名为“模糊度”的新度量，用于衡量目标与源回归函数之间的差异；提出了一种简单的迁移学习流程；并建立了一个通用定理，阐明了该新度量如何通过风险改善来关联学习的可迁移性。本文提出的“边界迁移”（TAB）模型通过阈值平衡目标数据与源数据的性能，兼具高效性与鲁棒性，在提升分类性能的同时避免了负迁移。此外，我们在非参数分类和逻辑回归任务上验证了TAB模型的有效性，其实现了对数因子意义下的最优上界。模拟研究进一步支持了TAB的实用性。本文还提供了无需迁移学习专业知识即可约束超额误分类误差的简易方法。