Coping with distributional shifts is an important part of transfer learning methods in order to perform well in real-life tasks. However, most of the existing approaches in this area either focus on an ideal scenario in which the data does not contain noises or employ a complicated training paradigm or model design to deal with distributional shifts. In this paper, we revisit the robustness of the minimum error entropy (MEE) criterion, a widely used objective in statistical signal processing to deal with non-Gaussian noises, and investigate its feasibility and usefulness in real-life transfer learning regression tasks, where distributional shifts are common. Specifically, we put forward a new theoretical result showing the robustness of MEE against covariate shift. We also show that by simply replacing the mean squared error (MSE) loss with the MEE on basic transfer learning algorithms such as fine-tuning and linear probing, we can achieve competitive performance with respect to state-of-the-art transfer learning algorithms. We justify our arguments on both synthetic data and 5 real-world time-series data.

翻译：应对分布偏移是迁移学习方法在现实任务中取得良好性能的重要组成部分。然而，该领域现有方法大多专注于数据不含噪声的理想场景，或采用复杂的训练范式与模型设计来处理分布偏移。本文重新审视了最小误差熵（MEE）准则——一种在统计信号处理中广泛用于处理非高斯噪声的目标函数的鲁棒性，并探究其在分布偏移普遍存在的现实迁移学习回归任务中的可行性与实用性。具体而言，我们提出了新的理论结果，证明了MEE对协变量偏移的鲁棒性。此外，我们表明，通过简单地将均方误差（MSE）损失替换为MEE，并应用于微调与线性探测等基础迁移学习算法，即可获得与当前最优迁移学习算法相匹敌的竞争性能。我们在合成数据与5组真实时间序列数据上验证了上述论点。