Domain adaptation (DA) is a statistical learning problem that arises when the distribution of the source data used to train a model differs from that of the target data used to evaluate the model. While many DA algorithms have demonstrated considerable empirical success, blindly applying these algorithms can often lead to worse performance on new datasets. To address this, it is crucial to clarify the assumptions under which a DA algorithm has good target performance. In this work, we focus on the assumption of the presence of conditionally invariant components (CICs), which are relevant for prediction and remain conditionally invariant across the source and target data. We demonstrate that CICs, which can be estimated through conditional invariant penalty (CIP), play three prominent roles in providing target risk guarantees in DA. First, we propose a new algorithm based on CICs, importance-weighted conditional invariant penalty (IW-CIP), which has target risk guarantees beyond simple settings such as covariate shift and label shift. Second, we show that CICs help identify large discrepancies between source and target risks of other DA algorithms. Finally, we demonstrate that incorporating CICs into the domain invariant projection (DIP) algorithm can address its failure scenario caused by label-flipping features. We support our new algorithms and theoretical findings via numerical experiments on synthetic data, MNIST, CelebA, and Camelyon17 datasets.

域自适应是一种统计学习问题，当用于训练模型的源数据分布与用于评估模型的目标数据分布不同时产生。本研究着重于讨论条件不变组件（CICs）存在的假设，在域自适应中扮演了重要角色，通过条件不变惩罚（CIP）可以估计CICs，并提出了一种新的基于CICs的算法（IW-CIP）来提供目标风险保证，解决了其他域自适应算法在源数据和目标数据之间存在的差异问题，并通过将CICs纳入域不变投影（DIP）算法解决了由标签翻转特征引起的失败情况，通过合成数据、MNIST、CelebA和Camelyon17数据集的数值实验来验证了新算法和理论发现。

领域适应中条件不变组件的重要角色：理论与算法