论文标题
部分可观测时空混沌系统的无模型预测
Direct-Effect Risk Minimization for Domain Generalization
论文作者
论文摘要
我们研究了分布外（O.O.D.）概括的问题，在训练和测试域中，属性的虚假相关性各不相同。这被称为相关性转移问题，并引起了机器学习可靠性的关注。在这项工作中，我们介绍了从因果推论到域泛化问题的直接和间接影响的概念。我们认为，学习直接效应的模型可以最大程度地减少相关转移域中最坏的风险。为了消除间接效应，我们的算法包括两个阶段：在第一阶段，我们通过使用表示和类标签的域标签的预测误差来学习间接效应表示。在第二阶段，我们通过将每个数据与另一个数据相似的间接效应表示的数据匹配，但在训练和验证阶段中的不同类标签来删除在第一阶段学习的间接效果。我们的方法已显示与现有方法兼容，并在相关转移的数据集上提高了它们的概括性能。对5个相关转移数据集和域基准测试的实验验证了我们方法的有效性。
We study the problem of out-of-distribution (o.o.d.) generalization where spurious correlations of attributes vary across training and test domains. This is known as the problem of correlation shift and has posed concerns on the reliability of machine learning. In this work, we introduce the concepts of direct and indirect effects from causal inference to the domain generalization problem. We argue that models that learn direct effects minimize the worst-case risk across correlation-shifted domains. To eliminate the indirect effects, our algorithm consists of two stages: in the first stage, we learn an indirect-effect representation by minimizing the prediction error of domain labels using the representation and the class labels; in the second stage, we remove the indirect effects learned in the first stage by matching each data with another data of similar indirect-effect representation but of different class labels in the training and validation phase. Our approach is shown to be compatible with existing methods and improve the generalization performance of them on correlation-shifted datasets. Experiments on 5 correlation-shifted datasets and the DomainBed benchmark verify the effectiveness of our approach.
