Distribution shifts are ubiquitous in real-world machine learning applications, posing a challenge to the generalization of models trained on one data distribution to another. We focus on scenarios where data distributions vary across multiple segments of the entire population and only make local assumptions about the differences between training and test (deployment) distributions within each segment. We propose a two-stage multiply robust estimation method to improve model performance on each individual segment for tabular data analysis. The method involves fitting a linear combination of the based models, learned using clusters of training data from multiple segments, followed by a refinement step for each segment. Our method is designed to be implemented with commonly used off-the-shelf machine learning models. We establish theoretical guarantees on the generalization bound of the method on the test risk. With extensive experiments on synthetic and real datasets, we demonstrate that the proposed method substantially improves over existing alternatives in prediction accuracy and robustness on both regression and classification tasks. We also assess its effectiveness on a user city prediction dataset from Meta.

翻译：分布偏移在现实世界机器学习应用中普遍存在，这对将在一种数据分布上训练的模型推广至另一种分布提出了挑战。我们关注数据分布在整个总体多个片段间变化的情况，且仅对每个片段内训练与测试（部署）分布间的差异做出局部假设。我们提出一种两阶段多重稳健估计方法，旨在提升表格数据分析中每个独立片段的模型性能。该方法涉及拟合基于模型的线性组合——这些基础模型通过使用来自多个片段的训练数据聚类学习得到，随后对每个片段进行细化步骤。我们的方法设计为可与常用现成机器学习模型配合实现。我们建立了该方法在测试风险上泛化界限的理论保证。通过在合成与真实数据集上的大量实验，我们证明所提方法在回归与分类任务的预测精度和稳健性方面均显著优于现有替代方案。我们还评估了其在Meta用户城市预测数据集上的有效性。