Machine learning models often have uneven performance among subpopulations (a.k.a., groups) in the data distributions. This poses a significant challenge for the models to generalize when the proportions of the groups shift during deployment. To improve robustness to such shifts, existing approaches have developed strategies that train models or perform hyperparameter tuning using the group-labeled data to minimize the worst-case loss over groups. However, a non-trivial amount of high-quality labels is often required to obtain noticeable improvements. Given the costliness of the labels, we propose to adopt a different paradigm to enhance group label efficiency: utilizing the group-labeled data as a target set to optimize the weights of other group-unlabeled data. We introduce Group-robust Sample Reweighting (GSR), a two-stage approach that first learns the representations from group-unlabeled data, and then tinkers the model by iteratively retraining its last layer on the reweighted data using influence functions. Our GSR is theoretically sound, practically lightweight, and effective in improving the robustness to subpopulation shifts. In particular, GSR outperforms the previous state-of-the-art approaches that require the same amount or even more group labels.

本研究针对机器学习模型在子群体（群体）中表现不均的问题，提出了一种新的方法以提高模型在群体比例变化时的鲁棒性。通过引入群体鲁棒样本重标定（GSR），研究者以无群体标签数据为基础，优化其他无群体标签数据的权重，从而实现性能的提升，并且GSR在理论上可靠、实践中高效，优于需要相同或更多群体标签的传统方法。

基于影响函数的群体鲁棒样本重标定应对子群体转变