Improving Subgroup Robustness via Data Selection

Abstract

Machine learning models can often fail on subgroups that are underrepresented during training. While dataset balancing can improve performance on underper-forming groups, it requires access to training group annotations and can end up removing large portions of the dataset. In this paper, we introduce Data Debiasing with Datamodels (D3M), a debiasing approach which isolates and removes specific training examples that drive the model’s failures on minority groups. Our approach enables us to efficiently train debiased classifiers while removing only a small number of examples, and does not require training group annotations or additional hyperparameter tuning