Improving Subgroup Robustness via Data Selection

Saachi Jain

Abstract:

Machine learning models can often fail on subgroups that are underrepresented
during training. While dataset balancing can improve performance on underperforming 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