Subpopulation Shift

Subpopulation shift, a type of data distribution change affecting specific subgroups within a dataset, poses a significant challenge to machine learning model generalization and fairness. Current research focuses on developing robust models through techniques like adversarial training, group-aware priors, and importance reweighting, often incorporating mixup strategies to improve performance on underrepresented subpopulations. Addressing subpopulation shift is crucial for deploying reliable and unbiased machine learning models across diverse real-world applications, particularly in sensitive areas like healthcare and medical image analysis, where accurate and equitable performance across subgroups is paramount. This research aims to improve model robustness and reduce performance disparities across different subpopulations.