Fairness-Aware Class Imbalanced Learning on Multiple Subgroups.

ABSTRACT: We present a novel Bayesian-based optimization framework that addresses the challenge of generalization in overparameterized models when dealing with imbalanced subgroups and limited samples per subgroup. Our proposed tri-level optimization framework utilizes local predictors, which are trained on a small amount of data, as well as a fair and class-balanced predictor at the middle and lower levels. To effectively overcome saddle points for minority classes, our lower-level formulation incorporates sharpness-aware minimization. Meanwhile, at the upper level, the framework dynamically adjusts the loss function based on validation loss, ensuring a close alignment between the global predictor and local predictors. Theoretical analysis demonstrates the framework's ability to enhan

SUBMITTER: Tarzanagh DA 

PROVIDER: S-EPMC11003754 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature