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This study introduces a novel Bayesian optimization framework to improve generalization in overparameterized models, especially with imbalanced data. The new tri-level approach enhances both classification and fairness for minority groups.

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Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Optimization Theory

Background:

  • Overparameterized models struggle with generalization, particularly in scenarios with imbalanced subgroups and limited data per subgroup.
  • Existing methods often fail to adequately address fairness and generalization simultaneously, especially for minority classes.

Purpose of the Study:

  • To develop a novel Bayesian-based optimization framework to enhance generalization in overparameterized models facing imbalanced subgroups.
  • To improve both classification accuracy and fairness for minority classes within limited sample settings.

Main Methods:

  • A tri-level optimization framework incorporating local predictors trained on small datasets.
  • Integration of a fair and class-balanced predictor at middle and lower levels.
  • Sharpness-aware minimization for minority class saddle points and dynamic loss adjustment based on validation loss at the upper level.

Main Results:

  • Theoretical analysis indicates enhanced classification and fairness generalization, with potential improvements in the generalization bound.
  • Empirical results demonstrate superior performance compared to current state-of-the-art methods.
  • The framework effectively addresses challenges posed by imbalanced subgroups and limited samples.

Conclusions:

  • The proposed tri-level Bayesian optimization framework offers a significant advancement in achieving robust generalization for overparameterized models.
  • The method successfully balances classification performance and fairness, particularly for underrepresented groups.
  • The framework provides a promising direction for developing more equitable and effective machine learning models.