Empirical Risk Minimization

Empirical Risk Minimization (ERM) is a fundamental machine learning principle aiming to find models that minimize prediction errors on training data. Current research focuses on improving ERM's robustness and generalization ability through techniques like regularization (including relative entropy and f-divergences), distributionally robust optimization, and model aggregation strategies that prioritize variance reduction over error minimization. These advancements address challenges such as overfitting, adversarial attacks, and fairness concerns, leading to more reliable and trustworthy machine learning models with broader applicability across diverse fields.