Hypergradient Descent

Hypergradient descent is a technique used to optimize hyperparameters in machine learning models by treating them as variables within a nested optimization problem (bilevel optimization). Current research focuses on improving the efficiency and convergence properties of hypergradient methods, particularly for constrained problems and within specific contexts like federated learning, often employing techniques like double momentum and Riemannian manifold optimization. These advancements are significant because efficient hyperparameter tuning is crucial for achieving optimal performance in various machine learning applications, ranging from large-scale optimization problems to personalized incentive design.