Hypergradient Estimation

Hypergradient estimation focuses on efficiently calculating the gradient of an outer objective function dependent on the solution of an inner optimization problem—a common challenge in bilevel optimization used extensively in machine learning for tasks like hyperparameter tuning. Current research emphasizes improving the accuracy and efficiency of hypergradient computation, exploring techniques like preconditioning, reparameterization, and leveraging the Koopman operator to approximate global hypergradients from local ones. These advancements are crucial for scaling bilevel optimization to larger datasets and more complex models, particularly in federated learning settings where communication efficiency is paramount, leading to improved algorithms for various machine learning applications.