Bilevel Learning

Bilevel learning tackles nested optimization problems, where an outer problem optimizes hyperparameters or high-level strategies while an inner problem solves a lower-level task, often involving model training or parameter estimation. Current research focuses on developing efficient algorithms, particularly for non-convex lower-level problems, with applications ranging from hyperparameter optimization and inverse problems to reinforcement learning and generative models. This framework offers a powerful approach to improve model performance and robustness across diverse fields by learning optimal parameters or control strategies directly from data, leading to more effective and adaptable solutions.