Functional Bilevel Optimization for Machine Learning

Bilevel optimization methods solve problems with hierarchical structures, optimizing two interdependent objectives: an inner-level objective and an outer-level one. Initially used in machine learning for model selection [Bennett et al., 2006] and sparse feature learning [Mairal et al., 2012], these methods gained popularity as efficient alternatives to grid search for hyper-parameter tuning [Feurer and Hutter,