Implicit Differentiation for Hyperparameter Tuning the Weighted Graphical Lasso

This work addresses hyperparameter tuning for the Graphical Lasso, which is incremental as it applies an existing implicit differentiation technique to a specific model.

The authors tackled the problem of hyperparameter tuning for the Graphical Lasso by developing a framework and algorithm based on implicit differentiation and bilevel optimization, resulting in a first-order method that computes the Jacobian of the solution with respect to hyperparameters.