Implicit Differentiation for Hyperparameter Tuning the Weighted Graphical Lasso
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Abstract (arXiv)
We provide a framework and algorithm for tuning the hyperparameters of the Graphical Lasso via a bilevel optimization problem solved with a first-order method. In particular, we derive the Jacobian of the Graphical Lasso solution with respect to its regularization hyperparameters.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 6 of its 19 references that are in the map. First posted on arXiv 5 Jul 2023.
Builds on
6 of 19 references.
References · 6
- 2021Convex optimizationcited by 2,479
- 2017Forward and Reverse Gradient-Based Hyperparameter Optimizationcited by 232
- 2019GLAD: Learning Sparse Graph Recoverycited by 23
- 2021Implicit differentiation for fast hyperparameter selection in non-smooth convex learningcited by 22
- 2014Local Linear Convergence of Forward-Backward under Partial Smoothnesscited by 13
- 2018A Proximal Approach for a Class of Matrix Optimization Problemscited by 8
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