Implicit Differentiation for Hyperparameter Tuning the Weighted Graphical Lasso

Can Pouliquen, Paulo Gonçalves, Mathurin Massias, Titouan Vayer
7/5/2023
cs.LGmath.OCstat.ML

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Abstract

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.

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Cite this paper

@article{pouliquen2023implicit,
  title  = {Implicit Differentiation for Hyperparameter Tuning the Weighted Graphical Lasso},
  author = {Can Pouliquen and Paulo Gonçalves and Mathurin Massias and Titouan Vayer},
  year   = {2023},
  eprint = {2307.02130},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2307.02130v1}
}

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