# Efficient Regularization Parameter Selection for Latent Variable Graphical Models via Bi-Level Optimization

# Efficient Regularization Parameter Selection for Latent Variable Graphical Models via Bi-Level Optimization

## Joachim Giesen, Frank Nussbaum, Christopher Schneider

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence

Main track. Pages 2378-2384.
https://doi.org/10.24963/ijcai.2019/330

Latent variable graphical models are an extension of Gaussian graphical models that decompose the precision matrix into a sparse and a low-rank component. These models can be learned with theoretical guarantees from data via a semidefinite program. This program features two regularization terms, one for promoting sparsity and one for promoting a low rank. In practice, however, it is not straightforward to learn a good model since the model highly depends on the regularization parameters that control the relative weight of the loss function and the two regularization terms. Selecting good regularization parameters can be modeled as a bi-level optimization problem, where the upper level optimizes some form of generalization error and the lower level provides a description of the solution gamut. The solution gamut is the set of feasible solutions for all possible values of the regularization parameters. In practice, it is often not feasible to describe the solution gamut efficiently. Hence, algorithmic schemes for approximating solution gamuts have been
devised. One such scheme is Benson's generic vector optimization algorithm that comes with approximation guarantees. So far Benson's algorithm has not been used in conjunction with semidefinite programs like the latent variable graphical Lasso. Here, we develop an adaptive variant of Benson's algorithm for the semidefinite case and show that
it keeps the known approximation and run time guarantees. Furthermore, Benson's algorithm turns out to be practically more efficient for the latent variable graphical model than the existing solution gamut approximation scheme on a wide range of data sets.

Keywords:

Machine Learning: Learning Graphical Models

Machine Learning: Feature Selection ; Learning Sparse Models

Machine Learning: Unsupervised Learning