2024-09-05-echave-sustaeta-rodriguez24a.md

title	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Latent Gaussian Graphical Models with Golazo Penalty	The existence of latent variables in practical problems is common, for example when some variables are difficult or expensive to measure, or simply unknown. When latent variables are unaccounted for, structure learning for Gaussian graphical models can be blurred by additional correlation between the observed variables that is incurred by the latent variables. A standard approach for this problem is a latent version of the graphical lasso that splits the inverse covariance matrix into a sparse and a low-rank part that are penalized separately. In this paper we propose a generalization of this via the flexible Golazo penalty. This allows us to introduce latent versions of for example the adaptive lasso, positive dependence constraints or predetermined sparsity patterns, and combinations of those. We develop an algorithm for the latent Gaussian graphical model with the Golazo penalty and demonstrate it on simulated and real data.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	echave-sustaeta-rodriguez24a	0	Latent Gaussian Graphical Models with Golazo Penalty	199	212	199-212	199	false	Echave-Sustaeta Rodr{\'i}guez, Ignacio and R{\"o}ttger, Frank	given family Ignacio Echave-Sustaeta Rodríguez given family Frank Röttger	2024-09-05		Proceedings of The 12th International Conference on Probabilistic Graphical Models	246	inproceedings	date-parts 2024 9 5	https://raw.githubusercontent.com/mlresearch/v246/main/assets/echave-sustaeta-rodriguez24a/echave-sustaeta-rodriguez24a.pdf