2024-04-18-andrew24a.md

title	software	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
GmGM: a fast multi-axis Gaussian graphical model	https://github.com/AIStats-GmGM/GmGM/	This paper introduces the Gaussian multi-Graphical Model, a model to construct sparse graph representations of matrix- and tensor-variate data. We generalize prior work in this area by simultaneously learning this representation across several tensors that share axes, which is necessary to allow the analysis of multimodal datasets such as those encountered in multi-omics. Our algorithm uses only a single eigendecomposition per axis, achieving an order of magnitude speedup over prior work in the ungeneralized case. This allows the use of our methodology on large multi-modal datasets such as single-cell multi-omics data, which was challenging with previous approaches. We validate our model on synthetic data and five real-world datasets.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	andrew24a	0	{GmGM}: a fast multi-axis {G}aussian graphical model	2053	2061	2053-2061	2053	false	Andrew, Ethan B. and Westhead, David and Cutillo, Luisa	given family Ethan B. Andrew given family David Westhead given family Luisa Cutillo	2024-04-18		Proceedings of The 27th International Conference on Artificial Intelligence and Statistics	238	inproceedings	date-parts 2024 4 18	https://proceedings.mlr.press/v238/andrew24a/andrew24a.pdf