A Generic Family of Graphical Models: Diversity, Efficiency, and Heterogeneity

Yufei Huang, Changhu Wang, Junjie Tang, Weichi Wu, Ruibin Xi
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:25876-25907, 2025.

Abstract

Traditional network inference methods, such as Gaussian Graphical Models, which are built on continuity and homogeneity, face challenges when modeling discrete data and heterogeneous frameworks. Furthermore, under high-dimensionality, the parameter estimation of such models can be hindered by the notorious intractability of high-dimensional integrals. In this paper, we introduce a new and flexible device for graphical models, which accommodates diverse data types, including Gaussian, Poisson log-normal, and latent Gaussian copula models. The new device is driven by a new marginally recoverable parametric family, which can be effectively estimated without evaluating the high-dimensional integration in high-dimensional settings thanks to the marginal recoverability. We further introduce a mixture of marginally recoverable models to capture ubiquitous heterogeneous structures. We show the validity of the desirable properties of the models and the effective estimation methods, and demonstrate their advantages over the state-of-the-art network inference methods via extensive simulation studies and a gene regulatory network analysis of real single-cell RNA sequencing data.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-huang25aj, title = {A Generic Family of Graphical Models: Diversity, Efficiency, and Heterogeneity}, author = {Huang, Yufei and Wang, Changhu and Tang, Junjie and Wu, Weichi and Xi, Ruibin}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {25876--25907}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/huang25aj/huang25aj.pdf}, url = {https://proceedings.mlr.press/v267/huang25aj.html}, abstract = {Traditional network inference methods, such as Gaussian Graphical Models, which are built on continuity and homogeneity, face challenges when modeling discrete data and heterogeneous frameworks. Furthermore, under high-dimensionality, the parameter estimation of such models can be hindered by the notorious intractability of high-dimensional integrals. In this paper, we introduce a new and flexible device for graphical models, which accommodates diverse data types, including Gaussian, Poisson log-normal, and latent Gaussian copula models. The new device is driven by a new marginally recoverable parametric family, which can be effectively estimated without evaluating the high-dimensional integration in high-dimensional settings thanks to the marginal recoverability. We further introduce a mixture of marginally recoverable models to capture ubiquitous heterogeneous structures. We show the validity of the desirable properties of the models and the effective estimation methods, and demonstrate their advantages over the state-of-the-art network inference methods via extensive simulation studies and a gene regulatory network analysis of real single-cell RNA sequencing data.} }
Endnote
%0 Conference Paper %T A Generic Family of Graphical Models: Diversity, Efficiency, and Heterogeneity %A Yufei Huang %A Changhu Wang %A Junjie Tang %A Weichi Wu %A Ruibin Xi %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-huang25aj %I PMLR %P 25876--25907 %U https://proceedings.mlr.press/v267/huang25aj.html %V 267 %X Traditional network inference methods, such as Gaussian Graphical Models, which are built on continuity and homogeneity, face challenges when modeling discrete data and heterogeneous frameworks. Furthermore, under high-dimensionality, the parameter estimation of such models can be hindered by the notorious intractability of high-dimensional integrals. In this paper, we introduce a new and flexible device for graphical models, which accommodates diverse data types, including Gaussian, Poisson log-normal, and latent Gaussian copula models. The new device is driven by a new marginally recoverable parametric family, which can be effectively estimated without evaluating the high-dimensional integration in high-dimensional settings thanks to the marginal recoverability. We further introduce a mixture of marginally recoverable models to capture ubiquitous heterogeneous structures. We show the validity of the desirable properties of the models and the effective estimation methods, and demonstrate their advantages over the state-of-the-art network inference methods via extensive simulation studies and a gene regulatory network analysis of real single-cell RNA sequencing data.
APA
Huang, Y., Wang, C., Tang, J., Wu, W. & Xi, R.. (2025). A Generic Family of Graphical Models: Diversity, Efficiency, and Heterogeneity. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:25876-25907 Available from https://proceedings.mlr.press/v267/huang25aj.html.

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