ICML 2019oral14 citations
Partially Linear Additive Gaussian Graphical Models
Sinong Geng, Minhao Yan, Mladen Kolar, Sanmi Koyejo
Abstract
We propose a partially linear additive Gaussian graphical model (PLA-GGM) for the estimation of associations between random variables distorted by observed confounders. Model parameters are estimated using an $L_1$-regularized maximal pseudo-profile likelihood estimator (MaPPLE) for which we prove a $\sqrt{n}$-sparsistency. Importantly, our approach avoids parametric constraints on the effects of confounders on the estimated graphical model structure. Empirically, the PLA-GGM is applied to both synthetic and real-world datasets, demonstrating superior performance compared to competing methods.
BibTeX
@InProceedings{pmlr-v97-geng19a,
title = {Partially Linear Additive {G}aussian Graphical Models},
author = {Geng, Sinong and Yan, Minhao and Kolar, Mladen and Koyejo, Sanmi},
booktitle = {Proceedings of the 36th International Conference on Machine Learning},
pages = {2180--2190},
year = {2019},
editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
volume = {97},
series = {Proceedings of Machine Learning Research},
month = {09--15 Jun},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v97/geng19a/geng19a.pdf},
url = {https://proceedings.mlr.press/v97/geng19a.html},
abstract = {We propose a partially linear additive Gaussian graphical model (PLA-GGM) for the estimation of associations between random variables distorted by observed confounders. Model parameters are estimated using an $L_1$-regularized maximal pseudo-profile likelihood estimator (MaPPLE) for which we prove a $\sqrt{n}$-sparsistency. Importantly, our approach avoids parametric constraints on the effects of confounders on the estimated graphical model structure. Empirically, the PLA-GGM is applied to both synthetic and real-world datasets, demonstrating superior performance compared to competing methods.}
}