Skip to yearly menu bar Skip to main content


Partially Linear Additive Gaussian Graphical Models

Sinong Geng · Minhao Yan · Mladen Kolar · Sanmi Koyejo

Pacific Ballroom #214

Keywords: [ Graphical Models ]

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.

Live content is unavailable. Log in and register to view live content