Partially linear additive Gaussian graphical models

Sinong Geng, Minhao Yan, Mladen Kolar, Oluwasanmi Koyejo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 Li-regularized maximal pseudo-profile likelihood estimator (MaP-PLE) for which we prove √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.

Original languageEnglish (US)
Title of host publication36th International Conference on Machine Learning, ICML 2019
PublisherInternational Machine Learning Society (IMLS)
Pages3827-3847
Number of pages21
ISBN (Electronic)9781510886988
StatePublished - Jan 1 2019
Event36th International Conference on Machine Learning, ICML 2019 - Long Beach, United States
Duration: Jun 9 2019Jun 15 2019

Publication series

Name36th International Conference on Machine Learning, ICML 2019
Volume2019-June

Conference

Conference36th International Conference on Machine Learning, ICML 2019
CountryUnited States
CityLong Beach
Period6/9/196/15/19

ASJC Scopus subject areas

  • Education
  • Computer Science Applications
  • Human-Computer Interaction

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  • Cite this

    Geng, S., Yan, M., Kolar, M., & Koyejo, O. (2019). Partially linear additive Gaussian graphical models. In 36th International Conference on Machine Learning, ICML 2019 (pp. 3827-3847). (36th International Conference on Machine Learning, ICML 2019; Vol. 2019-June). International Machine Learning Society (IMLS).