Sparse and Locally Constant Gaussian Graphical Models

By Jean Honorio, Luis E. Ortiz, Dimitris Samaras, Nikos Paragios, Rita Z. Goldstein. 2009

28 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Yu Zhang
2. Qinliang Su
3. Lawrence Carin
4. Changyou Chen
5. Xuejun Liao
6. Qiang Yang
7. Chunyuan Li
8. Yik-Chung Wu

Citations in the corpus

• Nonlinear Statistical Learning with Truncated Gaussian Graphical Models (2016, background): “Gaussian graphical models (GGM) constitute a subset of graphical models that have found successful application in a diverse range of areas (Honorio et al., 2009; Liu & Will-sky, 2013; Oh & Deasy, 2014; Meng et al., 2014; Su & Wu, 2015a;b).”
• Multi-Task Learning of Gaussian Graphical Models (2010, background): “…models for singletask learning, for enforcing diagonal structure (Levina et al., 2008), block structure for known block-variable assignments (Duchi et al., 2008a) and unknown blockvariable assignments (Marlin & K.Murphy, 2009; Marlin et al., 2009), or spatial coherence (Honorio et al., 2009).”
• Distributed Estimation of Variance in Gaussian Graphical Model via Belief Propagation: Accuracy Analysis and Improvement (2015, background): “Since many physical processes and phenomenons can be modeled by Gaussian graphical models, they are widely used in applications ranging from computer vision [3], [4], image processing [5], oceanography [6], gene regulatory networks [7], cellular communication networks [8] to wireless sensor…”