  This paper studies how to capture dependency graph structures from real data
which may not be Gaussian. Starting from marginal loss functions not
necessarily derived from probability distributions, we utilize an additive
over-parametrization with shrinkage to incorporate variable dependencies into
the criterion. An iterative Gaussian graph learning algorithm is proposed with
ease in implementation. Statistical analysis shows that the estimators achieve
satisfactory accuracy with the error measured in terms of a proper Bregman
divergence. Real-life examples in different settings are given to demonstrate
the efficacy of the proposed methodology.

Machine Learning;Methodology