  Graphical model selection in Markov random fields is a fundamental problem in
statistics and machine learning. Two particularly prominent models, the Ising
model and Gaussian model, have largely developed in parallel using different
(though often related) techniques, and several practical algorithms with
rigorous sample complexity bounds have been established for each. In this
paper, we adapt a recently proposed algorithm of Klivans and Meka (FOCS, 2017),
based on the method of multiplicative weight updates, from the Ising model to
the Gaussian model, via non-trivial modifications to both the algorithm and its
analysis. The algorithm enjoys a sample complexity bound that is qualitatively
similar to others in the literature, has a low runtime $O(mp^2)$ in the case of
$m$ samples and $p$ nodes, and can trivially be implemented in an online
manner.
