Learning Gaussian Graphical Models via Multiplicative Weights
AISTATS, pp. 1104-1114, 2020.
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 bound...More
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