On the Use of Variational Inference for Learning Discrete Graphical Model

Abstract

We study the general class of estimators for graphical model structure based on optimizing !1-regularized approximate loglikelihood, where the approximate likelihood uses tractable variational approximations of the partition function. We provide a message-passing algorithm that directly computes the !1 regularized approximate MLE. Further, in the case of… (More)

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Cite this paper

@inproceedings{Yang2011OnTU, title={On the Use of Variational Inference for Learning Discrete Graphical Model}, author={Eunho Yang and Pradeep Ravikumar}, booktitle={ICML}, year={2011} }