Learning Convex Inference of Marginals
published: July 30, 2008, recorded: July 2008, views: 3255
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Graphical models trained using maximum likelihood are a common tool for probabilistic inference of marginal distributions. However, this approach suffers difficulties when either the inference process of the model is approximate. In this paper, the inference process is first defined to be the minimization of a convex function, inspired by free energy approximations. Learning is then done directly in terms of the performance of the inference process at univariate marginal prediction. The main novelty is that this is a direct minimization of empirical risk, where the risk measures the accuracy of predicted marginals.
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