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We show that in high dimensional distributions, i.e., the regime where the alphabet size of each node is comparable to the number of observations, the Chow-Liu algorithm on learning graphical models is highly sub-optimal. We propose a new approach, where the key ingredient is to replace the empirical mutual information in the Chow-Liu algorithm with a minimax rate-optimal estimator proposed recently...
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