Sparse Logistic Regression Learns All Discrete Pairwise Graphical Models
neural information processing systems, pp. 8069-8079, 2019.
sample complexitylogistic regressionweight vectormarkov graphconvex program
We characterize the effectiveness of a classical algorithm for recovering the Markov graph of a general discrete pairwise graphical model from i.i.d. samples. The algorithm is (appropriately regularized) maximum conditional log-likelihood, which involves solving a convex program for each node; for Ising models this is 1-constrained logist...More
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