Stochastic Learning for Sparse Discrete Markov Random Fields with Controlled Gradient Approximation Error
Sinong Geng,
Zhaobin Kuang,
Jie Liu
et al.
Abstract:We study the L 1 -regularized maximum likelihood estimator/estimation (MLE) problem for discrete Markov random fields (MRFs), where efficient and scalable learning requires both sparse regularization and approximate inference.To address these challenges, we consider a stochastic learning framework called stochastic proximal gradient (SPG; Honorio 2012a, Atchade et al. 2014, Miasojedow andRejchel 2016). SPG is an inexact proximal gradient algorithm [Schmidt et al., 2011], whose inexactness stems from the stoch… Show more
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