Stochastic Learning for Sparse Discrete Markov Random Fields with Controlled Gradient Approximation Error.

ABSTRACT: We study the L 1-regularized maximum likelihood estimator/estimation (MLE) problemfor 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 etal. 2014, Miasojedow and Rejchel 2016). SPG is an inexact proximal gradient algorithm [Schmidt et al., 2011], whose inexactness stems from the stochastic oracle (Gibbs sampling) for gradient approximation - exact gradient evaluation is infeasible in general due to the NP-hard inference problem for discrete MRFs [Koller and Friedman, 2009]. Theoretically, we provide novel verifiable bounds to inspect and control the quality of gradi

SUBMITTER: Geng S 

PROVIDER: S-EPMC6292514 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature