On Use of the EM Algorithm for Penalized Likelihood Estimation

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Improving a Neural Semantic Parser by Counterfactual Learning from Human Bandit Feedback (2018, methodology): “The idea is inspired by one-step-late algorithms that have been introduced for EM algorithms (Green, 1990).”
Richardson–Lucy algorithm with total variation regularization for 3D confocal microscope deconvolution (2006, methodology): “(10)), and we adopt an explicit scheme, as in (Green, 1990), defined by:”
Penalized Model-Based Clustering with Application to Variable Selection (2007, methodology): “The derivation closely follows from that for standard model-based clustering (McLachlan and Peel, 2002) and the general methodology for penalized likelihood (Green, 1990).”
INFERENCE FOR NORMAL MIXTURES IN MEAN AND VARIANCE (2008, unknown): “Often, the penalized log-likelihood function also increases after each EM iteration (Green (1990)) and the algorithm converges quickly.”
Joint additive Kullback–Leibler residual minimization and regularization for linear inverse problems (2007, background): “Among other things, it was shown that this form of regularization is intimately connected to the EM (Estimate and Maximize) algorithm [43] for maximum likelihood functional estimation.”