GradVI optimizes the variational empirical Bayes regression objective with quasi-Newton methods, achieving similar accuracy to CAVI but faster convergence on correlated predictors and much faster trend filtering.
Studies in the history of probability and statistics XL Boscovich, Simpson and a 1760 manuscript note on fitting a linear relation
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Gradient-based optimization for variational empirical Bayes multiple regression
GradVI optimizes the variational empirical Bayes regression objective with quasi-Newton methods, achieving similar accuracy to CAVI but faster convergence on correlated predictors and much faster trend filtering.