Horseshoe Regularization for Machine Learning in Complex and Deep Models

24 Apr 2019Anindya BhadraJyotishka DattaYunfan LiNicholas G. Polson

Since the advent of the horseshoe priors for regularization, global-local shrinkage methods have proved to be a fertile ground for the development of Bayesian methodology in machine learning, specifically for high-dimensional regression and classification problems. They have achieved remarkable success in computation, and enjoy strong theoretical support... (read more)

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