Group selection and shrinkage: Structured sparsity for semiparametric additive models

arXiv:2105.12081v3 Announce Type: replace-cross
Abstract: Sparse regression and classification estimators that respect group structures have application to an assortment of statistical and machine learning problems, from multitask learning to sparse additive modeling to hierarchical selection. This work introduces structured sparse estimators that combine group subset selection with shrinkage. To accommodate sophisticated structures, our estimators allow for arbitrary overlap between groups. We develop an optimization framework for fitting the nonconvex regularization surface and present finite-sample error bounds for estimation of the …

abstract application arxiv classification hierarchical machine machine learning modeling multitask learning regression shrinkage sparsity statistical stat.me stat.ml type work