Sparse Bayesian Lasso via a Variable-Coefficient $\ell_1$ Penalty. (arXiv:2211.05089v2 [stat.ME] UPDATED)

Modern statistical learning algorithms are capable of amazing flexibility,
but struggle with interpretability. One possible solution is sparsity: making
inference such that many of the parameters are estimated as being identically
0, which may be imposed through the use of nonsmooth penalties such as the
$\ell_1$ penalty. However, the $\ell_1$ penalty introduces significant bias
when high sparsity is desired. In this article, we retain the $\ell_1$ penalty,
but define learnable penalty weights $\lambda_p$ endowed with hyperpriors. We
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arxiv bayesian lasso