11 citations · 37 across the 23 of their papers we have counts for
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stat.ME2017
Lasso Meets Horseshoe : A Survey
Anindya Bhadra, Jyotishka Datta, Nicholas G. Polson +1
The goal of this paper is to contrast and survey the major advances in two of the most commonly used high-dimensional techniques, namely, the Lasso and horseshoe regularization. La…
stat.ML2017★ 7 cited
Horseshoe Regularization for Feature Subset Selection
Anindya Bhadra, Jyotishka Datta, Nicholas G. Polson +1
Feature subset selection arises in many high-dimensional applications of statistics, such as compressed sensing and genomics. The penalty is ideal for this task, the cavea…