Independently Interpretable Lasso for Generalized Linear Models

Volume: 32, Issue: 6, Pages: 1168 - 1221
Published: Jun 1, 2020
Abstract
Sparse regularization such as [Formula: see text] regularization is a quite powerful and widely used strategy for high-dimensional learning problems. The effectiveness of sparse regularization has been supported practically and theoretically by several studies. However, one of the biggest issues in sparse regularization is that its performance is quite sensitive to correlations between features. Ordinary [Formula: see text] regularization...
Paper Details
Title
Independently Interpretable Lasso for Generalized Linear Models
Published Date
Jun 1, 2020
Volume
32
Issue
6
Pages
1168 - 1221
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