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Found 4 papers, 1 papers with code
Adaptive Lasso, Transfer Lasso, and Beyond: An Asymptotic Perspective
This paper presents a comprehensive exploration of the theoretical properties inherent in the Adaptive Lasso and the Transfer Lasso.
Transfer Learning via $\ell_1$ Regularization
The proposed method has a tight estimation error bound under a stationary environment, and the estimate remains unchanged from the source estimate under small residuals.
HMLasso: Lasso with High Missing Rate
Convex Conditioned Lasso (CoCoLasso) has been proposed for dealing with high-dimensional data with missing values, but it performs poorly when there are many missing values, so that the high missing rate problem has not been resolved.
Independently Interpretable Lasso: A New Regularizer for Sparse Regression with Uncorrelated Variables
However, one of the biggest issues in sparse regularization is that its performance is quite sensitive to correlations between features.
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