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Found 3 papers, 1 papers with code
Principal component-guided sparse regression
We propose a new method for supervised learning, especially suited to wide data where the number of features is much greater than the number of observations.
A Pliable Lasso
We propose a generalization of the lasso that allows the model coefficients to vary as a function of a general set of modifying variables.
Methodology
A Blockwise Descent Algorithm for Group-penalized Multiresponse and Multinomial Regression
In this paper we purpose a blockwise descent algorithm for group-penalized multiresponse regression.
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