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A Consistent and Scalable Algorithm for Best Subset Selection in Single Index Models
SIMs provide an interpretable and flexible modeling framework for high-dimensional data, while best subset selection aims to find a sparse model from a large set of predictors.
Simultaneous Best Subset Selection and Dimension Reduction via Primal-Dual Iterations
Sparse reduced rank regression is an essential statistical learning method.
Optimal and Safe Estimation for High-Dimensional Semi-Supervised Learning
Our goal is to investigate when and how the unlabeled data can be exploited to improve the estimation of the regression parameters of linear model in light of the fact that such linear models may be misspecified in data analysis.
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