Penalized (or regularized) regression, as represented by lasso and its variants, has become
a standard technique for analyzing high-dimensional data when the number of variables …

Although the Lasso has been extensively studied, the relationship between its prediction
performance and the correlations of the covariates is not fully understood. In this paper, we …

We introduce a novel approach for high-dimensional regression with theoretical guarantees.
The new procedure overcomes the challenge of tuning parameter selection of Lasso and …

The task of tuning regularization coefficients in regularized regression models with provable
guarantees across problem instances still poses a significant challenge in the literature. This …

Sparse regression models are increasingly prevalent due to their ease of interpretability and
superior out-of-sample performance. However, the exact model of sparse regression with an …

Graphical models are widely used to model stochastic dependences among large
collections of variables. We introduce a new method of estimating undirected conditional …

An important unresolved challenge in the theory of regularization is to set the regularization
coefficients of popular techniques like the ElasticNet with general provable guarantees. We …

Multi-task learning has emerged as a powerful machine learning paradigm for integrating
data from multiple sources, leveraging similarities between tasks to improve overall model …

This paper concerns statistical inference for the components of a high-dimensional
regression parameter despite possible endogeneity of each regressor. Given a first-stage …

Objectives: To investigate potential diagnostic model for predicting benign or malignant
status of subcentimeter pulmonary ground-glass nodules (SPGGNs)(≤ 1 cm) based on CT …