Optimal transport (OT) methods seek a transformation map (or plan) between two probability
measures, such that the transformation has the minimum transportation cost. Such a …

In this article, we propose a model-free conditional feature screening method with false
discovery rate (FDR) control for ultra-high dimensional data. The proposed method is built …

Background and objective: Identifying active features from ultra-high dimensional data is
one of the primary and vital tasks in statistical learning and biological discovery. Methods: In …

This paper studies data with mass features, commonly observed in applications such as text
classification and medical diagnosis. We allow data to have several structures without …

Identifying significant variables for the high-dimensional logistic regression model is a
fundamental problem in modern statistics and machine learning. This paper introduces a …

The logistic regression model is a simple and classic approach to binary classification,
where in sparse high-dimensional settings, one believes that only a small proportion of the …

Variable selection is a procedure to obtain truly important predictors from inputs. Complex
nonlinear dependencies and strong coupling pose great challenges for variable selection in …

We consider the model-free feature screening problem that aims to discard non-informative
features before downstream analysis. Most of the existing feature screening approaches …

Feature screening is an important tool in analysing ultrahigh‐dimensional data, particularly
in the field of Omics and oncology studies. However, most attention has been focused on …

Learning the conditional dependence structures through high-dimensional graphical models
is of fundamental importance in many contemporary applications. Despite the fast growing …