arXiv research

Method screens weakly associated predictors in high-dimensional data.

RaSE screens variables via random subspaces, identifying joint effects.

Extends variable screening for ultrahigh-dimensional models, reducing dimensionality to sample size.

Safe screening reduces the number of triplets in metric learning.

The problem of learning a sparse model is conceptually interpreted as the process of identifying active features/samples and then optimizing the model over them. Recently introduced safe screening allows us to identify a part of non-active features/samples. So far, safe screening has been individually studied either fo…

New Bayesian optimization models for efficient material screening.

This paper treats the problem of screening for variables with high correlations in high dimensional data in which there can be many fewer samples than variables. We focus on threshold-based correlation screening methods for three related applications: screening for variables with large correlations within a single trea…

New screening rules improve lasso model fitting efficiency.

A new screening rule 'dynamic Sasvi' improves sparse optimization speed.

Study on lightlike submanifolds in metallic semi-Riemannian manifolds.

The paper studies special null hypersurfaces in spacetimes.

New AI platform screens portfolios for desirable firms and news.

New screening test for LASSO reduces complexity.

A variable screening procedure via correlation learning was proposed Fan and Lv (2008) to reduce dimensionality in sparse ultra-high dimensional models. Even when the true model is linear, the marginal regression can be highly nonlinear. To address this issue, we further extend the correlation learning to marginal nonp…

Recent computational strategies based on screening tests have been proposed to accelerate algorithms addressing penalized sparse regression problems such as the Lasso. Such approaches build upon the idea that it is worth dedicating some small computational effort to locate inactive atoms and remove them from the dictio…

A new screening method for high-dimensional data reduces computational cost.

A new method for virtual drug screening detects top treatments.

A new screening rule improves lasso solving speed.

Recently, to solve large-scale lasso and group lasso problems, screening rules have been developed, the goal of which is to reduce the problem size by efficiently discarding zero coefficients using simple rules independently of the others. However, screening for overlapping group lasso remains an open challenge because…

Study the geometry of specific submanifolds in Golden Semi-Riemannian manifolds.

Safe sample screening improves RSVM performance without sacrificing accuracy.

In data sets with many more features than observations, independent screening based on all univariate regression models leads to a computationally convenient variable selection method. Recent efforts have shown that in the case of generalized linear models, independent screening may suffice to capture all relevant feat…

Introduces screening rules for non-convex Lasso problems.

Efficiently allocate budgets for LLM-assisted virtual screening to reduce costs.

Deep learning predicts breast cancer with high accuracy from patient data.

New rules reduce SLOPE model fitting time by screening out irrelevant variables.

A new method reduces feature screening cost from $O(np)$ to $O(\sqrt{n}p)$.

The paper studies half-lightlike submanifolds in Lorentzian manifolds with specific distributions.

To find efficient screening methods for high dimensional linear regression models, this paper studies the relationship between model fitting and screening performance. Under a sparsity assumption, we show that a subset that includes the true submodel always yields smaller residual sum of squares (i.e., has better model…

Deep CNN model improves breast cancer screening exam classification.

This paper introduces LR-FFS for robust feature screening in federated learning under label shift.

Variable screening is a fast dimension reduction technique for assisting high dimensional feature selection. As a preselection method, it selects a moderate size subset of candidate variables for further refining via feature selection to produce the final model. The performance of variable screening depends on both com…

New types of null hypersurfaces found in Sasakian space-forms.

The paper introduces a new screening method for faster L1 regularization.

Model shows screening for infectious disease is hard but Thompson sampling works well.

A new method reduces feature size in CRFs for faster training.

The paper explores null hypersurfaces in Lorentzian manifolds using geometric immersions.

Study Einstein warped-product manifolds with specific curvature conditions.

New models compare mammograms to improve cancer diagnosis.

SRF learns sparse rule models by screening out features efficiently.

The paper studies lightlike submanifolds in bronze semi-Riemannian manifolds with specific geometric properties.

Study on null hypersurfaces in complex contact manifolds.

Screening rules help identify active sets in optimization problems.

A new feature screening method using projection correlation and knockoffs controls FDR in high-dimensional data.

Develops a new screening method called Newton screening for faster and more accurate sparse learning.

Safe screening rules reduce $\ell_0$-regression computation by fixing 76% of variables.

Simplified screening tests for data points in optimization.

Understanding how features interact with each other is of paramount importance in many scientific discoveries and contemporary applications. Yet interaction identification becomes challenging even for a moderate number of covariates. In this paper, we suggest an efficient and flexible procedure, called the interaction …