arXiv research

RaSE screens variables via random subspaces, identifying joint effects.

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

A new method for virtual drug screening detects top treatments.

New screening rules improve lasso model fitting efficiency.

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…

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

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

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…

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…

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…

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…

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 …

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

Screening methods improve model performance on 2 out of 10 datasets.

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

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

New online feature selection method handles streaming data with concept drift.

New method aggregates GDS analyses of randomly selected interaction models to identify important factors in screening experiments.

New AI platform screens portfolios for desirable firms and news.

Statistical inference can be computationally prohibitive in ultrahigh-dimensional linear models. Correlation-based variable screening, in which one leverages marginal correlations for removal of irrelevant variables from the model prior to statistical inference, can be used to overcome this challenge. Prior works on co…

Introduces screening rules for non-convex Lasso problems.

Safe sample screening improves RSVM performance without sacrificing accuracy.

A new distributed method speeds up sparse model training.

GIDS reduces high-dimensional response and predictor spaces, improving interpretability and computational efficiency.

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

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

A new screening rule speeds up OWL regression solving.

Screening rules help identify active sets in optimization problems.

Simplified screening tests for data points in optimization.

We present safe active incremental feature selection~(SAIF) to scale up the computation of LASSO solutions. SAIF does not require a solution from a heavier penalty parameter as in sequential screening or updating the full model for each iteration as in dynamic screening. Different from these existing screening methods,…

PS^2 selects assets then weights for high-dimensional investing.

We propose a flexible nonparametric regression method for ultrahigh-dimensional data. As a first step, we propose a fast screening method based on the favored smoothing bandwidth of the marginal local constant regression. Then, an iterative procedure is developed to recover both the important covariates and the regress…

New method detects biomarker-treatment interactions in clinical trials.

Decision stumps accurately screen variables in nonparametric models.

Safe screening improves generalized CGM's feature selection stability.

One way to solve lasso problems when the dictionary does not fit into available memory is to first screen the dictionary to remove unneeded features. Prior research has shown that sequential screening methods offer the greatest promise in this endeavor. Most existing work on sequential screening targets the context of …

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.

New ML method detects incomplete bid-rigging cartels.

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

In the present paper, we introduce screen transversal lightlike submanifolds of metallic semi-Riemannian manifolds with its subclasses, namely screen transversal anti-invariant, radical screen transversal and isotropic screen transversal lightlike submanifolds, and give an example. We show that there do not exist co-is…

DRSS method identifies unnecessary samples and features in DR covariate shift.

Safe screening rule reduces computational costs for Group OWL models.

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…

Method screens similar capsule endoscopic images, reducing doctor workload and improving accuracy.

DABS uses a policy network to select experiments in high-dimensional design spaces.

We study safe screening for metric learning. Distance metric learning can optimize a metric over a set of triplets, each one of which is defined by a pair of same class instances and an instance in a different class. However, the number of possible triplets is quite huge even for a small dataset. Our safe triplet scree…

Study improves reliability of neural models for virtual screening.