arXiv research

New approach safely screens features and samples simultaneously for sparse modeling.

Safe sample screening improves RSVM performance without sacrificing accuracy.

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

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

Sparse classifiers such as the support vector machines (SVM) are efficient in test-phases because the classifier is characterized only by a subset of the samples called support vectors (SVs), and the rest of the samples (non SVs) have no influence on the classification result. However, the advantage of the sparsity has…

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…

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…

We introduce a new approach to variable selection, called Predictive Correlation Screening, for predictor design. Predictive Correlation Screening (PCS) implements false positive control on the selected variables, is well suited to small sample sizes, and is scalable to high dimensions. We establish asymptotic bounds f…

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

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

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

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

New estimator learns graph of Ising models efficiently and optimally.

Simplified screening tests for data points in optimization.

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…

A new distributed method speeds up sparse model training.

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…

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

Method screens weakly associated predictors in high-dimensional data.

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

New group testing method uses Belief Propagation for accurate screening.

The paper introduces localized conformal p-values for conditional testing problems.

A new screening rule speeds up OWL regression solving.

Paper proposes automated depression screening using deep CNNs.

DeepFS uses deep neural networks to select significant features in ultra high-dimensional data.

We propose {graphical sure screening}, or GRASS, a very simple and computationally-efficient screening procedure for recovering the structure of a Gaussian graphical model in the high-dimensional setting. The GRASS estimate of the conditional dependence graph is obtained by thresholding the elements of the sample covar…

Decision stumps accurately screen variables in nonparametric models.

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

This paper proposes a general adaptive procedure for budget-limited predictor design in high dimensions called two-stage Sampling, Prediction and Adaptive Regression via Correlation Screening (SPARCS). SPARCS can be applied to high dimensional prediction problems in experimental science, medicine, finance, and engineer…

Variable selection in high-dimensional space characterizes many contemporary problems in scientific discovery and decision making. Many frequently-used techniques are based on independence screening; examples include correlation ranking (Fan and Lv, 2008) or feature selection using a two-sample t-test in high-dimension…

This paper is concerned with the problems of interaction screening and nonlinear classification in a high-dimensional setting. We propose a two-step procedure, IIS-SQDA, where in the first step an innovated interaction screening (IIS) approach based on transforming the original $p$-dimensional feature vector is propose…

DRSSS method reduces model training costs by eliminating unsafe samples.

PDTS accelerates chemical space exploration using parallel and distributed Thompson sampling.

New method accurately estimates Ising model parameters and structure.

Lasso is a widely used regression technique to find sparse representations. When the dimension of the feature space and the number of samples are extremely large, solving the Lasso problem remains challenging. To improve the efficiency of solving large-scale Lasso problems, El Ghaoui and his colleagues have proposed th…

New method learns discrete graphical models efficiently.

RaSE screens variables via random subspaces, identifying joint effects.

Safe screening reduces the number of triplets in metric learning.

Deep learning uses ROC cost functions to improve virtual screening accuracy.

Two methods improve tensor recovery in Ising models, revealing gene interactions.

New Bayesian optimization models for efficient material screening.

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.

We propose a novel application of the Simultaneous Orthogonal Matching Pursuit (S-OMP) procedure for sparsistant variable selection in ultra-high dimensional multi-task regression problems. Screening of variables, as introduced in \cite{fan08sis}, is an efficient and highly scalable way to remove many irrelevant variab…

New screening rules speed up optimal design calculations.

We study the problem of variable selection in convex nonparametric regression. Under the assumption that the true regression function is convex and sparse, we develop a screening procedure to select a subset of variables that contains the relevant variables. Our approach is a two-stage quadratic programming method that…

The paper studies special null hypersurfaces in spacetimes.