Variable selection is a challenging issue in statistical applications when the number of predictors far exceeds the number of observations . In this ultra-high dimensional setting, the sure independence screening (SIS) procedure was introduced to significantly reduce the dimensionality by preserving the true mod…
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Ultrahigh-dimensional variable selection plays an increasingly important role in contemporary scientific discoveries and statistical research. Among others, Fan and Lv [J. R. Stat. Soc. Ser. B Stat. Methodol. 70 (2008) 849-911] propose an independent screening framework by ranking the marginal correlations. They showed…
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…
Independent screening for single-index hazard rate models with ultra-high dimensional featuresstat.ML
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 screening method for high-dimensional data reduces computational cost.
problem Challenges in variable selection for ultrahigh-dimensional linear regression.
method Ordering absolute sample ridge partial correlations to screen variables.
result The method provides sure screening property without strong assumptions.
A new method for identifying interactions in high dimensions.
problem Challenges in identifying interactions with many covariates.
method Interaction pursuit (IP) procedure: feature screening and selection.
result The method screens interactions separately from main effects, improving effectiveness.
RaSE screens variables via random subspaces, identifying joint effects.
problem Missing joint effects of predictors in ultra-high dimensional data.
method Random Subspace Ensemble (RaSE) framework combining subspace evaluation criteria.
result RaSE identifies signals with no marginal effect or high-order interactions.
A new method reduces feature screening cost from to .
problem Eliminating non-informative features in ultrahigh-dimensional datasets.
method Adaptive subsampling method based on multi-armed bandit problem.
result The proposed method retains sure screening property and comparable performance to SIS.
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…
A new feature screening method using projection correlation and knockoffs controls FDR in high-dimensional data.
problem Feature selection in ultra-high dimensional datasets with heavy-tailed errors and multivariate responses.
method Projection correlation for dependence measurement, knockoffs for FDR control, two-step approach.
result The method controls FDR and ensures sure screening under weak assumptions.
IPDC method identifies interactions in high-dimensional multi-response models efficiently.
problem Identifying important interactions in high-dimensional multi-response models.
method Two-stage method using distance correlation for feature screening followed by feature selection.
result IPDC method is computationally efficient and effective even with diverging number of responses.
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…
AJL framework detects dynamic patterns in high-dimensional time-varying models.
problem Complex time-varying associations and abrupt regime shifts in longitudinal processes.
method Hierarchical regularization framework integrating functional variable selection with structural changepoint detection.
result The refined estimator achieves the oracle property in ultra-high-dimensional settings.
Sparse learning techniques have been routinely used for feature selection as the resulting model usually has a small number of non-zero entries. Safe screening, which eliminates the features that are guaranteed to have zero coefficients for a certain value of the regularization parameter, is a technique for improving t…
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 -dimensional feature vector is propose…
Variable selection in high dimensional space has challenged many contemporary statistical problems from many frontiers of scientific disciplines. Recent technology advance has made it possible to collect a huge amount of covariate information such as microarray, proteomic and SNP data via bioimaging technology while ob…
Extends variable screening for ultrahigh-dimensional models, reducing dimensionality to sample size.
problem Statistical inference challenges in ultrahigh-dimensional linear models.
method Extends correlation-based variable screening to arbitrary linear models and post-screening inference techniques.
result Shows a condition (screening condition) sufficient for successful variable screening in arbitrary linear models.
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…
A new distributed method speeds up sparse model training.
problem Efficiently training models with massive samples and high-dimensional features.
method Distributed Dynamic Safe Screening (DDSS) method for sparsity regularized models.
result Achieves linear convergence rate and eliminates almost all inactive features.
A fast MCMC sampler for sparse Bayesian inference.
problem Sparse Bayesian inference problems with high computational cost.
method Asynchronous Gibbs sampler extended with data sub-sampling.
result The Markov chain admits an invariant distribution that recovers the main signal with high probability.
DC-SIS selects features faster than mRMR for Parkinson's vocal diagnosis.
problem Feature selection for Parkinson's disease vocal data.
method DC-SIS (Distance Correlation Sure Independence Screening) using distance correlation measure.
result 90 times faster feature selection with similar accuracy.
Proposes a model for classifying high-dimensional time series with interpretable parameters.
problem Challenges in classifying high-dimensional time series, especially in neuroscience.
method Model-based approach using sparsity in inverse spectral density matrices, with interpretability of model parameters.
result Model demonstrates consistency and sure screening property, enabling nuanced inferences.
Paper reveals how lasso screening can be simplified.
problem Efficiently solving the lasso problem for large datasets.
method Uses the projection of features onto subspace spanned by normals of half spaces to reduce dimensionality.
result Optimization problem in high dimensions can be simplified to lower dimensions.
Study on umbilical submanifolds in specific geometric spaces.
problem Characterizing curvature properties of umbilical submanifolds.
method Analyzing totally and screen totally umbilical half lightlike submanifolds in almost contact B-metric manifolds.
result Curvature properties of these submanifolds were studied and characterized.
New online feature selection method handles streaming data with concept drift.
problem Handling streaming data with concept drift and sparsity.
method Online feature screening method with model adaptation.
result Online screening methods with model adaptation outperform without model adaptation on data streams with concept drift.
C-SURE improves complex-valued deep learning models by shrinking estimates, outperforming MLE and SurReal.
problem Improving accuracy and robustness of complex-valued deep learning models.
method Proposes a Stein's unbiased risk estimate (SURE) for complex-valued data and integrates it into a prototype CNN classifier.
result C-SURE outperforms SurReal and MLE in accuracy and robustness on complex-valued datasets.
We introduce the notion of a stationary random manifold and develop the basic entropy theory for it. Examples include manifolds admitting a compact quotient under isometries and generic leaves of a compact foliation. We prove that the entropy of an ergodic stationary random manifold is zero if and only if the manifold …
The study improves compound selection in in silico screening by focusing on model's ability to predict desirable outcomes.
problem Improving compound selection in in silico screening to reduce errors and enhance generalization.
method Extending learning theory, the study analyzes the impact of selection policies on generalization and proposes a method to mitigate challenges.
result Generalization can be enhanced by considering a model's ability to predict the fraction of desired outcomes in a batch.
Screening rules help identify active sets in optimization problems.
problem Identifying active sets in optimization problems.
method Screening rules based on subdifferential sets and optimality conditions.
result The number of iterations needed depends only on the convergence rate.
New neural network models predict molecular properties without 3D geometry, speeding up high-throughput screening.
problem Predicting molecular properties for large, complex molecules without computationally expensive 3D geometry.
method Message-passing neural networks trained with and without 3D structural information.
result Message-passing neural networks achieve similar accuracy to state-of-the-art methods without 3D geometry.
Screening Data Points in Empirical Risk Minimization via Ellipsoidal Regions and Safe Loss Functionscs.LG
Simplified screening tests for data points in optimization.
problem Discarding irrelevant data points in empirical risk minimization.
method Designing loss functions and regularizing convex losses to induce sparsity, using ellipsoidal approximations.
result Automatic discarding of data samples without losing optimization guarantees.
Better subset regressionstat.ME
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…
The paper studies lightlike submanifolds in bronze semi-Riemannian manifolds with specific geometric properties.
problem Characterizing and understanding lightlike submanifolds in bronze semi-Riemannian manifolds.
method Characterization theorems on geodesicity, integrability, and parallelism of distributions.
result No coisotropic, isotropic, or totally proper screen generic lightlike submanifolds exist.
Safe screening improves generalized CGM's feature selection stability.
problem Improving feature selection stability in generalized CGM.
method Coupling safe screening with generalized CGM.
result Safe screening matches solution support at rate .
New method screens important covariates in ultrahigh-dimensional data.
problem Handling ultrahigh-dimensional data for regression analysis.
method Favored smoothing bandwidth screening followed by iterative recovery.
result Screening method proves model selection consistency.
Random quotients preserve hyperbolic properties in groups.
problem Preserving hyperbolic properties in random group quotients.
method Independent random walks, spinning families, projection complexes.
result Random quotients of acylindrical and hierarchical hyperbolic groups remain so.
We study Weyl structures on lightlikes hypersurfaces endowed with a conformal structure of certain type and specific screen distribution: the Weyl screen structures. We investigate various differential geometric properties of Einstein-Weyl screen structures on lightlike hypersurfaces and show that, for ambiant Lorentzi…
Develops a new essential supremum concept for financial models.
problem Uncertainty in financial models with non-dominated, non-compact probability measures.
method Introduces quasi-sure essential supremum for real-valued functions and proves its properties.
result Bi-dual characterization of super-hedging cost and new results on aggregation of quasi-sure statements.
We study two global structural properties of a graph , denoted AS and CFS, which arise in a natural way from geometric group theory. We study these properties in the Erdös--Rényi random graph model G(n,p), proving a sharp threshold for a random graph to have the AS property asymptotically almost surely, and giving f…
Study on lightlike hypersurfaces in metallic semi-Riemannian manifolds.
problem Exploring geometric properties of lightlike hypersurfaces in metallic semi-Riemannian manifolds.
method Investigation of invariant and screen semi-invariant lightlike hypersurfaces, examination of integrability conditions.
result Induced structure on invariant lightlike hypersurfaces is metallic.
FlowMO uses Gaussian Processes for molecular property prediction with uncertainty.
problem Predicting molecular properties with uncertainty for small datasets.
method Gaussian Processes implemented in FlowMO, built on GPflow and RDKit.
result Comparable predictive performance to deep learning but superior uncertainty calibration.
On Newton Screeningstat.ML
Develops a new screening method called Newton screening for faster and more accurate sparse learning.
problem Sparse learning problems with large-scale optimization.
method Newton screening method with built-in working set and dual variable updates.
result Newton screening achieves one-step local convergence and sharp estimation error bound.
Deep neural network improves cancer detection accuracy in breast screening.
problem Improving cancer detection accuracy in breast screening.
method Two-stage training procedure using patch-level and macroscopic labels.
result Neural network achieves AUC of 0.895 in predicting cancer presence.
On the normal holonomy representation of spacelike submanifolds in pseudo-Riemannian space formsmath.DG
In this paper we study weakly irreducible holonomy representations of the normal connection of a spacelike submanifold in a pseudo-Riemannian space from. We associate screen representations to weakly irreducible normal holonomy groups and classify the screen representations having the Borel-Lichnérowicz property. In pa…
First-passage percolation affects graph properties like curvature and geodesics.
problem Effect of first-passage percolation on graph curvature and geodesics.
method Randomly perturbs the metric of a graph by assigning random edge lengths.
result Non-positive curvature and geodesic properties are not preserved by first-passage percolation.
The measure concentration property of an mm-space is roughly described as that any 1-Lipschitz map on to a metric space is almost close to a constant map. The target space is called the screen. The case of is widely studied in many literature (see \cite{gromov}, \cite{ledoux}, \cite{mil2}…
Decision stumps accurately screen variables in nonparametric models.
problem Challenges in theoretical properties of tree-based variable importance measures.
method Derive performance guarantees for variable selection using a single-level CART decision tree (decision stump).
result Decision stumps can perform consistent model selection despite being inaccurate for estimation.
Paper introduces a method to predict molecule properties from diverse data sources.
problem Limited ability to accommodate scarce or fragmented training data.
method Adaptive Invariance using invariant risk minimization to generalize beyond heterogeneous data.
result Predictor outperforms state-of-the-art transfer learning methods by significant margin.