Improved machine learning models for interpreting CDT results.
problem Improving accuracy and interpretability of CDT results.
method Analysis of pen stroke data using machine learning techniques.
result Machine learning models outperform existing scoring systems.
ABMs simulate financial behavior with AI and neuropsychology insights.
problem Lack of empiricism in ACE models.
method Agent-based models with cognitive biases and machine learning.
result ABMs offer new realism in financial behavior simulation.
ADReSS Challenge at INTERSPEECH 2020 benchmarks speech recognition for Alzheimer's dementia.
problem Automated recognition of Alzheimer's dementia from spontaneous speech.
method Provides a benchmark speech dataset, defines two tasks (classification and regression), and presents baseline models.
result Demonstrates the feasibility of automated speech recognition for Alzheimer's dementia.
New method extracts brain age from MRI sequences over time.
problem Lack of ground-truth labels in longitudinal neuroimaging data.
method Combines factor disentanglement with self-supervised learning.
result Extracts brain age information from MRI sequences.
New approach safely screens features and samples simultaneously for sparse modeling.
problem Learning sparse models to identify active features and samples.
method Alternating feature and sample screening steps, exploiting synergy between steps.
result Practical advantage in problems with large numbers of features and samples.
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.
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.
Safe screening reduces the number of triplets in metric learning.
problem Optimizing a metric over many triplets is computationally expensive and impractical.
method Safe triplet screening identifies and removes redundant triplets.
result Safe triplet screening maintains optimality without increasing computational cost.
New Bayesian optimization models for efficient material screening.
problem Efficiently screening materials with expensive and cheap tests.
method Flexible multi-test Bayesian optimization models with complex relationships.
result Demonstrated power on synthetic and real data.
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.
problem Efficiently solving high-dimensional lasso problems.
method Look-ahead screening rules to discard predictors.
result Look-ahead screening rules outperform existing methods.
A new screening rule 'dynamic Sasvi' improves sparse optimization speed.
problem Sparse optimization problem identification.
method Flexible framework based on Fenchel-Rockafellar duality for norm-regularized least squares.
result Dynamic Sasvi can eliminate more features and increase solver speed.
Study on lightlike submanifolds in metallic semi-Riemannian manifolds.
problem Characterizing and investigating properties of lightlike submanifolds in metallic semi-Riemannian manifolds.
method Introduced and analyzed subclasses of screen transversal lightlike submanifolds and investigated their geometric properties.
result Necessary and sufficient condition for an isotropic screen transversal lightlike submanifold to be totally geodesic.
The paper studies special null hypersurfaces in spacetimes.
problem Characterizing null screen isoparametric hypersurfaces in Lorentzian space forms.
method Developed screen isoparametric hypersurface concept for null hypersurfaces of Robertson-Walker spacetimes, derived Cartan identities, and provided local characterizations.
result Derived Cartan identities for the screen principal curvatures of null screen hypersurfaces in Lorentzian space forms and provided a local characterization.
New AI platform screens portfolios for desirable firms and news.
problem Optimizing portfolio selection with AI.
method Two LLM agents screen for firm fundamentals and news sentiment. Agents deliberate to generate buy/sell signals. High-dimensional estimation determines optimal weights.
result Screened portfolio's Sharpe ratio consistently estimates target, superior to baseline and conventional approaches.
New screening test for LASSO reduces complexity.
problem Efficient screening for LASSO problems.
method Joint screening test for LASSO problem, applied to sphere and dome regions.
result Effective screening of atoms reduces computational complexity.
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 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 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 virtual drug screening detects top treatments.
problem Understanding model performance in virtual drug screening tasks.
method Regression Enrichment Surfaces (RES) method.
result RES detects more top-performing treatments than existing methods.
A new screening rule improves lasso solving speed.
problem Efficiently solving lasso problems with high correlation.
method Uses second-order information from the Hessian to screen predictors.
result Outperforms alternatives on simulated and real data.
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…
Safe sample screening improves RSVM performance without sacrificing accuracy.
problem Improving RSVM performance under noisy conditions.
method Proposed two safe sample screening rules based on CCCP framework for RSVM.
result Significant reduction in computational time for RSVM.
Study the geometry of specific submanifolds in Golden Semi-Riemannian manifolds.
problem Investigate the geometry of specific submanifolds in Golden Semi-Riemannian manifolds.
method Investigate the geometry of distributions and induced connections, provide necessary and sufficient conditions for metric connections, and characterize submanifolds.
result Characterization of screen transversal anti-invariant lightlike submanifolds of Golden Semi-Riemannian manifolds.
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.
problem Efficiently solving non-convex Lasso problems with theoretical guarantees.
method Iterative majorization-minimization strategy with screening rule.
result Significant computational gain compared to classical methods.
Efficiently allocate budgets for LLM-assisted virtual screening to reduce costs.
problem Reducing the cost of evaluating alternatives in large-scale screening tasks.
method Propose a top- m m m greedy evaluation mechanism and the EFG- m m m algorithm for efficient budget allocation. result Prove that EFG- m m m is both sample-optimal and consistent in large-scale virtual screening. Deep learning predicts breast cancer with high accuracy from patient data.
problem Early detection of breast cancer from patient data.
method Feature selection and k-fold Monte Carlo cross-validation using deep learning.
result Deep learning model effectively distinguishes between cancer and healthy patients.
New rules reduce SLOPE model fitting time by screening out irrelevant variables.
problem Expensive tuning of regularization parameter in penalized regression models.
method Strong screening rules for group-based SLOPE models.
result Significant acceleration of fitting process for Group SLOPE and sparse-group SLOPE.
A new method reduces feature screening cost from O ( n p ) O(np) O ( n p ) to O ( n p ) O(\sqrt{n}p) O ( n p ) .
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 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.
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 half-lightlike submanifolds in Lorentzian manifolds with specific distributions.
problem Characterizing half-lightlike submanifolds in Lorentzian manifolds with conformal co-screen distributions.
method Using Cartan's formula, the authors classify half-lightlike submanifolds of Lorentzian space forms with constant screen principal curvatures.
result Screen homothetic half-lightlike submanifolds of a Lorentzian space form with a conformal co-screen distribution are locally lightlike triple product manifolds.
Deep CNN model improves breast cancer screening exam classification.
problem Improving accuracy in breast cancer screening exam classification.
method Localization-based deep CNN trained on 200,000 exams.
result AUC of 0.919 in predicting malignancy, reducing error rate by 23%.
This paper introduces LR-FFS for robust feature screening in federated learning under label shift.
problem Label shift challenges in federated learning for high-dimensional classification.
method Unified feature screening framework, label-shift robust federated feature screening (LR-FFS), federated estimation procedure.
result LR-FFS outperforms existing methods in diverse client environments with varying class distributions, sample sizes, and missing data.
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.
problem Identifying new structures in Sasakian space-forms.
method Defined and analyzed contact screen conformal and umbilic null hypersurfaces.
result Proved these hypersurfaces exist in Sasakian space forms with specific curvature.
The paper introduces a new screening method for faster L1 regularization.
problem Efficiently solving the ℓ _ 1 \ell\_{1} ℓ _ 1 -regularized least squares problem. method Combining safe screening tests and structured dictionary approximations.
result Significant reductions in computational complexity and execution times.
Model shows screening for infectious disease is hard but Thompson sampling works well.
problem Optimal screening policy for infectious diseases is hard to find.
method Stochastic-control model with Thompson sampling for optimal performance.
result Thompson sampling provides optimal performance guarantees in screening for infectious diseases.
A new method reduces feature size in CRFs for faster training.
problem Challenges in solving sparse CRFs for large-scale applications.
method Safe dynamic screening method exploiting dual optimum estimation.
result Significant speedup in training CRFs without loss of accuracy.
The paper explores null hypersurfaces in Lorentzian manifolds using geometric immersions.
problem Understanding the geometry of null hypersurfaces in Lorentzian manifolds.
method The approach involves isometric immersions of leafs of the screen distribution into semi-Euclidean spheres or hyperbolic spaces.
result Null hypersurfaces are shown to be umbilic and screen totally umbilic under certain geometric conditions.
Study Einstein warped-product manifolds with specific curvature conditions.
problem Understanding Einstein warped-product manifolds with screened Poisson equation constraints.
method Analyzing manifolds with specific curvature conditions and solving the screened Poisson equation.
result Dimension, Ricci curvature, and screened parameter are related through a quadratic equation.
New models compare mammograms to improve cancer diagnosis.
problem Improving cancer diagnosis accuracy by comparing recent and prior mammograms.
method Proposed neural network models trained on over 665,000 pairs of images.
result Best model achieves AUC of 0.866 in predicting malignancy.
SRF learns sparse rule models by screening out features efficiently.
problem Learning optimal sparse rule models is computationally intractable due to the large number of possible rules.
method SRF uses meta safe screening (mSS) to efficiently screen out multiple features, improving the learning of sparse rule models.
result SRF provides a general framework for fitting sparse rule models and can handle group regularization.
ROCS-derived features enhance virtual screening performance.
problem Improving virtual screening accuracy.
method Decomposed ROCS color force field into color components and atom overlaps, creating weighted features.
result Significant improvement in virtual screening performance (ROC AUC scores).
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.
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 null hypersurfaces in complex contact manifolds.
problem Characterizing null hypersurfaces in indefinite complex contact manifolds.
method Proved classification results for various null hypersurfaces and characterized the ambient space.
result The ambient complex contact manifold must have constant G H GH G H -sectional curvature of − 3 -3 − 3 for certain null hypersurfaces.