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Variable Selection

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Showing 301–325 of 566 papers

TitleStatusHype
Proximal Gradient Method with Extrapolation and Line Search for a Class of Nonconvex and Nonsmooth Problems—0
Pruning variable selection ensembles—0
Rademacher upper bounds for cross-validation errors with an application to the lasso—0
Random Forest Variable Importance-based Selection Algorithm in Class Imbalance Problem—0
Randomized maximum-contrast selection: subagging for large-scale regression—0
Real-time Traffic Accident Risk Prediction based on Frequent Pattern Tree—0
Recovery of Linear Components: Reduced Complexity Autoencoder Designs—0
Regularization Approach for Network Modeling of German Power Derivative Market—0
Regularization vs. Relaxation: A conic optimization perspective of statistical variable selection—0
Regularized Modal Regression with Applications in Cognitive Impairment Prediction—0
Regularized Multivariate Analysis Framework for Interpretable High-Dimensional Variable Selection—0
Reinforcement Learning for Variable Selection in a Branch and Bound Algorithm—0
Replicated Vector Approximate Message Passing For Resampling Problem—0
Resource-Constrained Heuristic for Max-SAT—0
Ridge partial correlation screening for ultrahigh-dimensional data—0
Risk factor identification for incident heart failure using neural network distillation and variable selection—0
Robust and Parallel Bayesian Model Selection—0
Robust Grouped Variable Selection Using Distributionally Robust Optimization—0
Robust Knockoffs for Controlling False Discoveries With an Application to Bond Recovery Rates—0
Robust Variable Selection for High-dimensional Regression with Missing Data and Measurement Errors—0
Sample Complexity of Tree Search Configuration: Cutting Planes and Beyond—0
Efficient kernel-based variable selection with sparsistency—0
A Self-Penalizing Objective Function for Scalable Interaction Detection—0
Sélection de variables par le GLM-Lasso pour la prédiction du risque palustre—0
Selection of training populations (and other subset selection problems) with an accelerated genetic algorithm (STPGA: An R-package for selection of training populations with a genetic algorithm)—0
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