Variable Selection
127 papers with code • 0 benchmarks • 0 datasets
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Asymptotic Behavior of Adversarial Training Estimator under $\ell_\infty$-Perturbation
Alternatively, a two-step procedure is proposed -- adaptive adversarial training, which could further improve the performance of adversarial training under $\ell_\infty$-perturbation.
Information-Theoretic State Variable Selection for Reinforcement Learning
Identifying the most suitable variables to represent the state is a fundamental challenge in Reinforcement Learning (RL).
False Discovery Rate Control for Gaussian Graphical Models via Neighborhood Screening
Unfortunately, well-established estimators, such as the graphical lasso or neighborhood selection, are known to be susceptible to a high prevalence of false edge detections.
Sparse PCA with False Discovery Rate Controlled Variable Selection
Sparse principal component analysis (PCA) aims at mapping large dimensional data to a linear subspace of lower dimension.
Semi-Supervised Deep Sobolev Regression: Estimation, Variable Selection and Beyond
We propose SDORE, a semi-supervised deep Sobolev regressor, for the nonparametric estimation of the underlying regression function and its gradient.
A least distance estimator for a multivariate regression model using deep neural networks
We propose a deep neural network (DNN) based least distance (LD) estimator (DNN-LD) for a multivariate regression problem, addressing the limitations of the conventional methods.
Variable Selection in High Dimensional Linear Regressions with Parameter Instability
We pose the issue of whether one should use weighted or unweighted observations at the variable selection stage in the presence of parameter instability, particularly when the number of potential covariates is large.
Sparse Learning and Class Probability Estimation with Weighted Support Vector Machines
The binary class probability is then estimated either by the $\ell^2$-norm regularized wSVMs framework with selected variables or by elastic net regularized wSVMs directly.
Random Forest Variable Importance-based Selection Algorithm in Class Imbalance Problem
Random Forest is a machine learning method that offers many advantages, including the ability to easily measure variable importance.
Economic Forecasts Using Many Noises
This paper addresses a key question in economic forecasting: does pure noise truly lack predictive power?
