Variable Selection
127 papers with code • 0 benchmarks • 0 datasets
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CAVIAR: Categorical-Variable Embeddings for Accurate and Robust Inference
Social science research often hinges on the relationship between categorical variables and outcomes.
CONCERT: Covariate-Elaborated Robust Local Information Transfer with Conditional Spike-and-Slab Prior
Distinguished from existing work, CONCERT is a one-step procedure, which achieves variable selection and information transfer simultaneously.
Statistical Mechanics of Dynamical System Identification
Recovering dynamical equations from observed noisy data is the central challenge of system identification.
A network-constrain Weibull AFT model for biomarkers discovery
We propose AFTNet, a novel network-constraint survival analysis method based on the Weibull accelerated failure time (AFT) model solved by a penalized likelihood approach for variable selection and estimation.
Penalized Generative Variable Selection
Deep networks are increasingly applied to a wide variety of data, including data with high-dimensional predictors.
Beyond Lines and Circles: Unveiling the Geometric Reasoning Gap in Large Language Models
Large Language Models (LLMs) demonstrate ever-increasing abilities in mathematical and algorithmic tasks, yet their geometric reasoning skills are underexplored.
kNN Algorithm for Conditional Mean and Variance Estimation with Automated Uncertainty Quantification and Variable Selection
In this paper, we introduce a kNN-based regression method that synergizes the scalability and adaptability of traditional non-parametric kNN models with a novel variable selection technique.
Data-driven model selection within the matrix completion method for causal panel data models
Matrix completion estimators are employed in causal panel data models to regulate the rank of the underlying factor model using nuclear norm minimization.
Variable selection for Naïve Bayes classification
However, features are usually correlated, a fact that violates the Na\"ive Bayes' assumption of conditional independence, and may deteriorate the method's performance.
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.
