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Variable Selection

Papers

Showing 125 of 566 papers

TitleStatusHype
Neural interval-censored survival regression with feature selectionCode2
Flexible Bayesian Nonlinear Model ConfigurationCode1
Bayesian Variable Selection in a Million DimensionsCode1
Post-selection inference with HSIC-LassoCode1
Synthesize High-dimensional Longitudinal Electronic Health Records via Hierarchical Autoregressive Language ModelCode1
Invariant Causal Prediction for Block MDPsCode1
Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization AlgorithmsCode1
Fast Bayesian Variable Selection in Binomial and Negative Binomial RegressionCode1
Learning Sparse Classifiers: Continuous and Mixed Integer Optimization PerspectivesCode1
OmicSelector: automatic feature selection and deep learning modeling for omic experimentsCode1
Learning a Generic Value-Selection Heuristic Inside a Constraint Programming SolverCode1
Trading with the Momentum Transformer: An Intelligent and Interpretable ArchitectureCode1
SharpSAT-TD in Model Counting Competitions 2021-2023Code1
Monte Carlo Tree Search based Variable Selection for High Dimensional Bayesian OptimizationCode1
abess: A Fast Best Subset Selection Library in Python and RCode1
A novel interpretable machine learning system to generate clinical risk scores: An application for predicting early mortality or unplanned readmission in a retrospective cohort studyCode1
copent: Estimating Copula Entropy and Transfer Entropy in RCode1
Exact Combinatorial Optimization with Graph Convolutional Neural NetworksCode1
Active learning for binary classification with variable selection0
A convex formulation for high-dimensional sparse sliced inverse regression0
On feature selection in double-imbalanced data settings: a Random Forest approach0
A Comparison of Variable Selection Methods for Blockwise Diagonal Designs0
A Graphical Approach to State Variable Selection in Off-policy Learning0
A gradient-based variable selection for binary classification in reproducing kernel Hilbert space0
A Comparison of Resampling and Recursive Partitioning Methods in Random Forest for Estimating the Asymptotic Variance Using the Infinitesimal Jackknife0
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