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Variable Selection

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Showing 201–250 of 566 papers

TitleStatusHype
Modeling High-Dimensional Data with Unknown Cut Points: A Fusion Penalized Logistic Threshold RegressionCode0
AutoScore-Ordinal: An interpretable machine learning framework for generating scoring models for ordinal outcomesCode0
Inference of Multiscale Gaussian Graphical Model—0
Yordle: An Efficient Imitation Learning for Branch and Bound—0
VC-PCR: A Prediction Method based on Supervised Variable Selection and Clustering—0
Extending the Use of MDL for High-Dimensional Problems: Variable Selection, Robust Fitting, and Additive Modeling—0
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
Bayesian Approaches to Shrinkage and Sparse EstimationCode0
Regularized Multivariate Analysis Framework for Interpretable High-Dimensional Variable Selection—0
Variational Bayes for high-dimensional proportional hazards models with applications within gene expressionCode0
Supervised Multivariate Learning with Simultaneous Feature Auto-grouping and Dimension Reduction—0
Trading with the Momentum Transformer: An Intelligent and Interpretable ArchitectureCode1
Variable Selection and Regularization via Arbitrary Rectangle-range Generalized Elastic NetCode0
Efficient and robust high-dimensional sparse logistic regression via nonlinear primal-dual hybrid gradient algorithms—0
A Fast Non-parametric Approach for Local Causal Structure Learning—0
Fast and Scalable Spike and Slab Variable Selection in High-Dimensional Gaussian ProcessesCode0
Branch and Bound in Mixed Integer Linear Programming Problems: A Survey of Techniques and Trends—0
Semiparametric discrete data regression with Monte Carlo inference and predictionCode0
abess: A Fast Best Subset Selection Library in Python and RCode1
Noise-Augmented Privacy-Preserving Empirical Risk Minimization with Dual-purpose Regularizer and Privacy Budget Retrieval and Recycling—0
The Terminating-Random Experiments Selector: Fast High-Dimensional Variable Selection with False Discovery Rate Control—0
Controlled-Variable Selection based on Chaos Theory for the Tennessee Eastman Plant—0
Provable Identifiability of ReLU Neural Networks via Lasso Regularization—0
A Comparison of Variable Selection Methods for Blockwise Diagonal Designs—0
Non-stationary Gaussian process discriminant analysis with variable selection for high-dimensional functional dataCode0
A gradient-based variable selection for binary classification in reproducing kernel Hilbert space—0
Deep neural networks with controlled variable selection for the identification of putative causal genetic variantsCode0
Adaptive Bayesian Sum of Trees Model for Covariate Dependent Spectral Analysis—0
Computationally Efficient High-Dimensional Bayesian Optimization via Variable SelectionCode0
Differentially Private Variable Selection via the Knockoff Filter—0
Variable Selection for Causal Inference via Outcome-Adaptive Random Forest—0
What drives bitcoin? An approach from continuous local transfer entropy and deep learning classification models—0
Double Machine Learning and Automated Confounder Selection -- A Cautionary Tale—0
Exclusive Group Lasso for Structured Variable SelectionCode0
Dimensionality Reduction and State Space Systems: Forecasting the US Treasury Yields Using Frequentist and Bayesian VARs—0
Bayesian forecast combination using time-varying features—0
Data-Driven Constitutive Relation Reveals Scaling Law for Hydrodynamic Transport Coefficients—0
Estimating high-dimensional Markov-switching VARs—0
LocalGLMnet: interpretable deep learning for tabular data—0
Inference for High Dimensional Censored Quantile RegressionCode0
Order Book Queue Hawkes-Markovian Modeling—0
Decoupling Shrinkage and Selection for the Bayesian Quantile Regression—0
Correlation scenarios and correlation stress testing—0
Robust Variable Selection and Estimation Via Adaptive Elastic Net S-Estimators for Linear RegressionCode0
Fast Bayesian Variable Selection in Binomial and Negative Binomial RegressionCode1
The SKIM-FA Kernel: High-Dimensional Variable Selection and Nonlinear Interaction Discovery in Linear TimeCode0
Sparsistent Model DiscoveryCode0
Semiparametric count data regression for self-reported mental health—0
AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival dataCode0
Bayesian Boosting for Linear Mixed Models—0
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