Variable Selection

Benchmarks archive 2025-07-28

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Datasets archive 2025-07-28

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Most implemented papers archive 2025-07-28

30 shown of 145 papers with code (566 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Exact Combinatorial Optimization with Graph Convolutional Neural Networks
  4 Jun 2019 6 repositories listed Syntology ran 0 of 2 samples · 2 unverified · 1 pointer-only (licence)
  Combinatorial optimization problems are typically tackled by the branch-and-bound paradigm.
• Deep Knockoffs
  16 Nov 2018 4 repositories listed
  This paper introduces a machine for sampling approximate model-X knockoffs for arbitrary and unspecified data distributions using deep generative models.
• Neural interval-censored survival regression with feature selection
  14 Jun 2022 3 repositories listed
  Survival analysis is a fundamental area of focus in biomedical research, particularly in the context of personalized medicine.
• Trading with the Momentum Transformer: An Intelligent and Interpretable Architecture
  16 Dec 2021 3 repositories listed
  We introduce the Momentum Transformer, an attention-based deep-learning architecture, which outperforms benchmark time-series momentum and mean-reversion trading strategies.
• BART: Bayesian additive regression trees
  19 Jun 2008 3 repositories listed Syntology ran 1 of 1 samples · 0 unverified
  We develop a Bayesian "sum-of-trees" model where each tree is constrained by a regularization prior to be a weak learner, and fitting and inference are accomplished via an iterative Bayesian backfitting MCMC algorithm…
• Stability Selection via Variable Decorrelation
  27 May 2025 2 repositories listed
  In addition, by noting that the instability of the Lasso is not limited to high-dimensional settings, we demonstrate the effectiveness of the proposed approach for low-dimensional data.
• On the Selection Stability of Stability Selection and Its Applications
  14 Nov 2024 2 repositories listed
  Stability selection is a widely adopted resampling-based framework for high-dimensional variable selection.
• Bayesian Variable Selection in a Million Dimensions
  2 Aug 2022 2 repositories listed
  Bayesian variable selection is a powerful tool for data analysis, as it offers a principled method for variable selection that accounts for prior information and uncertainty.
• OmicSelector: automatic feature selection and deep learning modeling for omic experiments
  2 Jun 2022 2 repositories listed
  A crucial phase of modern biomarker discovery studies is selecting the most promising features from high-throughput screening assays.
• Scalable Spike-and-Slab
  4 Apr 2022 2 repositories listed Syntology ran 7 of 19 samples · 12 unverified · 19 pointer-only (licence)
  Spike-and-slab priors are commonly used for Bayesian variable selection, due to their interpretability and favorable statistical properties.
• Flexible variable selection in the presence of missing data
  25 Feb 2022 2 repositories listed
  Through simulations, we show that our proposal has good operating characteristics and results in panels with higher classification and variable selection performance compared to several existing penalized regression…
• abess: A Fast Best Subset Selection Library in Python and R
  19 Oct 2021 2 repositories listed
  In addition, a user-friendly R library is available at the Comprehensive R Archive Network.
• Post-selection inference with HSIC-Lasso
  29 Oct 2020 2 repositories listed
  Detecting influential features in non-linear and/or high-dimensional data is a challenging and increasingly important task in machine learning.
• Iteratively Reweighted ℓ₁-Penalized Robust Regression
  9 Jul 2019 2 repositories listed
  This paper investigates tradeoffs among optimization errors, statistical rates of convergence and the effect of heavy-tailed errors for high-dimensional robust regression with nonconvex regularization.
• Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution
  21 Dec 2017 2 repositories listed
  Variable selection for Gaussian process models is often done using automatic relevance determination, which uses the inverse length-scale parameter of each input variable as a proxy for variable relevance.
• Bolasso: model consistent Lasso estimation through the bootstrap
  8 Apr 2008 2 repositories listed
  For various decays of the regularization parameter, we compute asymptotic equivalents of the probability of correct model selection (i.
• Handling bounded response in high dimensions: a Horseshoe prior Bayesian Beta regression approach
  28 May 2025 1 repository listed
  In this work, we propose a novel Bayesian approach for high-dimensional sparse Beta regression framework that employs a tempered posterior.
• High Dimensional Bayesian Optimization using Lasso Variable Selection
  2 Apr 2025 1 repository listed
  Bayesian optimization (BO) is a leading method for optimizing expensive black-box optimization and has been successfully applied across various scenarios.
• Bayesian Cox model with graph-structured variable selection priors for multi-omics biomarker identification
  17 Mar 2025 1 repository listed
  Our simulation results show that the proposed Bayesian Cox model with graph-based prior knowledge results in more trustable and stable variable selection and non-inferior survival prediction, compared to methods…
• Causal Feature Learning in the Social Sciences
  17 Mar 2025 1 repository listed
  Variable selection poses a significant challenge in causal modeling, particularly within the social sciences, where constructs often rely on inter-related factors such as age, socioeconomic status, gender, and race.
• AirCast: Improving Air Pollution Forecasting Through Multi-Variable Data Alignment
  25 Feb 2025 1 repository listed
  In this paper, we introduce AirCast, a novel multi-variable air pollution forecasting model, by combining weather and air quality variables.
• Proximal Iteration for Nonlinear Adaptive Lasso
  7 Dec 2024 1 repository listed
  Augmenting a smooth cost function with an ℓ₁ penalty allows analysts to efficiently conduct estimation and variable selection simultaneously in sophisticated models and can be efficiently implemented using proximal…
• Ab Initio Nonparametric Variable Selection for Scalable Symbolic Regression with Large p
  17 Oct 2024 1 repository listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  Symbolic regression (SR) is a powerful technique for discovering symbolic expressions that characterize nonlinear relationships in data, gaining increasing attention for its interpretability, compactness, and robustness.
• Comorbid anxiety predicts lower odds of depression improvement during smartphone-delivered psychotherapy
  16 Sep 2024 1 repository listed
  Comorbid anxiety disorders are common among patients with major depressive disorder (MDD), and numerous studies have identified an association between comorbid anxiety and resistance to pharmacological depression…
• VICatMix: variational Bayesian clustering and variable selection for discrete biomedical data
  23 Jun 2024 1 repository listed
  Effective clustering of biomedical data is crucial in precision medicine, enabling accurate stratifiction of patients or samples.
• Sparse-Group Boosting with Balanced Selection Frequencies: A Simulation-Based Approach and R Implementation
  31 May 2024 1 repository listed
  This paper introduces a novel framework for reducing variable selection bias by balancing selection frequencies of base-learners in boosting and introduces the sgboost package in R, which implements this framework…
• DeepLINK-T: deep learning inference for time series data using knockoffs and LSTM
  5 Apr 2024 1 repository listed
  DeepLINK-T combines deep learning with knockoff inference to control FDR in feature selection for time series models, accommodating a wide variety of feature distributions.
• Predictive Analytics of Varieties of Potatoes
  4 Apr 2024 1 repository listed
  We explore the application of machine learning algorithms specifically to enhance the selection process of Russet potato clones in breeding trials by predicting their suitability for advancement.
• High-dimensional forecasting with known knowns and known unknowns
  26 Jan 2024 1 repository listed
  Forecasts play a central role in decision making under uncertainty.
• Information-Theoretic State Variable Selection for Reinforcement Learning
  21 Jan 2024 1 repository listed
  Identifying the most suitable variables to represent the state is a fundamental challenge in Reinforcement Learning (RL).

Syntology lines on 4 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.