{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/variable-selection/papers/6","list_of":"/task/variable-selection","task":"Variable Selection","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":6,"pages_in_order":6,"rows_per_page":100,"rows":[501,566],"of":566,"counts":{"archive_papers_tagged":566,"with_a_code_link":145,"where_syntology_ran_a_sample":9,"not_listed_spam_title":0,"listed":566,"listed_where_code_ran":9,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":7,"every_run_a_failure_of_syntologys_instrument":2,"listed_with_a_run_with_no_instrument_failure":7,"listed_every_run_a_failure_of_syntologys_instrument":2,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/variable-selection","prev":"/task/variable-selection/papers/5","next":null,"papers":[{"url":null,"slug":"regularization-vs-relaxation-a-conic","title":"Regularization vs. Relaxation: A conic optimization perspective of statistical variable selection","date":"2015-10-20","arxiv_id":"1510.06083","repositories_listed":0,"syntology":null},{"url":null,"slug":"selection-de-variables-par-le-glm-lasso-pour","title":"Sélection de variables par le GLM-Lasso pour la prédiction du risque palustre","date":"2015-09-09","arxiv_id":"1509.02873","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-evolution-of-carrying-capacity-in","title":"The evolution of carrying capacity in constrained and expanding tumour cell populations","date":"2015-08-14","arxiv_id":"1402.0757","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-discrete-dantzig-selector-estimating","title":"The Discrete Dantzig Selector: Estimating Sparse Linear Models via Mixed Integer Linear Optimization","date":"2015-08-08","arxiv_id":"1508.01922","repositories_listed":0,"syntology":null},{"url":null,"slug":"elastic-net-procedure-for-partially-linear","title":"Elastic Net Procedure for Partially Linear Models","date":"2015-07-22","arxiv_id":"1507.06032","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-mrmr-variable-selection-method-a","title":"The mRMR variable selection method: a comparative study for functional data","date":"2015-07-13","arxiv_id":"1507.03496","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-dimensional-ordinary-least-squares","title":"High-dimensional Ordinary Least-squares Projection for Screening Variables","date":"2015-06-05","arxiv_id":"1506.01782","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-computational-complexity-of-high","title":"On the Computational Complexity of High-Dimensional Bayesian Variable Selection","date":"2015-05-29","arxiv_id":"1505.07925","repositories_listed":0,"syntology":null},{"url":null,"slug":"provable-sparse-tensor-decomposition","title":"Provable Sparse Tensor Decomposition","date":"2015-02-05","arxiv_id":"1502.01425","repositories_listed":0,"syntology":null},{"url":null,"slug":"accurate-estimation-of-quantitative-trait","title":"Accurate Estimation of Quantitative Trait Locus Effects with Epistatic by Improved Variational Linear Regression","date":"2015-01-12","arxiv_id":"1503.05628","repositories_listed":0,"syntology":null},{"url":null,"slug":"equitability-of-dependence-measure","title":"Equitability of Dependence Measure","date":"2015-01-09","arxiv_id":"1501.02102","repositories_listed":0,"syntology":null},{"url":null,"slug":"breaking-the-curse-of-dimensionality-with","title":"Breaking the Curse of Dimensionality with Convex Neural Networks","date":"2014-12-30","arxiv_id":"1412.8690","repositories_listed":0,"syntology":null},{"url":null,"slug":"support-recovery-without-incoherence-a-case","title":"Support recovery without incoherence: A case for nonconvex regularization","date":"2014-12-17","arxiv_id":"1412.5632","repositories_listed":0,"syntology":null},{"url":null,"slug":"pluto-penalized-unbiased-logistic-regression","title":"PLUTO: Penalized Unbiased Logistic Regression Trees","date":"2014-11-25","arxiv_id":"1411.6948","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimal-variable-selection-in-multi-group","title":"Optimal variable selection in multi-group sparse discriminant analysis","date":"2014-11-23","arxiv_id":"1411.6311","repositories_listed":0,"syntology":null},{"url":null,"slug":"group-regularized-estimation-under-structural","title":"Group Regularized Estimation under Structural Hierarchy","date":"2014-11-17","arxiv_id":"1411.4691","repositories_listed":0,"syntology":null},{"url":null,"slug":"faithful-variable-screening-for-high","title":"Faithful Variable Screening for High-Dimensional Convex Regression","date":"2014-11-07","arxiv_id":"1411.1805","repositories_listed":0,"syntology":null},{"url":null,"slug":"controlling-false-discoveries-in-high","title":"Controlling false discoveries in high-dimensional situations: Boosting with stability selection","date":"2014-11-05","arxiv_id":"1411.1285","repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-selection-through-minimization-of-the","title":"Feature Selection through Minimization of the VC dimension","date":"2014-10-27","arxiv_id":"1410.7372","repositories_listed":0,"syntology":null},{"url":null,"slug":"median-selection-subset-aggregation-for","title":"Median Selection Subset Aggregation for Parallel Inference","date":"2014-10-24","arxiv_id":"1410.6604","repositories_listed":0,"syntology":null},{"url":null,"slug":"penalized-versus-constrained-generalized","title":"Penalized versus constrained generalized eigenvalue problems","date":"2014-10-22","arxiv_id":"1410.6131","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-additive-model-using-symmetric","title":"Sparse Additive Model using Symmetric Nonnegative Definite Smoothers","date":"2014-09-08","arxiv_id":"1409.2552","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-regularized-regression-for-variable","title":"Efficient Regularized Regression for Variable Selection with L0 Penalty","date":"2014-07-28","arxiv_id":"1407.7508","repositories_listed":0,"syntology":null},{"url":null,"slug":"extensions-of-stability-selection-using","title":"Extensions of stability selection using subsamples of observations and covariates","date":"2014-07-18","arxiv_id":"1407.4916","repositories_listed":0,"syntology":null},{"url":null,"slug":"sequential-advantage-selection-for-optimal","title":"Sequential Advantage Selection for Optimal Treatment Regimes","date":"2014-05-20","arxiv_id":"1405.5239","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-permutation-approach-for-selecting-the","title":"A Permutation Approach for Selecting the Penalty Parameter in Penalized Model Selection","date":"2014-04-08","arxiv_id":"1404.2007","repositories_listed":0,"syntology":null},{"url":null,"slug":"dont-fall-for-tuning-parameters-tuning-free","title":"Don't Fall for Tuning Parameters: Tuning-Free Variable Selection in High Dimensions With the TREX","date":"2014-04-02","arxiv_id":"1404.0541","repositories_listed":0,"syntology":null},{"url":null,"slug":"selective-factor-extraction-in-high","title":"Selective Factor Extraction in High Dimensions","date":"2014-03-25","arxiv_id":"1403.6212","repositories_listed":0,"syntology":null},{"url":null,"slug":"simultaneous-sparse-estimation-of-canonical","title":"Simultaneous sparse estimation of canonical vectors in the p>>N setting","date":"2014-03-24","arxiv_id":"1403.6095","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-quantile-huber-regression-for","title":"Sparse Quantile Huber Regression for Efficient and Robust Estimation","date":"2014-02-19","arxiv_id":"1402.4624","repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-and-variable-selection-in","title":"Feature and Variable Selection in Classification","date":"2014-02-10","arxiv_id":"1402.2300","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-bayesian-unsupervised-learning","title":"Sparse Bayesian Unsupervised Learning","date":"2014-01-30","arxiv_id":"1401.8017","repositories_listed":0,"syntology":null},{"url":null,"slug":"monte-carlo-simulation-for-lasso-type","title":"Monte Carlo Simulation for Lasso-Type Problems by Estimator Augmentation","date":"2014-01-17","arxiv_id":"1401.4425","repositories_listed":0,"syntology":null},{"url":null,"slug":"inference-in-high-dimensions-with-the","title":"Inference in High Dimensions with the Penalized Score Test","date":"2014-01-12","arxiv_id":"1401.2678","repositories_listed":0,"syntology":null},{"url":null,"slug":"oracle-inequalities-for-convex-loss-functions","title":"Oracle Inequalities for Convex Loss Functions with Non-Linear Targets","date":"2013-12-12","arxiv_id":"1312.3525","repositories_listed":0,"syntology":null},{"url":null,"slug":"bartmachine-machine-learning-with-bayesian","title":"bartMachine: Machine Learning with Bayesian Additive Regression Trees","date":"2013-12-08","arxiv_id":"1312.2171","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-mining-using-unguided-symbolic","title":"Data Mining using Unguided Symbolic Regression on a Blast Furnace Dataset","date":"2013-09-23","arxiv_id":"1309.5931","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-dimensional-sparse-gaussian-graphical","title":"High dimensional Sparse Gaussian Graphical Mixture Model","date":"2013-08-15","arxiv_id":"1308.3381","repositories_listed":0,"syntology":null},{"url":null,"slug":"confidence-sets-based-on-thresholding","title":"Confidence Sets Based on Thresholding Estimators in High-Dimensional Gaussian Regression Models","date":"2013-08-14","arxiv_id":"1308.3201","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimal-feature-selection-in-high-dimensional","title":"Optimal Feature Selection in High-Dimensional Discriminant Analysis","date":"2013-06-27","arxiv_id":"1306.6557","repositories_listed":0,"syntology":null},{"url":null,"slug":"randomized-maximum-contrast-selection","title":"Randomized maximum-contrast selection: subagging for large-scale regression","date":"2013-06-14","arxiv_id":"1306.3494","repositories_listed":0,"syntology":null},{"url":null,"slug":"mean-field-variational-bayesian-inference-for","title":"Mean field variational Bayesian inference for support vector machine classification","date":"2013-05-13","arxiv_id":"1305.2667","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-rank-minrelation-majrelation-coefficient","title":"A Rank Minrelation - Majrelation Coefficient","date":"2013-05-09","arxiv_id":"1305.2038","repositories_listed":0,"syntology":null},{"url":null,"slug":"predictive-correlation-screening-application","title":"Predictive Correlation Screening: Application to Two-stage Predictor Design in High Dimension","date":"2013-03-10","arxiv_id":"1303.2378","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-compressed-regression","title":"Bayesian Compressed Regression","date":"2013-03-04","arxiv_id":"1303.0642","repositories_listed":0,"syntology":null},{"url":null,"slug":"gradient-based-kernel-method-for-feature","title":"Gradient-based kernel method for feature extraction and variable selection","date":"2012-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"apple-approximate-path-for-penalized","title":"APPLE: Approximate Path for Penalized Likelihood Estimators","date":"2012-11-02","arxiv_id":"1211.0889","repositories_listed":0,"syntology":null},{"url":null,"slug":"mixture-model-for-designs-in-high-dimensional","title":"Mixture model for designs in high dimensional regression and the LASSO","date":"2012-10-17","arxiv_id":"1210.4762","repositories_listed":0,"syntology":null},{"url":null,"slug":"group-descent-algorithms-for-nonconvex","title":"Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors","date":"2012-09-10","arxiv_id":"1209.2160","repositories_listed":0,"syntology":null},{"url":null,"slug":"consistent-selection-of-tuning-parameters-via","title":"Consistent selection of tuning parameters via variable selection stability","date":"2012-08-16","arxiv_id":"1208.3380","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-dimensional-screening-using-multiple","title":"High-Dimensional Screening Using Multiple Grouping of Variables","date":"2012-08-09","arxiv_id":"1208.2043","repositories_listed":0,"syntology":null},{"url":null,"slug":"structured-estimation-in-nonparameteric-cox","title":"Structured Estimation in Nonparameteric Cox Model","date":"2012-07-18","arxiv_id":"1207.4510","repositories_listed":0,"syntology":null},{"url":null,"slug":"multivariate-bernoulli-distribution","title":"Multivariate Bernoulli distribution","date":"2012-06-08","arxiv_id":"1206.1874","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimality-of-graphlet-screening-in-high","title":"Optimality of Graphlet Screening in High Dimensional Variable Selection","date":"2012-04-29","arxiv_id":"1204.6452","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multi-objective-exploratory-procedure-for","title":"A Multi-objective Exploratory Procedure for Regression Model Selection","date":"2012-03-28","arxiv_id":"1203.6276","repositories_listed":0,"syntology":null},{"url":null,"slug":"eigennet-a-bayesian-hybrid-of-generative-and","title":"EigenNet: A Bayesian hybrid of generative and conditional models for sparse learning","date":"2011-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-review-and-comparison-of-strategies-for","title":"A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition","date":"2011-08-16","arxiv_id":"1108.3259","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-primal-dual-algorithm-for-group-sparse","title":"A Primal-Dual Algorithm for Group Sparse Regularization with Overlapping Groups","date":"2010-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"block-variable-selection-in-multivariate","title":"Block Variable Selection in Multivariate Regression and High-dimensional Causal Inference","date":"2010-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multivariate-dyadic-regression-trees-for","title":"Multivariate Dyadic Regression Trees for Sparse Learning Problems","date":"2010-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"grouped-orthogonal-matching-pursuit-for","title":"Grouped Orthogonal Matching Pursuit for Variable Selection and Prediction","date":"2009-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"nonparametric-greedy-algorithms-for-the","title":"Nonparametric Greedy Algorithms for the Sparse Learning Problem","date":"2009-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"thresholding-procedures-for-high-dimensional","title":"Thresholding Procedures for High Dimensional Variable Selection and Statistical Estimation","date":"2009-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-large-feature-spaces-with","title":"Exploring Large Feature Spaces with Hierarchical Multiple Kernel Learning","date":"2008-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-large-feature-spaces-with-1","title":"Exploring Large Feature Spaces with Hierarchical Multiple Kernel Learning","date":"2008-09-09","arxiv_id":"0809.1493","repositories_listed":0,"syntology":null},{"url":null,"slug":"catching-change-points-with-lasso","title":"Catching Change-points with Lasso","date":"2007-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"9e0572b9b70bbde5728aa2d5d1b9d947afa2a52a75dcbf2abd3161279de88feb","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}