BUGS embeds univariate marginal guidance into a regularized horseshoe prior to induce adaptive shrinkage, supplies theoretical contraction guarantees, and offers an active-set MCMC approximation that scales to p=1,000,000 while improving false-discovery control.
The B ayesian lasso
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Derives distribution-dependent generalization bounds for tuning L1/L2 regularization in multi-task linear regression that do not degrade with feature dimension d under sub-Gaussian i.i.d. assumptions and are sharper than prior uniform bounds for large d.
Simulations show Ridge, Lasso, and ElasticNet perform similarly for prediction at high sample-to-feature ratios, but Lasso feature selection recall drops to 0.18 under high multicollinearity and low SNR while ElasticNet holds at 0.93.
citing papers explorer
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Bayesian Global-Local Shrinkage with Univariate Guidance for Ultra-High-Dimensional Regression
BUGS embeds univariate marginal guidance into a regularized horseshoe prior to induce adaptive shrinkage, supplies theoretical contraction guarantees, and offers an active-set MCMC approximation that scales to p=1,000,000 while improving false-discovery control.
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Distribution-dependent Generalization Bounds for Tuning Linear Regression Across Tasks
Derives distribution-dependent generalization bounds for tuning L1/L2 regularization in multi-task linear regression that do not degrade with feature dimension d under sub-Gaussian i.i.d. assumptions and are sharper than prior uniform bounds for large d.
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Choosing the Right Regularizer for Applied ML: Simulation Benchmarks of Popular Scikit-learn Regularization Frameworks
Simulations show Ridge, Lasso, and ElasticNet perform similarly for prediction at high sample-to-feature ratios, but Lasso feature selection recall drops to 0.18 under high multicollinearity and low SNR while ElasticNet holds at 0.93.