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.
The elements of statistical learning: data mining, inference, and prediction, volume 2
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The paper introduces an anchor-based heteroscedastic noise model for PBO that maps user uncertainty via KDE on reliable examples, incorporates it into GP surrogates, and derives risk-averse acquisition functions including a risk-adjusted EUBO variant that preserves one-step Bayes-optimality up to an
plmmr is an R package implementing penalized linear mixed models with file-backing for GWAS data exhibiting complex correlation structures.
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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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Anchor-Based Heteroscedastic Noise for Preferential Bayesian Optimization
The paper introduces an anchor-based heteroscedastic noise model for PBO that maps user uncertainty via KDE on reliable examples, incorporates it into GP surrogates, and derives risk-averse acquisition functions including a risk-adjusted EUBO variant that preserves one-step Bayes-optimality up to an
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plmmr: an R package to fit penalized linear mixed models for genome-wide association data with complex correlation structure
plmmr is an R package implementing penalized linear mixed models with file-backing for GWAS data exhibiting complex correlation structures.