EigenBayes combines spectral estimation of latent factors with adaptive empirical Bayes hyperparameter calibration to shrink superfluous components in overfitted factor models, delivering tractable posteriors and favorable asymptotics.
arXiv preprint arXiv:2008.00254 , year=
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An augmented kernel ridge regression estimator separates linear and nonlinear components to achieve sharp oracle inequalities and minimax optimal prediction risk under general kernels.
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Overfitted high-dimensional matrix factorizations via adaptive spectral shrinkage
EigenBayes combines spectral estimation of latent factors with adaptive empirical Bayes hyperparameter calibration to shrink superfluous components in overfitted factor models, delivering tractable posteriors and favorable asymptotics.
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Adaptive Kernel Ridge Regression with Linear Structure: Sharp Oracle Inequalities and Minimax Optimality
An augmented kernel ridge regression estimator separates linear and nonlinear components to achieve sharp oracle inequalities and minimax optimal prediction risk under general kernels.