An augmented kernel ridge regression estimator separates linear and nonlinear components to achieve sharp oracle inequalities and minimax optimal prediction risk under general kernels.
Journal of Machine Learning Research , volume=
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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A unified framework derives non-asymptotic bounds on conditional miscoverage in conformal prediction via pointwise and L_p routes and gives a common view of existing methods.
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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.
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A Unified Theory of Conditional Coverage in Conformal Prediction with Applications
A unified framework derives non-asymptotic bounds on conditional miscoverage in conformal prediction via pointwise and L_p routes and gives a common view of existing methods.