Provides a finite-sample minimax characterization of black-box assisted regression with a phase transition at δ_c(n) ~ n^{-β/(2β+d)} and a safe residual estimator achieving near-optimal risk.
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3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3verdicts
UNVERDICTED 3representative citing papers
An intrinsic spherical kernel ridge regression framework is introduced for non-linear responses on spheres, reducing infinite-dimensional estimation to finite via the representer theorem with convergence rates shown.
Conditional KRR reduces to KRR on a residual kernel with an added O(1/sqrt(N)) term in expected test risk and outperforms standard KRR when the F-component is dominant.
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Infinite-Dimensional Spherical Kernel ridge Regression
An intrinsic spherical kernel ridge regression framework is introduced for non-linear responses on spheres, reducing infinite-dimensional estimation to finite via the representer theorem with convergence rates shown.