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.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative 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.
citing papers explorer
-
Black-Box Assisted Regression: Phase Transitions and Minimax Optimality
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.
-
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.