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Risk Equivalence between RKHS Regression and Sequence Models for Lipschitz Spectral Algorithms
Kernel spectral algorithms are often summarized by convergence rates, which hide how their risk depends jointly on regularization, noise, the population spectrum, target coefficients, and the chosen filter. Gaussian sequence models arise as a simplified but characteristic setting for studying the in
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