New Gaussian spectral algorithms achieve optimal rates in misspecified learning

Researchers have developed fixed-bandwidth Gaussian kernel spectral algorithms that achieve minimax optimal convergence rates in nonparametric regression, even when the true regression function is misspecified. These algorithms demonstrate robustness to model misspecification due to the infinite smoothness of Gaussian kernels, allowing any spectral algorithm to reach optimal rates if the regularization parameter decays exponentially. The work also extends these algorithms to robust and adaptive transfer learning under concept shift, deriving optimal convergence rates up to logarithmic factors and analyzing the impact of concept shift magnitude and sample size on generalization error. AI

IMPACT Provides theoretical advancements in machine learning algorithms, potentially improving robustness and transfer learning capabilities.

RANK_REASON Academic paper detailing new algorithms and theoretical results. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• Gaussian spectral algorithms
• Haotian Lin
• Sobolev space

• paper
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