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A Smoothed Discrepancy Principle for Random Feature Methods and Neural Networks

arXiv:2609.21017v1 Announce Type: new Abstract: We study data-driven early stopping for spectral regularisation methods in the classical non-parametric regression setting. Building on the discrepancy principle, we propose a multi-scale stopping rule that applies to general kernel estimators and sho…

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  1. 2026-09-21 04:00 · arXiv stat.ML
    A Smoothed Discrepancy Principle for Random Feature Methods and Neural Networks

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