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Thus, the NTK kernel and the exponential kernelk(x, y) =e−|x−y| are equivalent in a bounded smooth boundary domain, also in its subdomain

noticing that 1 lies in the Sobolev space, for the NTK kernel in our setting, we also have that the RKHS of NTK in a bounded domain with smooth boundary is a Sobolev class · 2018

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Adversarial Robustness of NTK Neural Networks

stat.ML · 2026-04-28 · unverdicted · novelty 7.0

NTK neural networks achieve minimax optimal adversarial regression rates in Sobolev spaces using gradient flow with early stopping, but minimum norm interpolants are vulnerable in the overfitting regime.

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  • Adversarial Robustness of NTK Neural Networks stat.ML · 2026-04-28 · unverdicted · none · ref 21

    NTK neural networks achieve minimax optimal adversarial regression rates in Sobolev spaces using gradient flow with early stopping, but minimum norm interpolants are vulnerable in the overfitting regime.