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Overparameterization enables the algorithm to directly fit (perhaps even noisy) data, and challenges conventional wisdom which would advocate journal oil and gas underparameterization. We relate this method to kernel interpolation and show that both the interpolants and their generalization errors converge as p tends to infinity.

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