A Closed-Form Formula for Consistent Lipschitz Regression on Metric Spaces with Sparse Neural Network Realizations
A high-probability uniform ($L^{\infty}$) recovery guarantee that jointly controls approximation and statistical errors while enjoying an optimization error of zero is introduced.
Rui-Yang Hong, Hrad Ghoukasian, Anastasis Kratsios
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