2 citations · 2 across the 4 of their papers we have counts for
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math.NA2026
Near-Optimal Learning of Gaussian Sobolev Operators
Ben Adcock, Michael Griebel, Gregor Maier
A key question in operator learning is how to design surrogate operators with provable approximation guarantees in reasonable computational time. Whereas smooth operators can be ap…
math.NA2025
Function recovery and optimal sampling in the presence of nonuniform evaluation costs
Ben Adcock
We consider recovering a function in an -dimensional linear subspace from i.i.d. pointwise samples via (weighted) least-squares esti…
math.NA2024★ 2 cited
Learning smooth functions in high dimensions: from sparse polynomials to deep neural networks
Ben Adcock, Simone Brugiapaglia, Nick Dexter +1
Learning approximations to smooth target functions of many variables from finite sets of pointwise samples is an important task in scientific computing and its many applications in…