3 papers
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.ST2026
Towards Sharp Minimax Risk Bounds for Operator Learning
Ben Adcock, Gregor Maier, Rahul Parhi
We develop a minimax theory for operator learning, where the goal is to estimate an unknown operator between separable Hilbert spaces from finitely many noisy input-output samples.…
cs.LG2025
The Sample Complexity of Learning Lipschitz Operators with respect to Gaussian Measures
Ben Adcock, Michael Griebel, Gregor Maier
Operator learning, the approximation of mappings between infinite-dimensional function spaces using machine learning, has gained increasing research attention in recent years. Appr…