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Found 4 papers, 3 papers with code
An operator learning perspective on parameter-to-observable maps
Computationally efficient surrogates for parametrized physical models play a crucial role in science and engineering.
Error Bounds for Learning with Vector-Valued Random Features
This paper provides a comprehensive error analysis of learning with vector-valued random features (RF).
Convergence Rates for Learning Linear Operators from Noisy Data
This paper studies the learning of linear operators between infinite-dimensional Hilbert spaces.
The Random Feature Model for Input-Output Maps between Banach Spaces
Well known to the machine learning community, the random feature model is a parametric approximation to kernel interpolation or regression methods.
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