On efficient algorithms for computing near-best polynomial
approximations to high-dimensional, Hilbert-valued functions from limited
samples

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On efficient algorithms for computing near-best polynomial approximations to high-dimensional, Hilbert-valued functions from limited samples

25 March 2022

Simone Brugiapaglia

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Papers citing "On efficient algorithms for computing near-best polynomial approximations to high-dimensional, Hilbert-valued functions from limited samples"

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Title
Operator Learning of Lipschitz Operators: An Information-Theoretic Perspective Samuel Lanthaler 140 6 0 26 Jun 2024
Learning smooth functions in high dimensions: from sparse polynomials to deep neural networks Ben Adcock Simone Brugiapaglia N. Dexter S. Moraga 86 6 0 04 Apr 2024
Operator Learning: Algorithms and Analysis Nikola B. Kovachki S. Lanthaler Andrew M. Stuart 192 50 0 24 Feb 2024
A unified framework for learning with nonlinear model classes from arbitrary linear samples Ben Adcock Juan M. Cardenas N. Dexter 119 3 0 25 Nov 2023
Restarts subject to approximate sharpness: A parameter-free and optimal scheme for first-order methods Ben Adcock Matthew J. Colbrook Maksym Neyra-Nesterenko 92 2 0 05 Jan 2023