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06/01/2023
CS4ML: A general framework for active learning with arbitrary data based on Christoffel functions
We introduce a general framework for active learning in regression probl...
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05/29/2023
Optimal approximation of infinite-dimensional holomorphic functions
Over the last decade, approximating functions in infinite dimensions fro...
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11/22/2022
Near-optimal learning of Banach-valued, high-dimensional functions via deep neural networks
The past decade has seen increasing interest in applying Deep Learning (...
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08/25/2022
CAS4DL: Christoffel Adaptive Sampling for function approximation via Deep Learning
The problem of approximating smooth, multivariate functions from sample ...
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03/25/2022
On efficient algorithms for computing near-best polynomial approximations to high-dimensional, Hilbert-valued functions from limited samples
Sparse polynomial approximation has become indispensable for approximati...
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02/04/2022
Towards optimal sampling for learning sparse approximation in high dimensions
In this chapter, we discuss recent work on learning sparse approximation...
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01/31/2022
An Adaptive sampling and domain learning strategy for multivariate function approximation on unknown domains
Many problems in computational science and engineering can be described ...
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12/11/2020
Deep Neural Networks Are Effective At Learning High-Dimensional Hilbert-Valued Functions From Limited Data
The accurate approximation of scalar-valued functions from sample points...
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09/17/2020
Improved recovery guarantees and sampling strategies for TV minimization in compressive imaging
In this paper, we consider the use of Total Variation (TV) minimization ...
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01/16/2020