Nick C. Dexter
Orcid: 0000-0002-2418-4735
According to our database1,
Nick C. Dexter
authored at least 17 papers
between 2016 and 2023.
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Bibliography
2023
An Adaptive Sampling and Domain Learning Strategy for Multivariate Function Approximation on Unknown Domains.
SIAM J. Sci. Comput., February, 2023
A unified framework for learning with nonlinear model classes from arbitrary linear samples.
CoRR, 2023
Optimal approximation of infinite-dimensional holomorphic functions II: recovery from i.i.d. pointwise samples.
CoRR, 2023
CS4ML: A general framework for active learning with arbitrary data based on Christoffel functions.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
2022
Near-optimal learning of Banach-valued, high-dimensional functions via deep neural networks.
CoRR, 2022
CoRR, 2022
On efficient algorithms for computing near-best polynomial approximations to high-dimensional, Hilbert-valued functions from limited samples.
CoRR, 2022
CoRR, 2022
2021
The Gap between Theory and Practice in Function Approximation with Deep Neural Networks.
SIAM J. Math. Data Sci., 2021
Improved Recovery Guarantees and Sampling Strategies for TV Minimization in Compressive Imaging.
SIAM J. Imaging Sci., 2021
INGOT-DR: an interpretable classifier for predicting drug resistance in M. tuberculosis.
Algorithms Mol. Biol., 2021
Deep Neural Networks Are Effective At Learning High-Dimensional Hilbert-Valued Functions From Limited Data.
Proceedings of the Mathematical and Scientific Machine Learning, 2021
Learning High-Dimensional Hilbert-Valued Functions With Deep Neural Networks From Limited Data.
Proceedings of the AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences, Stanford, CA, USA, March 22nd - to, 2021
2020
An Interpretable Classification Method for Predicting Drug Resistance in M. Tuberculosis.
Proceedings of the 20th International Workshop on Algorithms in Bioinformatics, 2020
2018
Polynomial approximation via compressed sensing of high-dimensional functions on lower sets.
Math. Comput., 2018
2016
Explicit cost bounds of stochastic Galerkin approximations for parameterized PDEs with random coefficients.
Comput. Math. Appl., 2016