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Nick C. Dexter
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2020 – today
- 2025
- [j7]Ben Adcock, Simone Brugiapaglia
, Nick C. Dexter
Near-optimal learning of Banach-valued, high-dimensional functions via deep neural networks. Neural Networks 181: 106761 (2025) - 2024
- [c5]Ben Adcock, Juan M. Cardenas, Nick C. Dexter:
A Unified Framework for Learning with Nonlinear Model Classes from Arbitrary Linear Samples. ICML 2024 - [i15]Ben Adcock, Simone Brugiapaglia, Nick C. Dexter, Sebastian Moraga:
Learning smooth functions in high dimensions: from sparse polynomials to deep neural networks. CoRR abs/2404.03761 (2024) - [i14]Simone Brugiapaglia, Nick C. Dexter, Samir Karam, Weiqi Wang:
Physics-informed deep learning and compressive collocation for high-dimensional diffusion-reaction equations: practical existence theory and numerics. CoRR abs/2406.01539 (2024) - [i13]Ben Adcock, Nick C. Dexter, Sebastian Moraga:
Optimal deep learning of holomorphic operators between Banach spaces. CoRR abs/2406.13928 (2024) - 2023
- [j6]Ben Adcock, Juan M. Cardenas
An Adaptive Sampling and Domain Learning Strategy for Multivariate Function Approximation on Unknown Domains. SIAM J. Sci. Comput. 45(1): 200- (2023) - [c4]Juan M. Cardenas, Ben Adcock, Nick C. Dexter:
CS4ML: A general framework for active learning with arbitrary data based on Christoffel functions. NeurIPS 2023 - [i12]Ben Adcock, Nick C. Dexter, Sebastian Moraga:
Optimal approximation of infinite-dimensional holomorphic functions. CoRR abs/2305.18642 (2023) - [i11]Ben Adcock, Juan M. Cardenas, Nick C. Dexter:
CS4ML: A general framework for active learning with arbitrary data based on Christoffel functions. CoRR abs/2306.00945 (2023) - [i10]Ben Adcock, Nick C. Dexter, Sebastian Moraga:
Optimal approximation of infinite-dimensional holomorphic functions II: recovery from i.i.d. pointwise samples. CoRR abs/2310.16940 (2023) - [i9]Ben Adcock, Juan M. Cardenas, Nick C. Dexter:
A unified framework for learning with nonlinear model classes from arbitrary linear samples. CoRR abs/2311.14886 (2023) - 2022
- [i8]Ben Adcock, Juan M. Cardenas, Nick C. Dexter:
An Adaptive sampling and domain learning strategy for multivariate function approximation on unknown domains. CoRR abs/2202.00144 (2022) - [i7]Ben Adcock, Juan M. Cardenas, Nick C. Dexter, Sebastian Moraga:
Towards optimal sampling for learning sparse approximation in high dimensions. CoRR abs/2202.02360 (2022) - [i6]Ben Adcock, Simone Brugiapaglia, Nick C. Dexter, Sebastian Moraga:
On efficient algorithms for computing near-best polynomial approximations to high-dimensional, Hilbert-valued functions from limited samples. CoRR abs/2203.13908 (2022) - [i5]Ben Adcock, Juan M. Cardenas, Nick C. Dexter:
CAS4DL: Christoffel Adaptive Sampling for function approximation via Deep Learning. CoRR abs/2208.12190 (2022) - [i4]Ben Adcock, Simone Brugiapaglia, Nick C. Dexter, Sebastian Moraga:
Near-optimal learning of Banach-valued, high-dimensional functions via deep neural networks. CoRR abs/2211.12633 (2022) - 2021
- [j5]Hooman Zabeti, Nick C. Dexter
INGOT-DR: an interpretable classifier for predicting drug resistance in M. tuberculosis. Algorithms Mol. Biol. 16(1): 17 (2021) - [j4]Ben Adcock, Nick C. Dexter
Improved Recovery Guarantees and Sampling Strategies for TV Minimization in Compressive Imaging. SIAM J. Imaging Sci. 14(3): 1149-1183 (2021) - [j3]Ben Adcock, Nick C. Dexter
The Gap between Theory and Practice in Function Approximation with Deep Neural Networks. SIAM J. Math. Data Sci. 3(2): 624-655 (2021) - [c3]Ben Adcock, Simone Brugiapaglia, Nick C. Dexter, Sebastian Moraga:
Learning High-Dimensional Hilbert-Valued Functions With Deep Neural Networks From Limited Data. AAAI Spring Symposium: MLPS 2021 - [c2]Ben Adcock, Simone Brugiapaglia, Nick C. Dexter
Deep Neural Networks Are Effective At Learning High-Dimensional Hilbert-Valued Functions From Limited Data. MSML 2021: 1-36 - 2020
- [c1]Hooman Zabeti, Nick C. Dexter, Amir Hosein Safari, Nafiseh Sedaghat, Maxwell W. Libbrecht, Leonid Chindelevitch
An Interpretable Classification Method for Predicting Drug Resistance in M. Tuberculosis. WABI 2020: 2:1-2:18 - [i3]Ben Adcock, Nick C. Dexter:
The gap between theory and practice in function approximation with deep neural networks. CoRR abs/2001.07523 (2020) - [i2]Ben Adcock, Nick C. Dexter, Qinghong Xu:
Improved recovery guarantees and sampling strategies for TV minimization in compressive imaging. CoRR abs/2009.08555 (2020) - [i1]Ben Adcock, Simone Brugiapaglia, Nick C. Dexter, Sebastian Moraga:
Deep Neural Networks Are Effective At Learning High-Dimensional Hilbert-Valued Functions From Limited Data. CoRR abs/2012.06081 (2020)
2010 – 2019
- 2018
- [j2]Abdellah Chkifa
Polynomial approximation via compressed sensing of high-dimensional functions on lower sets. Math. Comput. 87(311): 1415-1450 (2018) - 2016
- [j1]Nick C. Dexter
Explicit cost bounds of stochastic Galerkin approximations for parameterized PDEs with random coefficients. Comput. Math. Appl. 71(11): 2231-2256 (2016)
Coauthor Index
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