Explicit cost bounds of stochastic Galerkin approximations for parameterized PDEs with random coefficients. - Nick C. Dexter; Clayton G. Webster; Guannan Zhang (Comput. Math. Appl., 2016)

Polynomial approximation via compressed sensing of high-dimensional functions on lower sets. - Abdellah Chkifa; Nick C. Dexter; Hoang Tran; Clayton G. Webster (Math. Comput., 2018)

An Interpretable Classification Method for Predicting Drug Resistance in M. Tuberculosis. - Hooman Zabeti; Nick C. Dexter; Amir Hosein Safari; Nafiseh Sedaghat; Maxwell W. Libbrecht; Leonid Chindelevitch (WABI, 2020)

Learning High-Dimensional Hilbert-Valued Functions With Deep Neural Networks From Limited Data. - Ben Adcock; Simone Brugiapaglia; Nick C. Dexter; Sebastian Moraga (AAAI Spring Symposium: MLPS, 2021)

Deep Neural Networks Are Effective At Learning High-Dimensional Hilbert-Valued Functions From Limited Data. - Ben Adcock; Simone Brugiapaglia; Nick C. Dexter; Sebastian Moraga (MSML, 2021)

INGOT-DR: an interpretable classifier for predicting drug resistance in M. tuberculosis. - Hooman Zabeti; Nick C. Dexter; Amir Hosein Safari; Nafiseh Sedaghat; Maxwell W. Libbrecht; Leonid Chindelevitch (Algorithms Mol. Biol., 2021)

Improved Recovery Guarantees and Sampling Strategies for TV Minimization in Compressive Imaging. - Ben Adcock; Nick C. Dexter; Qinghong Xu (SIAM J. Imaging Sci., 2021)

The Gap between Theory and Practice in Function Approximation with Deep Neural Networks. - Ben Adcock; Nick C. Dexter (SIAM J. Math. Data Sci., 2021)

Towards optimal sampling for learning sparse approximation in high dimensions. - Ben Adcock; Juan M. Cardenas; Nick C. Dexter; Sebastian Moraga (CoRR, 2022)

On efficient algorithms for computing near-best polynomial approximations to high-dimensional, Hilbert-valued functions from limited samples. - Ben Adcock; Simone Brugiapaglia; Nick C. Dexter; Sebastian Moraga (CoRR, 2022)

CAS4DL: Christoffel Adaptive Sampling for function approximation via Deep Learning. - Ben Adcock; Juan M. Cardenas; Nick C. Dexter (CoRR, 2022)

CS4ML: A general framework for active learning with arbitrary data based on Christoffel functions. - Juan M. Cardenas; Ben Adcock; Nick C. Dexter (NeurIPS, 2023)

Optimal approximation of infinite-dimensional holomorphic functions. - Ben Adcock; Nick C. Dexter; Sebastian Moraga (CoRR, 2023)

An Adaptive Sampling and Domain Learning Strategy for Multivariate Function Approximation on Unknown Domains. - Ben Adcock; Juan M. Cardenas; Nick C. Dexter (SIAM J. Sci. Comput., 2023)

A Unified Framework for Learning with Nonlinear Model Classes from Arbitrary Linear Samples. - Ben Adcock; Juan M. Cardenas; Nick C. Dexter (ICML, 2024)

Optimal deep learning of holomorphic operators between Banach spaces. - Ben Adcock; Nick C. Dexter; Sebastian Moraga Scheuermann (NeurIPS, 2024)

Learning smooth functions in high dimensions: from sparse polynomials to deep neural networks. - Ben Adcock; Simone Brugiapaglia; Nick C. Dexter; Sebastian Moraga (CoRR, 2024)

Physics-informed deep learning and compressive collocation for high-dimensional diffusion-reaction equations: practical existence theory and numerics. - Simone Brugiapaglia; Nick C. Dexter; Samir Karam; Weiqi Wang (CoRR, 2024)

Optimal approximation of infinite-dimensional holomorphic functions II: Recovery from i.i.d. pointwise samples. - Ben Adcock; Nick C. Dexter; Sebastian Moraga (J. Complex., 2025)

Near-optimal learning of Banach-valued, high-dimensional functions via deep neural networks. - Ben Adcock; Simone Brugiapaglia; Nick C. Dexter; Sebastian Moraga (Neural Networks, 2025)