Optimal sampling strategies for multivariate function approximation on general domains. - Ben Adcock; Juan M. Cardenas (CoRR, 2019)

Near-Optimal Sampling Strategies for Multivariate Function Approximation on General Domains. - Ben Adcock; Juan M. Cardenas (SIAM J. Math. Data Sci., 2020)

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

A high order unfitted hybridizable discontinuous Galerkin method for linear elasticity. - Juan M. Cardenas; Manuel E. Solano (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)

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)