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)

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)

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

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)

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)