Sebastian Moraga

According to our database, Sebastian Moraga authored at least 9 papers between 2021 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

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

2024

Optimal deep learning of holomorphic operators between Banach spaces.

Ben Adcock, Nick C. Dexter, Sebastian Moraga

CoRR, 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

2023

Optimal approximation of infinite-dimensional holomorphic functions II: recovery from i.i.d. pointwise samples.

Ben Adcock, Nick C. Dexter, Sebastian Moraga

CoRR, 2023

Optimal approximation of infinite-dimensional holomorphic functions.

Ben Adcock, Nick C. Dexter, Sebastian Moraga

CoRR, 2023

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

Towards optimal sampling for learning sparse approximation in high dimensions.

Ben Adcock, Juan M. Cardenas, Nick C. Dexter, Sebastian Moraga

CoRR, 2022

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

Proceedings of the Mathematical and Scientific Machine Learning, 2021

Learning High-Dimensional Hilbert-Valued Functions With Deep Neural Networks From Limited Data.

Ben Adcock, Simone Brugiapaglia, Nick C. Dexter, Sebastian Moraga

Proceedings of the AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences, Stanford, CA, USA, March 22nd - to, 2021