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This is a repository associated with the paper "An adaptive sampling and domain learning strategy for multivariate function approximation on unknown domains" by Ben Adcock, Juan M. Cardenas, and Nick Dexter available at https://arxiv.org/abs/2202.00144
This is a repository associated with the chapter book "Towards optimal sampling for learning sparse approximations in high dimensions" by Ben Adcock, Juan M. Cardenas, Nick Dexter and Sebastian Moraga to be published by Springer in late 2021, available at https://arxiv.org/abs/2202.02360
Scripts and notebooks to reproduce the experiments and analyses of the paper Adrian Englhardt, Holger Trittenbach, Daniel Kottke, Bernhard Sick, Klemens Böhm, "Efficient SVDD sampling with approximation guarantees for the decision boundary", Machine Learning (2022).