Official code for "Federated Learning under Heterogeneous and Correlated Client Availability" (INFOCOM'23)

With AutoBrewML Framework the time it takes to get production-ready ML models with great ease and efficiency highly accelerates.

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

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).

isso -- Iterative Samling Schedule Optimization

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

Sampling strategies for the Elementary Effects method.

It provides evenly separated points on the surface of a sphere. Then create two polyhedra that are highly regular and export them in .obj

This is a repository associated with the paper "Near-optimal sampling strategies for multivariate function on general domains" by Ben Adcock and Juan M. Cardenas available at https://epubs.siam.org/doi/10.1137/19M1279459 and https://arxiv.org/abs/1908.01249

A Julia package for one-class classification sampling methods.