Improved Active Learning via Dependent Leverage Score Sampling

arXiv:2310.04966v2 Announce Type: replace
Abstract: We show how to obtain improved active learning methods in the agnostic (adversarial noise) setting by combining marginal leverage score sampling with non-independent sampling strategies that promote spatial coverage. In particular, we propose an easily implemented method based on the \emph{pivotal sampling algorithm}, which we test on problems motivated by learning-based methods for parametric PDEs and uncertainty quantification. In comparison to independent sampling, our method reduces the number of samples needed to reach a given …

abstract active learning adversarial algorithm arxiv coverage cs.lg independent noise pivotal promote sampling show spatial strategies test type via