Web: http://arxiv.org/abs/2103.03757

Sept. 15, 2022, 1:11 a.m. | Antoine de Mathelin, Francois Deheeger, Mathilde Mougeot, Nicolas Vayatis

cs.LG updates on arXiv.org arxiv.org

The goal of the paper is to design active learning strategies which lead to
domain adaptation under an assumption of Lipschitz functions. Building on
previous work by Mansour et al. (2009) we adapt the concept of discrepancy
distance between source and target distributions to restrict the maximization
over the hypothesis class to a localized class of functions which are
performing accurate labeling on the source domain. We derive generalization
error bounds for such active learning strategies in terms of Rademacher …

active learning arxiv domain adaptation

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