Abstract:The effectiveness of active learning hinges on the choice of the acquisition criterion by which a learning algorithm selects potentially informative data points whose label is subsequently queried. This paper proposes a novel gradient-based acquisition criterion, derived from a generalization bound introduced by Luo et al. (2022). This criterion can be applied in lieu of uncertainty measures in uncertainty sampling, or incorporated into diversity-based methods that consider the spread of sampled points in addition to the uncertainty of their labels. We provide a theoretical justification of the proposed acquisition criterion, and demonstrate its effectiveness in an empirical evaluation.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.02609 [cs.LG] |
| (or arXiv:2605.02609v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.02609 arXiv-issued DOI via DataCite (pending registration) |
From: Mohamadsadegh Khosravani [view email]
[v1]
Mon, 4 May 2026 13:56:09 UTC (4,567 KB)
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