Gradient-Discrepancy Acquisition for Pool-Based Active Learning

Mohamadsadegh Khosravani; Sandra Zilles

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arXiv · 2605.02609

Gradient-Discrepancy Acquisition for Pool-Based Active Learning

Mohamadsadegh Khosravani·Sandra Zilles

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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.

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Mohamadsadegh Khosravani, Sandra Zilles. 2026-05-04. Gradient-Discrepancy Acquisition for Pool-Based Active Learning. https://arxiv.org/abs/2605.02609

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