Gradient-Discrepancy Acquisition for Pool-Based Active Learning
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866909047581573120 |
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| author | Khosravani, Mohamadsadegh Zilles, Sandra |
| author_facet | Khosravani, Mohamadsadegh Zilles, Sandra |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_02609 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Gradient-Discrepancy Acquisition for Pool-Based Active Learning Khosravani, Mohamadsadegh Zilles, Sandra Machine Learning 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. |
| title | Gradient-Discrepancy Acquisition for Pool-Based Active Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2605.02609 |