English(EN) When Can Unlabeled Data Pools Replace Membership Queries?

研究：特定条件下无标签数据可替代成员查询

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-02 05:00

一篇新的研究论文探讨了在何种条件下，无标签数据可以有效地替代机器学习中的成员查询。研究发现，对于同质半空间，答案取决于数据的角度分布。如果这种分布将正质量分配给开放集合的方向，并将超平面分配给零质量，那么池学习器可以在特定边界内实现近乎最优的标签复杂度。然而，即使有大量的无标签数据，偏离这些角度假设也会导致池学习和成员查询学习之间出现显著的差异。 AI

影响为机器学习模型的数据效率提供了理论见解。

排序理由论文发表在arXiv上，讨论机器学习的理论方面。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv cs.LG TIER_1 English(EN) · Dravyansh Sharma · 2026-11-02 05:00

When Can Unlabeled Data Pools Replace Membership Queries?

arXiv:2610.41001v1 Announce Type: new Abstract: Membership queries let a learner label arbitrary points and give near-optimal $\tilde O(n\log(1/\eps))$ label complexity for halfspaces under every marginal. A pool learner may label only sampled points. We ask when unlabeled data c…

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When Can Unlabeled Data Pools Replace Membership Queries?

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