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Research: Unlabeled data can replace membership queries under specific conditions

By PulseAugur Editorial · [1 sources] ·

A new research paper explores the conditions under which unlabeled data can effectively substitute for membership queries in machine learning. The study identifies that for homogeneous halfspaces, the answer depends on the angular distribution of data. If this distribution assigns positive mass to open sets of directions and zero mass to hyperplanes, a pool learner can achieve near-optimal label complexity with a specific bound. However, deviating from these angular assumptions can lead to significant separations between pool and membership query learning, even with abundant unlabeled data. AI

IMPACT Provides theoretical insights into data efficiency in machine learning models.

RANK_REASON Research paper published on arXiv discussing theoretical aspects of machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Research: Unlabeled data can replace membership queries under specific conditions

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Research paper published on arXiv discussing theoretical aspects of machine learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Dravyansh Sharma ·

    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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