mlsanityData to 7 Oct 2026

Making Better Use of Unlabelled Data in Bayesian Active Learning

arXiv 2404.17249 ↗First posted 26 Apr 2024cs.LG stat.MLcited by 10

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Abstract (arXiv)

Fully supervised models are predominant in Bayesian active learning. We argue that their neglect of the information present in unlabelled data harms not just predictive performance but also decisions about what data to acquire. Our proposed solution is a simple framework for semi-supervised Bayesian active learning. We find it produces better-performing models than either conventional Bayesian active learning or semi-supervised learning with randomly acquired data. It is also easier to scale up than the conventional approach. As well as supporting a shift towards semi-supervised models, our findings highlight the importance of studying models and acquisition methods in conjunction.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 84 of its 160 references that are in the map. First posted on arXiv 26 Apr 2024.

Builds on

84 of 160 references.

References · 84

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Citing papers per year: 2024: 2; 2025: 3; 2026: 5.

10 papers.