Making Better Use of Unlabelled Data in Bayesian Active Learning
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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.
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Citing papers per year: 2024: 2; 2025: 3; 2026: 5.
10 papers.
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- 2026A Federated Generalized Expectation-Maximization Algorithm for Mixture Models with an Unknown Number of Componentscited by 1
- 2026Loss-Driven Bayesian Active Learningcited by 1
- 2026Consistency Regularised Gradient Flows for Inverse Problemscited by 1
- 2025Prediction-Oriented Subsampling from Data Streamscited by 0
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- 2026FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Datacited by 0
- 2026FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data22 Jun 2026
- 2026Consistency Regularised Gradient Flows for Inverse Problems8 May 2026
- 2026Loss-Driven Bayesian Active Learning13 Apr 2026
- 2026A Federated Generalized Expectation-Maximization Algorithm for Mixture Models with an Unknown Number of Components29 Jan 2026
- 2026Active learning for photonic crystals22 Jan 2026
- 2025Active Learning with Task-Driven Representations for Messy Pools29 Oct 2025
- 2025Prediction-Oriented Subsampling from Data Streams5 Aug 2025
- 2025Optimizing Likelihoods via Mutual Information: Bridging Simulation-Based Inference and Bayesian Optimal Experimental Design11 Feb 2025
- 2024Rethinking Aleatoric and Epistemic Uncertainty30 Dec 2024
- 2024Post-hoc Probabilistic Vision-Language Models8 Dec 2024