Making better use of unlabelled data in Bayesian Active learning
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...
| Main Authors: | , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
Journal of Machine Learning Research
2024
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