A primer about Semi-Supervised Learning, the approaches taken with different algorithms and the limitations of using unlabelled data. The post Introduction to Semi-Supervised Learning appeared first on Towards Data Science .
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arXiv:2606. 00512v1 Announce Type: new Abstract: In many modern machine learning pipelines, abundant pretrained representations serve as noisy proxy covariates, while task-specific labels remain scarce.
In a world where human time is expensive, learn how to use it only when really necessary The post Reducing Human Annotation with ML Active Learning appeared first on Towards Data Science .
arXiv:2603. 01346v2 Announce Type: replace Abstract: We revisit the framework of Smart PAC learning, which seeks supervised learners which compete with semi-supervised learners that are provided full knowledge of the marginal distribution on unlabeled data.
The latest information about the Unconference is now available at the Unconference wiki, which will be periodically updated with more information for attendees.
arXiv:2608. 03706v1 Announce Type: new Abstract: Statistical learning is a fascinating field that has long been the mainstream of machine learning/artificial intelligence.