Research map / Label-efficient and robust learning / Semi-supervised learning
A Closer Look to Positive-Unlabeled Learning from Fine-grained Perspectives: An Empirical Study
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An empirical study that revisits positive-unlabeled (PU) learning methods from fine-grained perspectives to examine their basic characteristics.
Problem
Positive-unlabeled learning trains a binary classifier from a few labeled positive instances and many unlabeled instances; the community has proposed dozens of methods, but their basic characteristics are not well understood.
Approach
A comprehensive empirical study that analyzes existing PU learning methods along fine-grained perspectives rather than proposing a new method; the abstract is truncated so specific findings are not stated.
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- Learning from positive and unlabeled examples -Finite size sample bounds (NeurIPS 2025 · similarity 0.92)
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- Positive-Unlabeled Learning by Latent Group-Aware Meta Disambiguation (CVPR 2024 · similarity 0.88)
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