Tag: positive
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Micro Text Classification Based on Balanced Positive-Unlabeled Learning
Micro Text Classification Based on Balanced Positive-Unlabeled Learning arXiv:2503.13562v1 Announce Type: new Abstract: In real-world text classification tasks, negative texts often contain a minimal proportion of negative content, which is especially problematic in areas like text quality control, legal risk screening, and sensitive information interception. This challenge manifests at two levels: at the macro level,…
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Generation from Noisy Examples
Generation from Noisy Examples arXiv:2501.04179v1 Announce Type: new Abstract: We continue to study the learning-theoretic foundations of generation by extending the results from Kleinberg and Mullainathan [2024] and Li et al. [2024] to account for noisy example streams. In the noiseless setting of Kleinberg and Mullainathan [2024] and Li et al. [2024], an adversary picks…