On the Complexity of Learning a Class Ratio from Unlabeled Data

DOI10.1613/jair.1.12013zbMath1497.68419arXiv2004.03515OpenAlexW3114166581MaRDI QIDQ5145827

Benjamin Fish, Lev Reyzin

Publication date: 22 January 2021

Published in: Journal of Artificial Intelligence Research (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2004.03515

zbMATH Keywords

machine learning

Mathematics Subject Classification ID

Analysis of algorithms and problem complexity (68Q25) Learning and adaptive systems in artificial intelligence (68T05)

Cites Work

• Semi-supervised learning of class balance under class-prior change by distribution matching
• Adjusting the Outputs of a Classifier to New a Priori Probabilities: A Simple Procedure
• A theory of the learnable
• Understanding Machine Learning
• Noise-tolerant learning, the parity problem, and the statistical query model
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