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In label-noise learning, \textit{noise transition matrix}, denoting the probabilities that clean labels flip into noisy labels, plays a central role in building \textit{statistically consistent classifiers}.
Learning from noisy examples
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A rate of convergence for mixture proportion estimation, with application to learning from noisy labels
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Learning from massive noisy labeled data for image classification
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Learning with bounded instance-and label-dependent label noise
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Co-teaching: Robust training of deep neural networks with extremely noisy labels
Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, and Masashi Sugiyama · 2018
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MentorNet: Learning data-driven curriculum for very deep neural networks on corrupted labels
Lu Jiang, Zhengyuan Zhou, Thomas Leung, Li-Jia Li, and Li Fei-Fei · 2018
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Jan Kremer, Fei Sha, and Christian Igel · 2018
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Dimensionality-driven learning with noisy labels
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Curtis G Northcutt, Tailin Wu, and Isaac L Chuang · 2017
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Foundations of Machine Learning
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Learning to reweight examples for robust deep learning
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Joint optimization framework for learning with noisy labels
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Robustness of conditional gans to noisy labels
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An efficient and provable approach for mixture proportion estimation using linear independence assumption
Xiyu Yu, Tongliang Liu, Mingming Gong, Kayhan Batmanghelich, and Dacheng Tao · 2018
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Xiyu Yu, Tongliang Liu, Mingming Gong, and Dacheng Tao · 2018
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Generalized cross entropy loss for training deep neural networks with noisy labels
Zhilu Zhang and Mert Sabuncu · 2018
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An operator theoretic approach to nonparametric mixture models
Robert A Vandermeulen and Clayton D Scott · 2019
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How does disagreement benefit co-teaching?
Xingrui Yu, Bo Han, Jiangchao Yao, Gang Niu, Ivor W Tsang, and Masashi Sugiyama · 2019
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