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Weakly-supervised learning is a paradigm for alleviating the scarcity of labeled data by leveraging lower-quality but larger-scale supervision signals.
Learning from noisy examples
Dana Angluin and Philip Laird · 1988
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Asymptotic statistics , volume 3
Aad W Van der Vaart · 2000
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Information theory, inference and learning algorithms
David JC MacKay · 2003
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Learning about individuals from group statistics
Hendrik Kück and Nando de Freitas · 2005
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Theory of point estimation
Erich L Lehmann and George Casella · 2006
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Semi-supervised learning
Chapelle Olivier, S Bernhard, and Zien Alexander · 2006
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Learning classifiers from only positive and unlabeled data
Charles Elkan and Keith Noto · 2008
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A brief introduction to weakly supervised learning
Zhi-Hua Zhou · 2008
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky et al · 2009
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A survey on transfer learning
Sinno Jialin Pan and Qiang Yang · 2009
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Estimating labels from label proportions
Novi Quadrianto, Alex J Smola, Tiberio S Caetano, and Quoc V Le · 2009
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Presence-only data and the em algorithm
Gill Ward, Trevor Hastie, Simon Barry, Jane Elith, and John R Leathwick · 2009
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A positive and unlabeled learning algorithm for one-class classification of remote-sensing data
Wenkai Li, Qinghua Guo, and Charles Elkan · 2010
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Composite binary losses
Mark D Reid and Robert C Williamson · 2010
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Active learning
Burr Settles · 2012
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Learning with noisy labels
Nagarajan Natarajan, Inderjit S Dhillon, Pradeep K Ravikumar, and Ambuj Tewari · 2013
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Classification with asymmetric label noise: Consistency and maximal denoising
Clayton Scott, Gilles Blanchard, and Gregory Handy · 2013
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Ilya Sutskever, James Martens, George Dahl, and Geoffrey Hinton · 2013
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Machine learning: Trends, perspectives, and prospects
Michael I Jordan and Tom M Mitchell · 2015
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Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Learning from corrupted binary labels via class-probability estimation
Aditya Menon, Brendan van Rooyen, Cheng Soon Ong, and Bob Williamson · 2015
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Positive-unlabeled learning for the prediction of conformational b-cell epitopes
Jing Ren, Qian Liu, John Ellis, and Jinyan Li · 2015
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Deep learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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∝ \propto svm for learning with label proportions
Felix Yu, Dong Liu, Sanjiv Kumar, Jebara Tony, and Shih-Fu Chang · 2013
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Decontamination of mutually contaminated models
Gilles Blanchard and Clayton Scott · 2014
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Analysis of learning from positive and unlabeled data
Marthinus C du Plessis, Gang Niu, and Masashi Sugiyama · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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(almost) no label no cry
Giorgio Patrini, Richard Nock, Paul Rivera, and Tiberio Caetano · 2014
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Sainbayar Sukhbaatar, Joan Bruna, Manohar Paluri, Lubomir Bourdev, and Rob Fergus · 2014
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Learning from complementary labels
Takashi Ishida, Gang Niu, Weihua Hu, and Masashi Sugiyama · 2017
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Making deep neural networks robust to label noise: A loss correction approach
Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, and Lizhen Qu · 2017
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A theory of learning with corrupted labels
Brendan van Rooyen and Robert C Williamson · 2017
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Learning with biased complementary labels
Xiyu Yu, Tongliang Liu, Mingming Gong, and Dacheng Tao · 2018
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Complementary-label learning for arbitrary losses and models
Takashi Ishida, Gang Niu, Aditya Menon, and Masashi Sugiyama · 2019
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On the minimal supervision for training any binary classifier from only unlabeled data
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