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The problem of learning from label proportions (LLP) involves training classifiers with weak labels on bags of instances, rather than strong labels on individual instances.
Learning from aggregate views
Bee-Chung Chen, Lei Chen, Raghu Ramakrishnan, and David R Musicant · 2006
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Supervised learning by training on aggregate outputs
David R Musicant, Janara M Christensen, and Jamie F Olson · 2007
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Semi-supervised learning (chapelle, o. et al., eds.; 2006)[book reviews]
Olivier Chapelle, Bernhard Scholkopf, and Alexander Zien · 2009
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Kernel k-means based framework for aggregate outputs classification
Shuo Chen, Bin Liu, Mingjie Qian, and Changshui Zhang · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 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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Svm classifier estimation from group probabilities
Stefan Rueping · 2010
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Ng · 2011
Earlier work this paper cites.
Learning from label proportions by optimizing cluster model selection
Marco Stolpe and Katharina Morik · 2011
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Learning about individuals from group statistics
Hendrik Kuck and Nando de Freitas · 2012
Earlier work this paper cites.
Learning bayesian network classifiers from label proportions
Jerónimo Hernández-González, Iñaki Inza, and Jose A Lozano · 2013
Earlier work this paper cites.
Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee · 2013
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Min Lin, Qiang Chen, and Shuicheng Yan · 2013
Earlier work this paper cites.
∝ \propto svm for learning with label proportions
Felix X Yu, Dong Liu, Sanjiv Kumar, Tony Jebara, and Shih-Fu Chang · 2013
Cited alongside, same era.
Learning a generative classifier from label proportions
Kai Fan, Hongyi Zhang, Songbai Yan, Liwei Wang, Wensheng Zhang, and Jufu Feng · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Cited alongside, same era.
Semi-supervised learning with deep generative models
Durk P Kingma, Shakir Mohamed, Danilo Jimenez Rezende, and Max Welling · 2014
Cited alongside, same era.
Video event detection by inferring temporal instance labels
Kuan-Ting Lai, Felix X Yu, Ming-Syan Chen, and Shih-Fu Chang · 2014
Cited alongside, same era.
Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
Later among the works it cites.
Co-training for demographic classification using deep learning from label proportions
Ehsan Mohammady Ardehaly and Aron Culotta · 2017
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Learning with label proportions based on nonparallel support vector machines
Zhensong Chen, Zhiquan Qi, Bo Wang, Limeng Cui, Fan Meng, and Yong Shi · 2017
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A probabilistic approach for learning with label proportions applied to the us presidential election
Tao Sun, Dan Sheldon, and Brendan O’Connor · 2017
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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Antti Tarvainen and Harri Valpola · 2017
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Deep learning from label proportions for emphysema quantification
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Giorgio Patrini, Richard Nock, Paul Rivera, and Tiberio Caetano · 2014
Cited alongside, same era.
On learning from label proportions
Felix X Yu, Krzysztof Choromanski, Sanjiv Kumar, Tony Jebara, and Shih-Fu Chang · 2014
Cited alongside, same era.
Alter-cnn: An approach to learning from label proportions with application to ice-water classification
Fan Li and Graham Taylor · 2015
Cited alongside, same era.
Linear twin svm for learning from label proportions
Bo Wang, Zhensong Chen, and Zhiquan Qi · 2015
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Temporal ensembling for semi-supervised learning
Samuli Laine and Timo Aila · 2016
Cited alongside, same era.
Learning with label proportions via npsvm
Zhiquan Qi, Bo Wang, Fan Meng, and Lingfeng Niu · 2016
Cited alongside, same era.
Gerda Bortsova, Florian Dubost, Silas Ørting, Ioannis Katramados, Laurens Hogeweg, Laura Thomsen, Mathilde Wille, and Marleen de Bruijne · 2018
Later among the works it cites.
Fitting the data from embryo implantation prediction: Learning from label proportions
Jerónimo Hernández-González, Inaki Inza, Lorena Crisol-Ortíz, María A Guembe, María J Iñarra, and Jose A Lozano · 2018
Later among the works it cites.
Virtual adversarial training: a regularization method for supervised and semi-supervised learning
Takeru Miyato, Shin-ichi Maeda, Shin Ishii, and Masanori Koyama · 2018
Later among the works it cites.
Realistic evaluation of deep semi-supervised learning algorithms
Avital Oliver, Augustus Odena, Colin A Raffel, Ekin Dogus Cubuk, and Ian Goodfellow · 2018
Later among the works it cites.
Mixmatch: A holistic approach to semi-supervised learning
David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin Raffel · 2019
Closest in time.
Deep multi-class learning from label proportions
Gabriel Dulac-Arnold, Neil Zeghidour, Marco Cuturi, Lucas Beyer, and Jean-Philippe Vert · 2019
Closest in time.
Interpolation consistency training for semi-supervised learning
Vikas Verma, Alex Lamb, Juho Kannala, Yoshua Bengio, and David Lopez-Paz · 2019
Closest in time.