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We propose a learning algorithm capable of learning from label proportions instead of direct data labels.
On-line learning and stochastic approximations
Léon Bottou · 1998
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Learning about individuals from group statistics
Nando de Freitas and Hendrick Kück · 2005
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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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Estimating labels from label proportions
Novi Quadrianto, Alex J. Smola, Tiberio S. Caetano, and Quoc V. Le · 2008
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Kernel k-means based framework for aggregate outputs classification
S. Chen, B. Liu, M. Qian, and C. Zhang · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Estimating labels from label proportions
Novi Quadrianto, Alex J. Smola, Tibério S. Caetano, and Quoc V. Le · 2009
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SVM classifier estimation from group probabilities
Stefan Rueping · 2010
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Ranking via sinkhorn propagation
Ryan Prescott Adams and Richard S Zemel · 2011
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Learning from label proportions by optimizing cluster model selection
Marco Stolpe and Katharina Morik · 2011
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Sinkhorn distances: Lightspeed computation of optimal transport
Marco Cuturi · 2013
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Learning bayesian network classifiers from label proportions
Jerónimo Hernández-González, Iñaki Inza, and Jose A. Lozano · 2013
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∝ \propto SVM for learning with label proportions
Felix X. Yu, Dong Liu, Sanjiv Kumar, Tony Jebara, and Shih-Fu Chang · 2013
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Object-based visual sentiment concept analysis and application
Tao Chen, Felix X. Yu, Jiawei Chen, Yin Cui, Yan-Ying Chen, and Shih-Fu Chang · 2014
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Learning a generative classifier from label proportions
Kai Fan, Hongyi Zhang, Songbai Yan, Liwei Wang, Wensheng Zhang, and Jufu Feng · 2014
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Video event detection by inferring temporal instance labels
Kuan-Ting Lai, Felix X. Yu, Ming-Syan Chen, and Shih-Fu Chang · 2014
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(almost) no label no cry
Giorgio Patrini, Richard Nock, Paul Rivera, and Tiberio Caetano · 2014
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Learning with a Wasserstein loss
Charlie Frogner, Chiyuan Zhang, Hossein Mobahi, Mauricio Araya, and Tomaso A Poggio · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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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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Learning from label proportions for sar image classification
Yongke Ding, Yuanxiang Li, and Wenxian Yu · 2017
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Learning with label proportions via npsvm
Z. Qi, B. Wang, F. Meng, and L. Niu · 2017
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Deep learning from label proportions for emphysema quantification
Gerda Bortsova, Florian Dubost, Silas Ørting, Ioannis Katramados, Laurens Hogeweg, Laura Thomsen, Mathilde Wille, and Marleen de Bruijne · 2018
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From group to individual labels using deep features
Dimitrios Kotzias, Misha Denil, Nando de Freitas, and Padhraic Smyth · 2015
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Alter-cnn: An approach to learning from label proportions with application to ice-water classification
Fan Li and Graham Taylor · 2015
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Linear twin SVM for learning from label proportions
B. Wang, Z. Chen, and Z. Qi · 2015
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Wasserstein barycentric coordinates: histogram regression using optimal transport
Nicolas Bonneel, Gabriel Peyré, and Marco Cuturi · 2016
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Learning population-level diffusions with generative RNNs
Tatsunori Hashimoto, David Gifford, and Tommi Jaakkola · 2016
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Modeling attributes from category-attribute proportions
Felix X. Yu, Liangliang Cao, Michele Merler, Noel Codella, Tao Chen, John R. Smith, and Shih-Fu Chang
Cited in the paper.
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Unbalanced optimal transport: geometry and Kantorovich formulation
Lenaic Chizat, Gabriel Peyré, Bernhard Schmitzer, and François-Xavier Vialard · 2018
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Wasserstein discriminant analysis
Rémi Flamary, Marco Cuturi, Nicolas Courty, and Alain Rakotomamonjy · 2018
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Learning from label proportions on high-dimensional data
Yong Shi, Jiabin Liu, Zhiquan Qi, and Bo Wang · 2018
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Computational optimal transport
Gabriel Peyré and Marco Cuturi · 2019
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Learning from label proportions with pinball loss
Yong Shi, Limeng Cui, Zhensong Chen, and Zhiquan Qi · 2019
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