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Eliciting labels from crowds is a potential way to obtain large labeled data.
A general class of coefficients of divergence of one distribution from another
Syed Mumtaz Ali and Samuel D Silvey · 1966
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Maximum likelihood estimation of observer error-rates using the em algorithm
Alexander Philip Dawid and Allan M Skene · 1979
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Combining labeled and unlabeled data with co-training
Avrim Blum and Tom Mitchell · 1998
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Information theory and statistics: A tutorial
Imre Csiszár, Paul C Shields, et al · 2004
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Labelme: a database and web-based tool for image annotation
Bryan C Russell, Antonio Torralba, Kevin P Murphy, and William T Freeman · 2008
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Whose vote should count more: Optimal integration of labels from labelers of unknown expertise
Jacob Whitehill, Ting-fan Wu, Jacob Bergsma, Javier R Movellan, and Paul L Ruvolo · 2009
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Learning from crowds
Vikas C Raykar, Shipeng Yu, Linda H Zhao, Gerardo Hermosillo Valadez, Charles Florin, Luca Bogoni, and Linda Moy · 2010
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The multidimensional wisdom of crowds
Peter Welinder, Steve Branson, Pietro Perona, and Serge J Belongie · 2010
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Variational inference for crowdsourcing
Qiang Liu, Jian Peng, and Alexander T Ihler · 2012
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Learning from the wisdom of crowds by minimax entropy
Denny Zhou, Sumit Basu, Yi Mao, and John C Platt · 2012
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Aggregating crowdsourced binary ratings
Nilesh Dalvi, Anirban Dasgupta, Ravi Kumar, and Vibhor Rastogi · 2013
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Dogs vs. cats competition
Kaggle · 2013
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Budget-optimal task allocation for reliable crowdsourcing systems
David R Karger, Sewoong Oh, and Devavrat Shah · 2014
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The cifar-10 dataset
Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton · 2014
Cited alongside, same era.
Gaussian process classification and active learning with multiple annotators
Filipe Rodrigues, Francisco Pereira, and Bernardete Ribeiro · 2014
Cited alongside, same era.
An Information Theoretic Framework For Designing Information Elicitation Mechanisms That Reward Truth-telling
Y. Kong and G. Schoenebeck · 2016
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f-gan: Training generative neural samplers using variational divergence minimization
Sebastian Nowozin, Botond Cseke, and Ryota Tomioka · 2016
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Data programming: Creating large training sets, quickly
Alexander J Ratner, Christopher M De Sa, Sen Wu, Daniel Selsam, and Christopher Ré · 2016
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Pulmonary nodule detection in ct images: false positive reduction using multi-view convolutional networks
Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Geert Litjens, Paul Gerke, Colin Jacobs, Sarah J Van Riel, Mathilde Marie Winkler Wille, Matiullah Naqibullah, Clara I Sánchez, and Bram van Ginneken · 2016
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A permutation-based model for crowd labeling: Optimal estimation and robustness
Nihar B Shah, Sivaraman Balakrishnan, and Martin J Wainwright · 2016
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Spectral methods meet em: A provably optimal algorithm for crowdsourcing
Yuchen Zhang, Xi Chen, Denny Zhou, and Michael I Jordan · 2014
Cited alongside, same era.
Aggnet: deep learning from crowds for mitosis detection in breast cancer histology images
Shadi Albarqouni, Christoph Baur, Felix Achilles, Vasileios Belagiannis, Stefanie Demirci, and Nassir Navab · 2016
Cited alongside, same era.
Achieving budget-optimality with adaptive schemes in crowdsourcing
Ashish Khetan and Sewoong Oh · 2016
Cited alongside, same era.
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Who said what: Modeling individual labelers improves classification
Melody Y Guan, Varun Gulshan, Andrew M Dai, and Geoffrey E Hinton · 2017
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Learning from noisy singly-labeled data
Ashish Khetan, Zachary C Lipton, and Anima Anandkumar · 2017
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Filipe Rodrigues and Francisco Pereira · 2017
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Water from two rocks: Maximizing the mutual information
Yuqing Kong and Grant Schoenebeck · 2018
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