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Crowdsourcing is a popular paradigm for effectively collecting labels at low cost.
Maximum likelihood estimation of observer error-rates using the em algorithm
A. P. Dawid and A. M. Skene · 1979
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Assouad, Fano, and Le Cam
B. Yu · 1997
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Theory of Point Estimation
E. Lehmann and G. Casella · 2003
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Cheap and fast—but is it good? Evaluating non-expert annotations for natural language tasks
R. Snow, B. O’Connor, D. Jurafsky, and A. Y. Ng · 2008
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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Whose vote should count more: Optimal integration of labels from labelers of unknown expertise
J. Whitehill, P. Ruvolo, T. Wu, J. Bergsma, and J. R. Movellan · 2009
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Learning from crowds
V. C. Raykar, S. Yu, L. H. Zhao, G. H. Valadez, C. Florin, L. Bogoni, and L. Moy · 2010
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The multidimensional wisdom of crowds
P. Welinder, S. Branson, S. Belongie, and P. Perona · 2010
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Who moderates the moderators? Crowdsourcing abuse detection in user-generated content
A. Ghosh, S. Kale, and P. McAfee · 2011
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Overview of the trec 2011 crowdsourcing track
M. Lease and G. Kazai · 2011
Cited alongside, same era.
A spectral algorithm for latent dirichlet allocation
A. Anandkumar, D. P. Foster, D. Hsu, S. M. Kakade, and Y.-K. Liu · 2012
Cited alongside, same era.
Tensor decompositions for learning latent variable models
A. Anandkumar, R. Ge, D. Hsu, S. M. Kakade, and M. Telgarsky · 2012
Cited alongside, same era.
A method of moments for mixture models and hidden markov models
A. Anandkumar, D. Hsu, and S. M. Kakade · 2012
Cited alongside, same era.
A spectral algorithm for learning hidden markov models
D. Hsu, S. M. Kakade, and T. Zhang · 2012
Cited alongside, same era.
Variational inference for crowdsourcing
Optimistic knowledge gradient policy for optimal budget allocation in crowdsourcing
X. Chen, Q. Lin, and D. Zhou · 2013
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Aggregating crowdsourced binary ratings
N. Dalvi, A. Dasgupta, R. Kumar, and V. Rastogi · 2013
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Learning mixtures of discrete product distributions using spectral decompositions
P. Jain and S. Oh · 2013
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Efficient crowdsourcing for multi-class labeling
D. R. Karger, S. Oh, and D. Shah · 2013
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Partial information from spectral methods
P. Liang · 2013
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Contrastive learning using spectral methods
J. Zou, D. Hsu, D. Parkes, and R. Adams · 2013
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Q. Liu, J. Peng, and A. T. Ihler · 2012
Cited alongside, same era.
Learning from the wisdom of crowds by minimax entropy
D. Zhou, J. C. Platt, S. Basu, and Y. Mao · 2012
Cited alongside, same era.
A tensor spectral approach to learning mixed membership community models
A. Anandkumar, R. Ge, D. Hsu, and S. M. Kakade · 2013
Cited alongside, same era.
Spectral experts for estimating mixtures of linear regressions
A. T. Chaganty and P. Liang · 2013
Cited alongside, same era.
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Minimax optimal convergence rates for estimating ground truth from crowdsourced labels
C. Gao and D. Zhou · 2014
Closest in time.
Budget-optimal task allocation for reliable crowdsourcing systems
D. R. Karger, S. Oh, and D. Shah · 2014
Closest in time.
Aggregating ordinal labels from crowds by minimax conditional entropy
D. Zhou, Q. Liu, J. C. Platt, and C. Meek · 2014
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