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There is a rapidly increasing interest in crowdsourcing for data labeling.
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Convex Optimization
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Maximum entropy density estimation with generalized regularization and an application to species distribution modeling
M. Dudik, S. J. Phillips, and R. E. Schapire · 2007
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Get another label? Improving data quality and data mining using multiple noisy labelers
V. S. Sheng, F. Provost, and P. G. Ipeirotis · 2008
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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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Whose vote should count more: optimal integration of labels from labelers of unknown expertise
J. Whitehill, P. Ruvolo, T. Wu, J. Bergsma, and J. 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
Adaptive crowdsourcing algorithms for the bandit survey problem
I. Abraham, O. Alonso, V. Kandylas, and A. Slivkins · 2013
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Optimistic knowledge gradient policy for optimal budget allocation in crowdsourcing
X. Chen, Q. Lin, and D. Zhou · 2013
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POMDP-based control of workflows for crowdsourcing
P. Dai, C. H. Lin, Mausam, and D. S. Weld · 2013
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Aggregating crowdsourced binary ratings
N. Dalvi, A. Dasgupta, R. Kumar, and V. Rastogi · 2013
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Minimax optimal convergence rates for estimating ground truth from crowdsourced labels
C. Gao and D. Zhou · 2013
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P. Welinder, S. Branson, S. Belongie, and P. Perona · 2010
Cited alongside, same era.
Who moderates the moderators? Crowdsourcing abuse detection in user-generated content
A. Ghosh, S. Kale, and P. McAfee · 2011
Cited alongside, same era.
Algorithm discovery by protein folding game players
F. Khatib, S. Cooper, M. D. Tyka, K. Xu, I. Makedon, Z. Popović, D. Baker, and F. Players · 2011
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http://www.machinedlearnings.com/2011/08/low-rank-confusion-modeling-of.html, 2011a
P. Mineiro · 2011
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http://www.machinedlearnings.com/2011/02/ordered-values-and-mechanical-turk-part.html, 2011b
P. Mineiro · 2011
Cited alongside, same era.
Crowdsourcing translation: Professional quality from non-professionals
O. F. Zaidan and C. Callison-Burch · 2011
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.
Q. Liu, M. Steyvers, and A. Ihler · 2013
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Adaptive time windows for real-time crowd captioning
M. Murphy, C. D. Miller, W. S. Lasecki, and J. P. Bigham · 2013
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Truthful incentives in crowdsourcing tasks using regret minimization mechanisms
A. Singla and A. Krause · 2013
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N. Anari, G. Goel, and A. Nikzad · 2014
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Demographic estimation from face images: Human vs. machine performance
H. Han, C. Otto, X. Liu, and A. K. Jain · 2014
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Adaptive contract design for crowdsourcing markets: Bandit algorithms for repeated principal-agent problems
C.-J. Ho, A. Slivkins, and J. Wortman · 2014
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Budget-optimal task allocation for reliable crowdsourcing systems
D. R. Karger, S. Oh, and D. Shah · 2014
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Double or nothing: Multiplicative incentive mechanisms for crowdsourcing
N. B. Shah and D. Zhou · 2014
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Community-based Bayesian aggregation models for crowdsourcing
M. Venanzi, J. Guiver, G. Kazai, P. Kohli, and M. Shokouhi · 2014
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Spectral methods meet EM: A provably optimal algorithm for crowdsourcing
Y. Zhang, X. Chen, D. Zhou, and M. I. Jordan · 2014
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Aggregating ordinal labels from crowds by minimax conditional entropy
D. Zhou, Q. Liu, J. C. Platt, and C. Meek · 2014
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