Fetching the paper…
Reading the bibliography…
Crowdsourcing systems are popular for solving large-scale labelling tasks with low-paid workers.
A. P. Dawid and A. M. Skene, “Maximum likelihood estimation of observer error-rates using the EM algorithm,” Journal of the Royal Statistical Society. Series C (Applied Statistics) , vol. 28, no. 1, pp. 20–28, 1979
1979
Earlier work this paper cites.
J. Pearl, “Reverend bayes on inference engines: A distributed hierarchical approach,” in Proceedings of AAAI , 1982
1982
Earlier work this paper cites.
N. Littlestone and M. K. Warmuth, “The weighted majority algorithm,” in Proceedings of IEEE FOCS , 1989
1989
Earlier work this paper cites.
P. Smyth, U. Fayyad, M. Burl, P. Perona, and P. Baldi, “Inferring ground truth from subjective labelling of venus images,” in Proceedings of NIPS , 1995
1995
Earlier work this paper cites.
B. Bollobás, Random graphs . Springer, 1998
1998
Earlier work this paper cites.
Completely Automated Public Turing test to tell Computers and Humans Apart, “Captcha,” http://www.captcha.net/ , 2000
2000
Earlier work this paper cites.
R. Jin and Z. Ghahramani, “Learning with multiple labels,” in Proceedings of NIPS , 2003
2003
Earlier work this paper cites.
R. Snow, B. O’Connor, D. Jurafsky, and A. Y. Ng, “Cheap and fast-but is it good?: evaluating non-expert annotations for natural language tasks,” in Proceedings of EMNLP . Association for Computational Linguistics, 2008
2008
Earlier work this paper cites.
V. S. Sheng, F. Provost, and P. G. Ipeirotis, “Get another label? improving data quality and data mining using multiple, noisy labelers,” in Proceedings of ACM SIGKDD , 2008
2008
Earlier work this paper cites.
J. Whitehill, P. Ruvolo, T. Wu, J. Bergsma, and J. Movellan, “Whose vote should count more: Optimal integration of labels from labelers of unknown expertise,” in Proceedings of NIPS , 2009
2009
Earlier work this paper cites.
R. H. Keshavan, S. Oh, and A. Montanari, “Matrix completion from a few entries,” in 2009 IEEE International Symposium on Information Theory . IEEE, 2009, pp. 324–328
2009
Earlier work this paper cites.
P. Welinder, S. Branson, S. Belongie, and P. Perona, “The multidimensional wisdom of crowds,” in Proceedings of NIPS , 2010
2010
Cited alongside, same era.
V. C. Raykar, S. Yu, L. H. Zhao, G. H. Valadez, C. Florin, L. Bogoni, L. Moy, and D. Blei, “Learning from crowds,” Journal of Machine Learning Research , vol. 11, pp. 1297–1322, 2010
2010
Cited alongside, same era.
A. Ghosh, S. Kale, and P. McAfee, “Who moderates the moderators?: Crowdsourcing abuse detection in user-generated content,” in Proceedings of ACM EC , 2011
2011
Cited alongside, same era.
D. R. Karger, S. Oh, and D. Shah, “Iterative learning for reliable crowdsourcing systems,” in Proceedings of NIPS , 2011
2011
Cited alongside, same era.
Q. Liu, J. Peng, and A. T. Ihler, “Variational inference for crowdsourcing,” in Proceedings of NIPS , 2012
2012
Cited alongside, same era.
——, “Budget-optimal task allocation for reliable crowdsourcing systems,” Operations Research , vol. 62, no. 1, pp. 1–24, 2014
2014
Later among the works it cites.
Y. Zhang, X. Chen, D. Zhou, and M. I. Jordan, “Spectral methods meet em: A provably optimal algorithm for crowdsourcing,” in Proceedings of NIPS , 2014
2014
Later among the works it cites.
2014
Later among the works it cites.
E. Mossel, J. Neeman, and A. Sly, “Belief propagation, robust reconstruction and optimal recovery of block models,” in Proceedings of COLT , 2014
2014
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
D. Zhou, J. Platt, S. Basu, and Y. Mao, “Learning from the wisdom of crowds by minimax entropy,” in Proceedings of NIPS , 2012
2012
Cited alongside, same era.
N. Dalvi, A. Dasgupta, R. Kumar, and V. Rastogi, “Aggregating crowdsourced binary ratings,” in Proceedings of WWW , 2013
2013
Cited alongside, same era.
——, “Efficient crowdsourcing for multi-class labeling,” in Proceedings of ACM SIGMETRICS , 2013
2013
Cited alongside, same era.
H. Li, B. Yu, and D. Zhou, “Error rate analysis of labeling by crowdsourcing,” in Proceedings of ICML , 2013
2013
Cited alongside, same era.
2013
Cited alongside, same era.
S. Kudekar, T. Richardson, and R. L. Urbanke, “Spatially coupled ensembles universally achieve capacity under belief propagation,” IEEE Transactions on Information Theory , vol. 59, no. 12, pp. 7761–7813, 2013
2013
Cited alongside, same era.
2015
Later among the works it cites.
S. Park and J. Shin, “Max-product belief propagation for linear programming: applications to combinatorial optimization,” in Proceedings of UAI , 2015
2015
Later among the works it cites.
B. Hajek, Y. Wu, and J. Xu, “Exact recovery threshold in the binary censored block model,” in Proceedings of IEEE Information Theory Workshop , 2015
2015
Later among the works it cites.
C. Bordenave, M. Lelarge, and L. Massoulié, “Non-backtracking spectrum of random graphs: community detection and non-regular ramanujan graphs,” in Proceedings of IEEE FOCS , 2015
2015
Later among the works it cites.
A. Khetan and S. Oh, “Achieving budget-optimality with adaptive schemes in crowdsourcing,” in Advances in Neural Information Processing Systems , 2016, pp. 4844–4852
2016
Closest in time.