@inproceedings{gao2016exact,
author = {Gao, Chao and Lu, Yu and Zhou, Denny},
title = {Exact Exponent in Optimal Rates for Crowdsourcing},
booktitle = {Proceedings of the 33rd International Conference on Machine Learning},
year = {2016},
month = {June},
abstract = {Crowdsourcing has become a popular tool for labeling large datasets. This paper studies the optimal error rate for aggregating crowdsourced labels provided by a collection of amateur workers. Under the Dawid-Skene probabilistic model, we establish matching upper and lower bounds with an exact exponent mI(π), where m is the number of workers and I(π) is the average Chernoff information that characterizes the workers’ collective ability. Such an exact characterization of the error exponent allows us to state a precise sample size requirement m≥1/I(π) log(1/ϵ)  in order to achieve an ϵ misclassification error. In addition, our results imply optimality of various forms of EM algorithms given accurate initializers of the model parameters},
url = {http://approjects.co.za/?big=en-us/research/publication/exact-exponent-in-optimal-rates-for-crowdsourcing/},
pages = {603-611},
edition = {Proceedings of the 33rd International Conference on Machine Learning},
}