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 m<\/i>I<\/i>(\u03c0<\/i>), where m<\/i>\u00a0is the number of workers and I<\/i>(\u03c0<\/i>)\u00a0is the average Chernoff information that characterizes the workers\u2019 collective ability. Such an exact characterization of the error exponent allows us to state a precise sample size requirement m<\/i>\u22651\/I<\/i>(\u03c0<\/i>)\u00a0log(1\/\u03f5<\/i>)\u00a0\u00a0in order to achieve an\u00a0\u03f5 misclassification error. In addition, our results imply optimality of various forms of EM algorithms given accurate initializers of the model parameters<\/p>\n","protected":false},"excerpt":{"rendered":"
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(\u03c0), where m\u00a0is the number of workers and I(\u03c0)\u00a0is the average […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13556],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-245579","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-locale-en_us"],"msr_publishername":"","msr_edition":"Proceedings of the 33rd International Conference on Machine 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