  In this paper, we analyze PAC learnability from labels produced by
crowdsourcing. In our setting, unlabeled examples are drawn from a distribution
and labels are crowdsourced from workers who operate under classification
noise, each with their own noise parameter. We develop an end-to-end
crowdsourced PAC learning algorithm that takes unlabeled data points as input
and outputs a trained classifier. Our three-step algorithm incorporates
majority voting, pure-exploration bandits, and noisy-PAC learning. We prove
several guarantees on the number of tasks labeled by workers for PAC learning
in this setting and show that our algorithm improves upon the baseline by
reducing the total number of tasks given to workers. We demonstrate the
robustness of our algorithm by exploring its application to additional
realistic crowdsourcing settings.
