Conference : IEEE International Symposium on Information Theory (2022)
Abstract : Crowdsourcing has emerged as an effective platform to label data with low cost by using non-expert workers. However, inferring correct labels from multiple noisy answers on data has been a challenging problem, since the quality of answers varies widely across tasks and workers. We propose a highly general crowdsourcing model in which the reliability of each worker can vary depending on the type of a given task, where the number of types d can scale in the number of tasks. In this model, we characterize the optimal sample complexity to correctly infer the unknown labels within any given accuracy, and propose an algorithm achieving the order-wise optimal result
