2020/03/21 by Doyeon Kim, Kim, Doyeon, Hye Won Chung +1
Computer Science · Decision Sciences · #Auction Theory and Applications #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Mobile Crowdsensing and Crowdsourcing #Privacy-Preserving Technologies in Data
paper · pdf · doi:10.48550/arxiv.2004.00101
openalex publication_date 2020/03/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We consider crowdsourced labeling under a d-type worker-task specialization model, where each worker and task is associated with one particular type among a finite set of types and a worker provides a more reliable answer to tasks of the matched type than to tasks of unmatched types. We design an inference algorithm that recovers binary task labels (up to any given recovery accuracy) by using worker clustering, worker skill estimation and weighted majority voting. The designed inference algorithm does not require any information about worker/task types, and achieves any targeted recovery accuracy with the best known performance (minimum number of queries per task).