{"type":"ScholarlyArticle","title":"Toward a Robust and Universal Crowd-Labeling Framework","authors":[{"name":"Faiza Khan Khattak","slug":"faiza-khan-khattak","given":"Faiza Khan","family":"Khattak","affiliation":""}],"abstract":"One of the main challenges in crowd-labeling is to control for or determine in advance the proportion of low-quality/malicious labelers. We propose methods that estimate the labeler and data instance related parameters using frequentist and Bayesian approaches. All these approaches are based on expert-labeled instance (ground truth) for a small percentage of data to learn the parameters. We also derive a lower bound on the number of expert-labeled instances needed to get better quality labels.","doi":null,"url":"https://ijcai.org/proceedings/2016/577","pdf":"https://ijcai.org/proceedings/2016/0577.pdf","markdown":"https://ijcai.org/proceedings/2016/577.md","bibtex":"https://ijcai.org/proceedings/2016/bibtex/577","volume":{"slug":"2016","title":"Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2016","url":"https://ijcai.org/proceedings/2016/","year":2016,"location":"New York, NY, USA","dates":"9–15 July 2016","editors":"Subbarao Kambhampati","isbn":"978-1-57735-770-4; 978-1-57735-771-1","publisher":"AAAI Press / International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":577,"pages":{"first":4006,"last":4006},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}