{"type":"ScholarlyArticle","title":"Cost-Saving Effect of Crowdsourcing Learning","authors":[{"name":"Lu Wang","slug":"lu-wang","given":"Lu","family":"Wang","affiliation":""},{"name":"Zhi-Hua Zhou","slug":"zhi-hua-zhou","given":"Zhi-Hua","family":"Zhou","affiliation":""}],"abstract":"Crowdsourcing is widely adopted in many domains as a popular paradigm to outsource work to individuals. In the machine learning community, crowdsourcing is commonly used as a cost-saving way to collect labels for training data. While a lot of effort has been spent on developing methods for inferring labels from a crowd, few work concentrates on the theoretical foundation of crowdsourcing learning. In this paper, we theoretically study the cost-saving effect of crowdsourcing learning, and present an upper bound for the minimally-sufficient number of crowd labels for effective crowdsourcing learning. Our results provide an understanding about how to allocate crowd labels efficiently, and are verified empirically.","doi":null,"url":"https://ijcai.org/proceedings/2016/301","pdf":"https://ijcai.org/proceedings/2016/0301.pdf","markdown":"https://ijcai.org/proceedings/2016/301.md","bibtex":"https://ijcai.org/proceedings/2016/bibtex/301","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":301,"pages":{"first":2111,"last":2116},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}