Cost-Saving Effect of Crowdsourcing Learning
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 2111-2116.
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.
@inproceedings{ijcai2016p301,
title = {Cost-Saving Effect of Crowdsourcing Learning},
author = {Wang, Lu and Zhou, Zhi-Hua},
booktitle = {Proceedings of the Twenty-Fifth International Joint Conference on
Artificial Intelligence, {IJCAI-16}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Subbarao Kambhampati},
pages = {2111--2116},
year = {2016},
month = {7},
url = {https://ijcai.org/proceedings/2016/301},
}
TY - CPAPER TI - Cost-Saving Effect of Crowdsourcing Learning AU - Wang, Lu AU - Zhou, Zhi-Hua T2 - Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2016 SP - 2111 EP - 2116 UR - https://ijcai.org/proceedings/2016/301 ER -Copied
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