Toward a Robust and Universal Crowd-Labeling Framework

Khattak, Faiza Khan

Toward a Robust and Universal Crowd-Labeling Framework

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Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 4006-4006.

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.

Faiza Khan Khattak. 2016. Toward a Robust and Universal Crowd-Labeling Framework. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16. IJCAI, 4006–4006. https://ijcai.org/proceedings/2016/577

@inproceedings{ijcai2016p577,
  title     = {Toward a Robust and Universal Crowd-Labeling Framework},
  author    = {Khattak, Faiza Khan},
  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     = {4006--4006},
  year      = {2016},
  month     = {7},
  url       = {https://ijcai.org/proceedings/2016/577},
}

TY  - CPAPER
TI  - Toward a Robust and Universal Crowd-Labeling Framework
AU  - Khattak, Faiza Khan
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  - 4006
EP  - 4006
UR  - https://ijcai.org/proceedings/2016/577
ER  -