We work on fundamental issues in crowdsourcing, in particular, incentive mechanisms for paid crowdsourcing, algorithms and theory for crowdsourced problem solving.
- Dengyong Zhou, Qiang Liu, John C. Platt, and Christopher Meek. Aggregating Ordinal Labels from Crowds by Minimax Conditional Entropy, in Proceedings of the 31st International Conference on Machine Learning (ICML), 2014.
- Xi Chen, Qihang Lin, and Dengyong Zhou. Statistical Decision Making for Optimal Budget Allocation in Crowd Labeling.Technical Report arXiv:1403.3080, March, 2014. Submitted.
- Chao Gao and Dengyong Zhou. Minimax Optimal Convergence Rates for Estimating Ground Truth from Crowdsourced Labels. Technical Report arXiv:1310.5764, October, 2013. Submitted.
- Xi Chen, Qihang Lin, and Dengyong Zhou. Optimistic Knowledge Gradient Policy for Optimal Budget Allocation in Crowdsourcing, in Proceedings of the 30th International Conference on Machine Learning (ICML), 2013
- Dengyong Zhou, John Platt, Sumit Basu, and Yi Mao. Learning from the Wisdom of Crowds by Minimax Entropy, in Advances in Neural Information Processing Systems (NIPS), December 2012
- Algorithmic crowdsourcing. NIPS Workshop on Crowdsourcing: Theory, Algorithms and Applications, December 9, 2013. (slides)
- Learning from the Wisdom of Crowds by Minimax Entropy. UC Berkeley, Neyman Seminar, March 15, 2013. (slides)
- Learning from the Wisdom of Crowds by Minimax Entropy. Facebook, March 14, 2013. (slides)
- A Minimax Entropy Method for Learning the Wisdom of Crowds. Joint UW-Microsoft Research Machine Learning Workshop. Oct 26, 2012. (slides)
ICML'14 workshop: Crowdsourcing and Human Computing
NIPS'13 workshop: Crowdsourcing: Theory, Algorithms and Applications
- ICML'13 workshop: Machine Learning Meets Crowdsourcing