We work on fundamental issues in crowdsourcing, in particular, incentive mechanisms for paid crowdsourcing, algorithms and theory for crowdsourced problem solving.
- Chao Gao and Dengyong Zhou, Minimax Optimal Convergence Rates for Estimating Ground Truth from Crowdsourced Labels, no. MSR-TR-2013-110, October 2013
- 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