Algorithmic Crowdsourcing

We work on fundamental issues in crowdsourcing, in particular, incentive mechanisms for paid crowdsourcing, algorithms and theory for crowdsourced problem solving.

People

  • Denny Zhou (Microsoft Research, Redmond)
  • John Platt (Microsoft Research, Redmond)
  • Xi Chen (University of California at Berkeley)
  • Nihar Shah (University of California at Berkeley)
  • Chao Gao (Yale University)
  • Qiang Liu (University of California at Irvine)
  • Yuchen Zhang (University of California at Berkeley)

Publications

Invited Talks

  • Aggregating Ordinal Labels from Crowds by Minimax Conditional Entropy. ICML'14 workshop:  Crowdsourcing and Human Computing, June 25, 2014. (slides)
  • Algorithmic crowdsourcing. NIPS'13 workshop:  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)

Co-organized Workshops

Software and Datasets

coming soon.