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We seek contributions from all areas of machine learning that help to make human computation and crowdsourcing systems more efficient, robust, scalable, and/or lead to a better understanding of such systems.   More specifically, the workshop invites applications of machine learning that advance our understanding in the following areas of crowdsourcing:

  • Efficient and robust aggregation of opinions or knowledge
  • Automatic quality control and verification schemes for user generated content
  • Task-specific incentive design that inhibits collusion or cheating
  • Analysis and modeling of interactions among humans
  • Analysis of  individual and aggregate decision making

We also encourage analysis and comparisons of various machine learning approaches for specific application scenarios in human computation and crowdsourcing systems.

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Posted March 26, 2012 by crowdml12

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