  The unprecedented demand for large amount of data has catalyzed the trend of
combining human insights with machine learning techniques, which facilitate the
use of crowdsourcing to enlist label information both effectively and
efficiently. The classic work on crowdsourcing mainly focuses on the label
inference problem under the categorization setting. However, inferring the true
label requires sophisticated aggregation models that usually can only perform
well under certain assumptions. Meanwhile, no matter how complicated the
aggregation model is, the true model that generated the crowd labels remains
unknown. Therefore, the label inference problem can never infer the ground
truth perfectly. Based on the fact that the crowdsourcing labels are abundant
and utilizing aggregation will lose such kind of rich annotation information
(e.g., which worker provided which labels), we believe that it is critical to
take the diverse labeling abilities of the crowdsourcing workers as well as
their correlations into consideration. To address the above challenge, we
propose to tackle three research problems, namely inference, learning, and
teaching.
