15 citations · 42 across the 13 of their papers we have counts for
Showing 2018Show all
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stat.ML2018
Optimizing the Wisdom of the Crowd: Inference, Learning, and Teaching
Yao Zhou, Jingrui He
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…
stat.ML2018★ 4 cited
Unlearn What You Have Learned: Adaptive Crowd Teaching with Exponentially Decayed Memory Learners
Yao Zhou, Arun Reddy Nelakurthi, Jingrui He
With the increasing demand for large amount of labeled data, crowdsourcing has been used in many large-scale data mining applications. However, most existing works in crowdsourcing…
cs.SI2018★ 2 cited
ImVerde: Vertex-Diminished Random Walk for Learning Network Representation from Imbalanced Data
Jun Wu, Jingrui He, Yongming Liu
Imbalanced data widely exists in many high-impact applications. An example is in air traffic control, where we aim to identify the leading indicators for each type of accident caus…