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MIDAS researchers’ papers accepted at ACM KDD data science conference in London

Several U-M faculty affiliated with MIDAS will participate in the KDD2018 Conference in London in August. The meeting is held by the Associate for Computing Machinery’s Special Interest Group in Knowledge Discovery and Data Mining (KDD).

U-M researchers had the following papers accepted:

Learning Adversarial Networks for Semi-Supervised Text Classification via Policy Gradient
Yan Li (U-M); Jieping Ye (U-M)

TINET: Learning Invariant Networks via Knowledge Transfer
Chen Luo (Rice University); Zhengzhang Chen (NEC Laboratories America); Lu-An Tang (NEC Laboratories America); Anshumali Shrivastava (Rice University); Zhichun Li (NEC Laboratories America); Haifeng Chen (NEC Laboratories America); Jieping Ye (U-M)

Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts
Jiaqi Ma(U-M); Zhe Zhao (Google); Xinyang Yi (Google); Jilin Chen (Google); Lichan Hong (Google); Ed Chi (Google)

Learning Credible Models
Jiaxuan Wang (U-M); Jeeheh Oh (U-M); Haozhu Wang (U-M); Jenna Wiens (U-M)

Deep Multi-Output Forecasting: Learning to Accurately Predict Blood Glucose Trajectories
Ian Fox (U-M); Lynn Ang (U-M); Mamta Jaiswal (U-M); Rodica Pop-Busui (U-M); Jenna Wiens (U-M)

ActiveRemediation: The Search for Lead Pipes in Flint, Michigan
Jacob Abernethy (Georgia Institute of Technology); Alex Chojnacki (U-M); Arya Farahi (U-M); Eric Schwartz (U-M); Jared Webb (Brigham Young University)

Career Transitions and Trajectories: A Case Study in Computing
Tara Safavi (U-M); Maryam Davoodi (Purdue University); Danai Koutra (U-M)

In addition, U-M Professor Jieping Ye will present at the event’s Artificial Intelligence in Transportation tutorial, and U-M Assistant Professor Qiaozhu Mei will speak as part of Deep Learning Day.