Xin-Qiang Cai @ MSLAB, UTokyo

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蔡 欣 强
Xin-Qiang Cai
Ph.D. Student, Sugiyama-Yokoya-Ishida Lab
Department of Complexity Science and Engineering
Graduate School of Frontier Sciences
The University of Tokyo, Tokyo, Japan

Supervisor: Professor Masashi Sugiyama

Email:cai@ms.k.u-tokyo.ac.jp, jkrsndivide@gmail.com
Laboratory: Department of Complexity Science and Engineering, Graduate School of Frontier Sciences (Kashiwa campus)

Biography

Currently I am a first year Ph.D. student of Department of Complexity Science and Engineering in The University of Tokyo and a member of Sugiyama-Yokoya-Ishida Lab.


I got my M.Sc. degree in Computer Science and Technology in June 2021 from Nanjing University as a member of LAMDA Group, supervised by professor Zhi-Hua Zhou and professor Yuan Jiang.


I got my B.Sc. degree in Aircraft Design and Engineering in June 2018 from Northwestern Polytechnical University. In the same year, I was admitted to study for a M.Sc degree in Nanjing University without entrance examination.


Research Interests

Currently I am focusing on the subfields:

Publication

Xin-Qiang Cai, Peng Zhao, Kai Ming Ting, Xin Mu, Yuan Jiang. Nearest Neighbor Ensembles: An Effective Method for Difficult Problems in Streaming Classification with Emerging New Classes. In: Proceedings of the 19th IEEE International Conference on Data Mining (ICDM'19), Beijing, China, 2019. Page: 970-975. [code] [paper] [bibtex]

Xin-Qiang Cai, Yao-Xiang Ding, Yuan Jiang, Zhi-Hua Zhou. Imitation Learning from Pixel-Level Demonstrations by HashReward. In: Proceedings of the 20th International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS'21), online, 2021. Page: 279–287. [code] [paper] [bibtex]

Zi-Xuan Chen*, Xin-Qiang Cai*, Yuan Jiang, Zhi-Hua Zhou. Anomaly Guided Policy Learning from Imperfect Demonstrations. In: Proceedings of the 21th International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS'22), online, 2022. To appear. [paper] [bibtex]

Xin-Qiang Cai, Yao-Xiang Ding, Zi-Xuan Chen, Yuan Jiang, Masashi Sugiyama, Zhi-Hua Zhou. Seeing Differently, Acting Similarly: Imitation Learning with Heterogeneous Observations. Preprinted. [arxiv]


Patent

Service

Conference Journal

Awards & Honors

Correspondence

Email: cai@ms.k.u-tokyo.ac.jp, jkrsndivide@gmail.com