Xin-Qiang Cai @ MSLAB, UTokyo

caixq.JPG 

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 third year Ph.D. student of Department of Complexity Science and Engineering in The University of Tokyo and a member of Sugiyama-Yokoya-Ishida Lab. Also, I am working as a research assistant on Beyond AI Institution.


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:

Preprint (* denotes equal contributions)

Yuting Tang*, Xin-Qiang Cai*, Yao-Xiang Ding, Qiyu Wu, Guoqing Liu, Masashi Sugiyama. Reinforcement Learning from Bagged Reward: A Transformer-based Approach for Instance-Level Reward Redistribution. In: Arxiv. [arxiv]

Soichiro Nishimori, Xin-Qiang Cai, Johannes Ackermann, Masashi Sugiyama. Leveraging Domain-Unlabeled Data in Offline Reinforcement Learning across Two Domains. In: Arxiv. [arxiv]

Xingyu Song, Zhan Li, Shi Chen, Xin-Qiang Cai, Kazuyuki Demachi. An Animation-based Augmentation Approach for Action Recognition from Discontinuous Video. In: Arxiv. [arxiv]


Publication (* denotes equal contributions)

Kaiyan Zhao, Qiyu Wu, Xin-Qiang Cai, Yoshimasa Tsuruoka. Leveraging Multi-lingual Positive Instances in Contrastive Learning to Improve Sentence Embedding. In: Proceedings of the 8th Conference of the European Chapter of the Association for Computational Linguistics (ECAL'24), Malta, Mar. 17-22, 2024. To appear. [arxiv]

Pushi Zhang*, Baiting Zhu*, Xin-Qiang Cai*, Li Zhao, Masashi Sugiyama, Jiang Bian. IG-Net: Image-Goal Network for Offline Visual Navigation on A Large-Scale Game Map. In: Proceedings of the Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS'23), 6th Robot Learning Workshop, New Orleans, US, Dec. 10-16, 2023. [paper] [openreview]

Xin-Qiang Cai, Yu-Jie Zhang, Chao-Kai Chiang, Masashi Sugiyama. Imitation Learning from Vague Feedback. In: Proceedings of the Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS'23), New Orleans, US, Dec. 10-16, 2023. [paper] [bibtex]

Xin-Qiang Cai, Pushi Zhang, Li Zhao, Jiang Bian, Masashi Sugiyama, Ashley Juan Llorens. Distributional Pareto-Optimal Multi-Objective Reinforcement Learning. In: Proceedings of the Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS'23), New Orleans, US, Dec. 10-16, 2023. [paper] [bibtex]

Xin-Qiang Cai, Yao-Xiang Ding, Zi-Xuan Chen, Yuan Jiang, Masashi Sugiyama, Zhi-Hua Zhou. Seeing Differently, Acting Similarly: Heterogeneously Observable Imitation Learning. In: Proceedings of the Eleventh International Conference on Learning Representations (ICLR'23) (spotlight), Kigali, Rwanda, May 1-5, 2023. [openreview] [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) (oral), Auckland, New Zealand, May 9-13, 2022. Page: 244-252. [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) (oral), online, May 3-7, 2021. Page: 279–287. [code] [paper] [bibtex]

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, Nov. 8-11, 2019. Page: 970-975. [code] [paper] [bibtex]


Patent

Service

Conference Journal

Awards & Honors

Correspondence

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