Yikun Ban

Associate Professor
School of Computer Science and Engineering
Beihang University

I am a tenure-track associate professor in the School of Computer Science and Engineering at Beihang University and a memeber of the State Key Laboratory of Software Development Environment. Previously, I was a postdoc and obtained my Ph.D. degree at Computer Science, University of Illinois at Urbana-Champaign, where I worded closely with Prof. Jingrui He, Prof. Hanghang Tong, and Prof. Arindam Banerjee. Prior to this, I obtained my Master's degree from EECS, Peking University and bachelor's degree from Wuhan University.
I am interested in principled algorithms in the space of multi-armed bandits/reinforcement learning and deep learning, to solve real-world exploitation-exploration problems. Current research topics:
  • Reinforcement Learning with Human Feedback
  • Language Models (Agents) with Exploration
  • Neural Contextual Bandits - Algorithm and Theory

yikunb[at]buaa.edu.cn, yikunb2[at]illinois.edu
Google Scholar

News
  • 2024.12   "PageRank Bandit" is accepted by NeurIPS 2024, in which we first use bandit perspective to solve link prediction.
  • 2024.12   "Robust Neural Contextual Bandit" is accepted by NeurIPS 2024, in which we remove the Positive Definite assumption for NTK Matrix.
  • 2024.05   One paper is accepted by KDD 2024.
  • 2022.09   One paper was accepted by NeurIPS 2022.
  • 2022.01   One paper was accepted by ICLR 2022 Spotlight. EE-Net provides a novel neural-based exploration strategy, distinct from standard UCB and TS.

Preprint(*Equal Contribution, #Corresponding)

  1. Jiaru Zou, Yikun Ban#, Zihao Li, Yunzhe Qi, Ruizhong Qiu, Ling Yang, Jingrui He#
    Transformer Copilot: Learning from The Mistake Log in LLM Fine-tuning
  2. Zixuan Huang, Yikun Ban#, Lean Fu, Xiaojie Li, Zhongxiang Dai, Jianxin Li, Deqing Wang#
    Adaptive Sample Scheduling for Direct Preference Optimization

Publications(*Equal Contribution, #Corresponding)

  1. Xiaodong Lu, Mingzhe Liu, Tongyu Zhu, Leilei Sun, Jibin Wang, Weifeng Lv, Yikun Ban, Deqing Wang
    Adaptive Sampling-based Dynamic Graph Learning for Information Diffusion Prediction
    ACM Transactions on Information Systems (2025) ( TOIS )
  2. Xinrui He, Yikun Ban#, Jiaru Zou, Tianxin Wei, Curtiss Cook, Jingrui He#
    LLM-Forest: Ensemble Learning of LLMs with Graph-Augmented Prompts for Data Imputation
    The 63rd Annual Meeting of the Association for Computational Linguistics, Findings ( ACL'25 )
  3. Zihao Li, Lecheng Zheng, Bowen Jin, Dongqi Fu, Baoyu Jing, Yikun Ban, Jingrui He, Jiawei Han
    Can Graph Neural Networks Learn Language with Extremely Weak Text Supervision?
    The 63rd Annual Meeting of the Association for Computational Linguistics, Main( ACL'25 )
  4. Yunzhe Qi, Yikun Ban, Arindam Banerjee, Jingrui He
    Robust Neural Contextual Bandit against Adversarial Corruptions
    Thirty-eighth Conference on Neural Information Processing Systems ( NeurIPS'24 )
  5. Yikun Ban*, Jiaru Zou*, Zihao Li, Yunzhe Qi, Dongqi Fu, Jian Kang, Hanghang Tong, Jingrui He
    PageRank Bandits for Link Prediction
    Thirty-eighth Conference on Neural Information Processing Systems ( NeurIPS'24 )
  6. Yikun Ban*, Yunzhe Qi*, Tianxin Wei, Lihui Liu, Jingrui He
    Meta Clustering of Neural Bandits
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining ( KDD'24 )
  7. Yikun Ban, Yuchen Yan, Arindam Banerjee, Jingrui He
    Neural Exploitation and Exploration of Contextual Bandits

    To Appear
  8. Yikun Ban, Yunzhe Qi, Jingrui He
    Neural Contextual Bandits for Personalized Recommendation
    The Web Conference, Tutorial ( WWW'24)
    [ ]   []
  9. Yikun Ban, Ishika Agarwal, Ziwei Wu, Yada Zhu, Kommy Weldemariam, Hanghang Tong, and Jingrui He
    Neural Active Learning Beyond Bandits
    International Conference on Learning Representations ( ICLR'24 )
  10. Rohan Deb, Yikun Ban, Shiliang Zuo, Jingrui He, Arindam Banerjee
    Contextual Bandits with Online Neural Regression
    International Conference on Learning Representations ( ICLR'24 )
  11. Yunzhe Qi*, Yikun Ban*, Tianxin Wei, Jiaru Zou, Huaxiu Yao, and Jingrui He
    Meta-Learning with Neural Bandit Scheduler
    Thirty-seventh Conference on Neural Information Processing Systems ( NeurIPS'23 )
  12. Yunzhe Qi*, Yikun Ban*, and Jingrui He
    Graph Neural Bandits
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining ( KDD'23 )
  13. Yikun Ban*, Yuheng Zhang*, Hanghang Tong, Arindam Banerjee, and Jingrui He
    Improved Algorithms for Neural Active Learning
    Thirty-sixth Conference on Neural Information Processing Systems ( NeurIPS'22 )
  14. Dongqi Fu, Yikun Ban, Hanghang Tong, Ross Maciejewski, and Jingrui He
    DISCO: Comprehensive and Explainable Disinformation Detection
    ACM International Conference on Information and Knowledge Management (Demo Track) ( CIKM'22 )
  15. Yunzhe Qi, Yikun Ban, and Jingrui He
    Neural Bandit with Arm Group Graph
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining ( KDD'22 )
  16. Yikun Ban, Yuchen Yan, Arindam Banerjee, and Jingrui He
    EE-Net: Exploitation-Exploration Neural Networks in Contextual Bandits
    International Conference on Learning Representations ( ICLR'22, Spotlight)
  17. Yikun Ban and Jingrui He
    Convolutional Neural Bandit for Visual-aware Recommendation
    Preprint: ArXiv:2107.07438
  18. Yikun Ban, Jingrui He, and Curtiss B. Cook
    Multi-Facet Contextual Bandits: A Neural Network Perspective
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining ( KDD'21 )
  19. Yikun Ban and Jingrui He
    Local Clustering in Contextual Multi-Armed Bandits
    The Web Conference ( WWW'21 )
  20. Yuchen Yan, Lihui Liu, Yikun Ban, Baoyu Jing, and Hanghang Tong
    Dynamic Knowledge Graph Alignment
    AAAI Conference on Artificial Intelligence ( AAAI'21 )
  21. Yikun Ban and Jingrui He
    Generic Outlier Detection in Multi-Armed Bandit
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining ( KDD'20 )
  22. Yikun Ban, Xin liu, Ling Huang, Yitao Duan, Xue Liu, and Wei Xu
    No Place to Hide: Catching Fraudulent Entities in Tensors
    The Web Conference ( WWW ’19 )

Education

    Sep. 2019 - Dec. 2023
    Ph.D., Computer Science, University of Illinois at Urbana-Champaign, Illinois, US
    Sep. 2016 - Jul. 2019
    M.S., Computer Science, Peking University, Beijing, China
    Sep. 2012 - Jul. 2016
    B.S., School of Software Engineering, Wuhan University, Wuhan, China

Internship

  • May. 2022 - Aug. 2022, Applied Scientist Intern, AI Platform, Amazon

Services

  • Program Committee:ICML'22, KDD'22, AAAI'22, KDD'21, IJCAI'21, CIKM'21