Shiyin Lu

Currently, I am a senior algorithm engineer at Alibaba. I obtained my Ph.D. degree from Department of Computer Science and Technology in Nanjing University, where I was very fortunate to be advised by Prof. Lijun Zhang. I was also a member of LAMDA, led by Prof. Zhi-Hua Zhou.

Supervisor

Professor Lijun Zhang.

Biography

I received my B.E. degree from School of the Gifted Young, University of Science and Technology of China (USTC) in June 2017. In the same year, I was recommended for admission to graduate study in Nanjing University (NJU). I graduated from NJU and joined Alibaba in June 2022.

Research Interests

I am interested in designing and analyzing machine learning algorithms that can effectively and efficiently interact with environments under either full-information or bandit feedback. Currently, I am focused on developing algorithms and theories for online learning in open environments.

Publication

  1. Non-stationary Dueling Bandits for Online Learning to Rank
    Shiyin Lu, Yuan Miao, Ping Yang, Yao Hu, Lijun Zhang.
    In Proceedings of the 6th APWeb and WAIM Joint International Conference on Web and Big Data (APWeb-WAIM 2022), to appear, 2022


  2. Non-stationary Continuum-armed Bandits for Online Hyperparameter Optimization [PDF]
    Shiyin Lu, Yu-Hang Zhou, Jing-Cheng Shi, Wenya Zhu, Qingtao Yu, Qing-Guo Chen, Qing Da, and Lijun Zhang
    In Proceedings of the 15th International Conference on Web Search and Data Mining (WSDM 2022), pages 618 – 627, 2022


  3. Revisiting Smoothed Online Learning [PDF, Supplementary]
    Lijun Zhang, Wei Jiang, Shiyin Lu, and Tianbao Yang
    In Advances in Neural Information Processing Systems 34 (NeurIPS 2021), pages 13599 – 13612, 2021


  4. Stochastic Bandits with Graph Feedback in Non-stationary Environments [PDF]
    Shiyin Lu, Yao Hu, and Lijun Zhang
    In Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI 2021), pages 8758 – 8766, 2021.


  5. Stochastic Graphical Bandits with Adversarial Corruptions [PDF]
    Shiyin Lu, Guanghui Wang, and Lijun Zhang
    In Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI 2021), pages 8749 – 8757, 2021.


  6. Searching Privately by Imperceptible Lying: A Novel Private Hashing Method with Differential Privacy [PDF]
    Yimu Wang, Shiyin Lu, and Lijun Zhang
    In Proceedings of the 28th ACM International Conference on Multimedia (ACM Multimedia 2020), pages 2700 – 2709, 2020.


  7. Minimizing Dynamic Regret and Adaptive Regret Simultaneously [PDF, arXiv]
    Lijun Zhang, Shiyin Lu, and Tianbao Yang
    In Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020), pages 309 – 319, 2020.


  8. SAdam: A Variant of Adam for Strongly Convex Functions [PDF]
    Guanghui Wang, Shiyin Lu, Quan Cheng, Wei-Wei Tu, and Lijun Zhang
    In International Conference on Learning Representations (ICLR 2020), 2020.


  9. Adapting to Smoothness: A More Universal Algorithm for Online Convex Optimization [PDF]
    Guanghui Wang, Shiyin Lu, Yao Hu, and Lijun Zhang
    In Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI 2020), pages 6162 – 6169, 2020.


  10. Optimal Algorithms for Lipschitz Bandits with Heavy-tailed Rewards [PDF, Supplementary]
    Shiyin Lu, Guanghui Wang, Yao Hu, and Lijun Zhang
    In Proceedings of the 36th International Conference on Machine Learning (ICML 2019), pages 4154 – 4163, 2019.


  11. Multi-Objective Generalized Linear Bandits [PDF, arXiv]
    Shiyin Lu, Guanghui Wang, Yao Hu, and Lijun Zhang
    In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019), pages 3080 – 3086, 2019.


  12. Adaptivity and Optimality: A Universal Algorithm for Online Convex Optimization [PDF, Supplementary]
    Guanghui Wang, Shiyin Lu, and Lijun Zhang
    In Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI 2019), 2019.


  13. Adaptive Online Learning in Dynamic Environments [PDF, arXiv]
    Lijun Zhang, Shiyin Lu, and Zhi-Hua Zhou
    In Advances in Neural Information Processing Systems 31 (NeurIPS 2018), pages 1323 – 1333, 2018.


Academic Service

  • Reviewer for Conferences: AAAI 2021 – 2022, NeurIPS 2020, NeurIPS 2022, ICML 2020 – 2022, IJCAI 2020 – 2022, AISTATS 2022, PAKDD 2022, ECAI 2020.

  • Reviewer for Journal: IEEE TNNLS, ACM TIST, Neurocomputing.

Awards & Honors

  • 入选AI华人新星百强榜单, 百度学术, 2021.
  • National Scholarship for Doctoral Student, MOE of PRC, 2019.
  • Outstanding Graduate Student, NJU, 2019.
  • National Scholarship for Master Student, MOE of PRC, 2018.
  • Outstanding Graduate Student, NJU, 2018.
  • Kwang-Hua Scholarship, USTC, 2016.

Teaching Assistant