Fang Kong

I am an Assistant Professor in the Department of Statistics and Data Science at the Southern University of Science and Technology (SUSTech).

I obtained my PhD degree from Shanghai Jiao Tong University under the supervision of Prof. Shuai Li. During my PhD, I received the Baidu Scholarship for 2023 (only 10 receipts worldwide). Before that, I obtained my bachelor's degree in Software engineering from Shandong University (1/318), and worked as a research intern at the Chinese University of Hong Kong, Tencent Wechat, Microsoft Research Asia, and Alibaba Damo Academy.

I am broadly interested in developing theoretically guaranteed algorithms for sequential decision-making problems, with a particular focus on multi-armed bandits and reinforcement learning, as well as their applications in online experimentation and recommendation systems. Recently, I have also been exploring the intersection of online learning and mechanism design.

Email  /  CV  /  GoogleScholar /  DBLP /  GitHub

profile photo

I am always looking for visiting students, RAs, and postdocs. Please contact me if you are interested.

Publications  (* denotes equal contribution)
Improved Analysis for Bandit Learning in Matching Markets
Fang Kong, Zilong Wang and Shuai Li.
Accepted at NeurIPS, 2024  
Sequential Optimum Test with Multi-armed Bandits for Online Experimentation
Fang Kong, Penglei Zhao, Shichao Han, Yong Wang and Shuai Li.
Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (CIKM, Applied Research Paper Track), 2024  
Which LLM to Play? Convergence-Aware Online Model Selection with Time-Increasing Bandits [arXiv]
Yu Xia*, Fang Kong*, Tong Yu, Liya Guo, Ryan A. Rossi, Sungchul Kim and Shuai Li.
The Web Conference (WWW), 2024  
Improved Bandits in Many-to-one Matching Markets with Incentive Compatibility [arXiv]
Fang Kong and Shuai Li.
Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2024  
Improved Regret Bounds for Linear Adversarial MDPs via Linear Optimization
Fang Kong*, Xiangcheng Zhang*, Baoxiang Wang and Shuai Li.
Transactions on Machine Learning Research (TMLR), 2024  
Best-of-three-worlds Analysis for Linear Bandits with Follow-the-regularized-leader Algorithm [arXiv]
Fang Kong, Canzhe Zhao and Shuai Li.
Proceedings of 36th Conference on Learning Theory (COLT), 2023  
Online Influence Maximization under Decreasing Cascade Model [arXiv]
Fang Kong, Jize Xie, Baoxiang Wang, Tao Yao and Shuai Li.
Proceedings of the 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2023  
Stochastic No-Regret Learning for General Games with Variance Reduction
Yichi Zhou, Fang Kong and Shuai Li.
Proceedings of the 11th International Conference on Learning Representations (ICLR), 2023  
Player-optimal Stable Regret for Bandit Learning in Matching Markets [arXiv] [slides]
Fang Kong and Shuai Li
Proceedings of the 34th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), 2023  
Simultaneously Learning Stochastic and Adversarial Bandits with General Graph Feedback [arXiv] [slides]
Fang Kong, Yichi Zhou and Shuai Li
Proceedings of the 39th International Conference on Machine Learning (ICML), 2022  
Thompson Sampling for Bandit Learning in Matching Markets [arXiv]
Fang Kong, Junming Yin and Shuai Li
Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI), 2022  
The Hardness Analysis of Thompson Sampling for Combinatorial Semi-bandits with Greedy Oracle [arXiv] [slides] [poster]
Fang Kong, Yueran Yang, Wei Chen and Shuai Li
Proceedings of the 35th Conference on Neural Information Processing Systems (NeurIPS), 2021  
Combinatorial Online Learning based on Optimizing Feedbacks  (in Chinese)
Fang Kong, Yueran Yang, Wei Chen and Shuai Li
Big Data Research, 2021  
Online Influence Maximization under Linear Threshold Model [arXiv][slides][poster]
Shuai Li, Fang Kong, Kejie Tang, Qizhi Li and Wei Chen.
Proceedings of the 34th Conference on Neural Information Processing Systems (NeurIPS), 2020  
A Survey on Online Influence Maximization   (in Chinese)
Fang Kong, Qizhi Li and Shuai Li
Computer Science, 2020  
Tutorials & Invited Talks
"Reducing Exploration Cost and Risk in Online Learning: From Single Agent to Multi-agent Systems"
Invited Talk at the CCIR Youth Forum, Wuhan, China
2024.10
"Bandit Learning in Matching Markets"
Invited Talk at RLChina, Guangzhou, China
Invited Talk at the CCF Theoretical Computer Science Doctoral Forum, Changchun, China
2024.10
2024.6
"Bandit Learning in Mechanism Design: Matching Markets and Beyond" [slides]
Tutorial at AAMAS, Aucland, New Zealand
2024.5
"Best-of-three-worlds Analysis for Linear Bandits with Follow-the-regularized-leader Algorithm"
Invited Talk at the IJTCS-FAW Female Forum, Macau, China
2023.8
Internships and Research Experiences
Intern at Tencent WXG as a member of the Tencent Rhino-Bird Research Elite Program, Shenzhen, China. 2022.7-2024.7
Visiting student of Prof. John C.S. Lui at the Chinese University of Hong Kong, Hong Kong, China. 2023.2-2023.8
Research Intern of Dr. Yichi Zhou at Microsoft Research Asia (MSRA), Beijing, China. 2021.12-2022.5
Research Intern of Dr. Xue Wang and Dr. Tao Yao at Alibaba DAMO Academy, Hangzhou, China. 2021.6-2021.8
Awards
  • Outstanding Graduates of Shanghai, 2024
  • Baidu Scholarship (only 10 recipients worldwide), 2024
  • National Scholarship (for Ph.D. students), from the ministry of Education of China, 2023,2022
  • AAMAS Student Scholarship, 2023
  • Award of Excellence in Stars of Tomorrow Internship Program, Microsoft Research Asia, 2022
  • Outstanding Graduates of Shandong Province, 2020
  • Honors Bachelor of Shandong University, 2020
  • Outstanding Undergraduate Thesis of Shandong University, 2020
  • National Scholarship (for undergraduate students), from the ministry of Education of China, 2018,2017
Professional Services
Conference Reviewer for
  • International Conference on Learning Representations (ICLR), 2025
  • Conference on Neural Information Processing Systems (NeurIPS), 2024-2021
  • International Conference on Machine Learning (ICML), 2023-2022
  • Algorithmic Learning Theory (ALT), 2025
  • The Web Conference (WWW), 2025-2024
Journal Reviewer for
  • IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI)
  • Transactions on Machine Learning Research (TMLR)
Teaching
  • STA 303 Artificial Intelligence (Undergraduate), SUSTech, Fall 2024