Publications

Publications

• A Survey on Bandit Learning in Matching Markets
  Shuai Li, Zilong Wang, Fang Kong
  Accepted in IJCAI Survey Track, 2025.

• Bandit Learning in Matching Markets with Indifference
  Fang Kong, Jingqi Tang, Mingzhu Li, Pinyan Lu, John C.S. Lui, Shuai Li.
  Proceedings of the 13th International Conference on Learning Representations (ICLR), 2025

• Improved Analysis for Bandit Learning in Matching Markets
  Fang Kong, Zilong Wang and Shuai Li.
  Proceedings of the 38th Conference on Neural Information Processing Systems (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
  Also invited as a poster presentation at ICLR, 2025

• 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 [arXiv]
  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
  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
  Fang Kong, Qizhi Li and Shuai Li.
  Computer Science, 2020