Research Interests

Online xingkong星空中国官网, Reinforcement xingkong星空中国官网, Machine xingkong星空中国官网

Education

2020.9-2024.6 Shanghai Jiao Tong University, PhD in Computer Science

2016.9-2020.6 Shandong University, Bachelor’s Degree in Software Engineering

Research Experiences

2023.2-2023.8 The Chinese University of Hong Kong, Research Assistant

2022.7-2024.7 Tencent WXG, Research Intern

2021.12-2022.5 Microsoft Research Asia, Research Intern

2021.6-2021.8 Alibaba DAMO Academy, Research Intern

Publications

1. Yu Xia*, Fang Kong*, Tong Yu, Liya Guo, Ryan A. Rossi, Sungchul Kim, xingkong星空中国官网, “Convergence-Aware Online Model Selection with Time-Increasing Bandits”, The Web Conference (WWW), 2024.

2. Fang Kong, xingkong星空中国官网, “Improved Bandits in Many-to-one Matching Markets with Incentive Compatibility”, Proceedings of the 38th Annual AAAI Conference on Artificial Intelligence (AAAI), 2024. 

3. Fang Kong*, Xiangcheng Zhang*, Baoxiang Wang, xingkong星空中国官网, “Improved Regret Bounds for Linear Adversarial MDPs via Linear Optimization”, Transactions on Machine Learning Research (TMLR), 2024.

4. Fang Kong, Canzhe Zhao, xingkong星空中国官网, “Best-of-three-worlds Analysis for Linear Bandits with Follow-the-regularized-leader Algorithm”, Proceedings of the 36th Conference on Learning Theory (COLT), 2023.

5. Fang Kong, Jize Xie, Baoxiang Wang, Tao Yao, xingkong星空中国官网. “Online Influence Maximization under Decreasing Cascade Model”, Proceedings of the 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2023.

6. Yichi Zhou, Fang Kong, xingkong星空中国官网, “Stochastic No-Regret Learning for General Games with Variance Reduction”, International Conference on Learning Representations (ICLR), 2023.

7. Fang Kong, xingkong星空中国官网, “Player-optimal Stable Regret for Bandit Learning in Matching Markets”, Proceedings of the 2023 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA). 2023.

8. Fang Kong, Yichi Zhou, xingkong星空中国官网, “Simultaneously Learning Stochastic and Adversarial Bandits with General Graph Feedback”, International Conference on Machine Learning (ICML), 2022.

9. Fang Kong, Junming Yin, xingkong星空中国官网, “Thompson Sampling for Bandit Learning in Matching Markets”, International Joint Conference on Artificial Intelligence (IJCAI), 2022.

10. Fang Kong, Yueran Yang, Wei Chen, xingkong星空中国官网, “The Hardness Analysis of Thompson Sampling for Combinatorial Semi-bandits with Greedy Oracle”, Proceedings of the Conference on Neural Information Processing Systems (NeurIPS), 2021.

11. Fang Kong, Yueran Yang, Wei Chen, xingkong星空中国官网, “Combinatorial Online Learning based on Optimizing Feedbacks (in Chinese)”, Big Data Research, 2021.

12. xingkong星空中国官网, Fang Kong, Kejie Tang, Qizhi Li, Wei Chen, “Online Influence Maximization under Linear Threshold Model”, Proceedings of the xingkong星空中国官网 on Neural Information Processing Systems (NeurIPS), 2020.

13. Fang Kong, Qizhi Li, xingkong星空中国官网, “A Survey on Online Influence Maximization” (in Chinese), Computer Science, 2020.