研究領(lǐng)域
在線學習，強化學習，機器學習
教育經(jīng)歷
2020.9-2024.6 上海交通大學，計算機科學與技術(shù)，工學博士
2016.9-2020.6 山東大學，軟件工程，工學學士
科研經(jīng)歷
2023.2-2023.8 香港中文大學，科研助理
2022.7-2024.7 騰訊WXG，研究型實習生
2021.12-2022.5 微軟亞洲研究院，研究型實習生
2021.6-2021.8 阿里巴巴達摩院，研究型實習生
學術(shù)成果
Yu Xia*, Fang Kong*, Tong Yu, Liya Guo, Ryan A. Rossi, Sungchul Kim, Shuai Li, “Convergence-Aware Online Model Selection with Time-Increasing Bandits”, The Web Conference (WWW), 2024.
Fang Kong, Shuai Li, “Improved Bandits in Many-to-one Matching Markets with Incentive Compatibility”, Proceedings of the 38th Annual AAAI Conference on Artificial Intelligence (AAAI), 2024.
Fang Kong*, Xiangcheng Zhang*, Baoxiang Wang, Shuai Li, “Improved Regret Bounds for Linear Adversarial MDPs via Linear Optimization”, Transactions on Machine Learning Research (TMLR), 2024.
Fang Kong, Canzhe Zhao, Shuai Li, “Best-of-three-worlds Analysis for Linear Bandits with Follow-the-regularized-leader Algorithm”, Proceedings of the 36th Conference on Learning Theory (COLT), 2023.
Fang Kong, Jize Xie, Baoxiang Wang, Tao Yao, Shuai Li. “Online Influence Maximization under Decreasing Cascade Model”, Proceedings of the 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2023.
Yichi Zhou, Fang Kong, Shuai Li, “Stochastic No-Regret Learning for General Games with Variance Reduction”, International Conference on Learning Representations (ICLR), 2023.
Fang Kong, Shuai Li, “Player-optimal Stable Regret for Bandit Learning in Matching Markets”, Proceedings of the 2023 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA). 2023.
Fang Kong, Yichi Zhou, Shuai Li, “Simultaneously Learning Stochastic and Adversarial Bandits with General Graph Feedback”, International Conference on Machine Learning (ICML), 2022.
Fang Kong, Junming Yin, Shuai Li, “Thompson Sampling for Bandit Learning in Matching Markets”, International Joint Conference on Artificial Intelligence (IJCAI), 2022.
Fang Kong, Yueran Yang, Wei Chen, Shuai Li, “The Hardness Analysis of Thompson Sampling for Combinatorial Semi-bandits with Greedy Oracle”, Proceedings of the Conference on Neural Information Processing Systems (NeurIPS), 2021.
Fang Kong, Yueran Yang, Wei Chen, Shuai Li, “Combinatorial Online Learning based on Optimizing Feedbacks (in Chinese)”, Big Data Research, 2021.
Shuai Li, Fang Kong, Kejie Tang, Qizhi Li, Wei Chen, “Online Influence Maximization under Linear Threshold Model”, Proceedings of the Conference on Neural Information Processing Systems (NeurIPS), 2020.
Fang Kong, Qizhi Li, Shuai Li, “A Survey on Online Influence Maximization” (in Chinese), Computer Science, 2020.
