Fang Kong

According to our database, Fang Kong authored at least 17 papers between 2020 and 2026.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of four.

2026

Best Arm Identification in Generalized Linear Bandits via Hybrid Feedback.

Qirun Zeng, Xuchuang Wang, Jiayi Shen, Xutong Liu, Fang Kong, Jinhang Zuo

CoRR, May, 2026

Bandit learning in matching markets with relative feedback.

Fang Kong, Xiaoxi Zhang, Xiao Huang, Lijun Zhang, Shuai Li

Theor. Comput. Sci., 2026

Online Multi-LLM Selection via Contextual Bandits Under Unstructured Context Evolution.

Manhin Poon, Xiangxiang Dai, Xutong Liu, Fang Kong, John C. S. Lui, Jinhang Zuo

Proceedings of the Fortieth AAAI Conference on Artificial Intelligence, 2026

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 Thirteenth International Conference on Learning Representations, 2025

2024

Improved Regret Bounds for Linear Adversarial MDPs via Linear Optimization.

Fang Kong, Xiangcheng Zhang, Baoxiang Wang, Shuai Li

Trans. Mach. Learn. Res., 2024

Which LLM to Play? Convergence-Aware Online Model Selection with Time-Increasing Bandits.

Yu Xia, Fang Kong, Tong Yu, Liya Guo, Ryan A. Rossi, Sungchul Kim, Shuai Li

Proceedings of the ACM on Web Conference 2024, 2024

Improved Analysis for Bandit Learning in Matching Markets.

Fang Kong, Zilong Wang, Shuai Li

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

Sequential Optimum Test with Multi-armed Bandits for Online Experimentation.

Fang Kong, Penglei Zhao, Shichao Han, Yong Wang, Shuai Li

Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, 2024

Improved Bandits in Many-to-One Matching Markets with Incentive Compatibility.

Fang Kong, Shuai Li

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Player-optimal Stable Regret for Bandit Learning in Matching Markets.

Fang Kong, Shuai Li

Proceedings of the 2023 ACM-SIAM Symposium on Discrete Algorithms, 2023

Stochastic No-regret Learning for General Games with Variance Reduction.

Yichi Zhou, Fang Kong, Shuai Li

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Best-of-three-worlds Analysis for Linear Bandits with Follow-the-regularized-leader Algorithm.

Fang Kong, Canzhe Zhao, Shuai Li

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

Online Influence Maximization under Decreasing Cascade Model.

Fang Kong, Jize Xie, Baoxiang Wang, Tao Yao, Shuai Li

Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, 2023

2022

Thompson Sampling for Bandit Learning in Matching Markets.

Fang Kong, Junming Yin, Shuai Li

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

Simultaneously Learning Stochastic and Adversarial Bandits with General Graph Feedback.

Fang Kong, Yichi Zhou, Shuai Li

Proceedings of the International Conference on Machine Learning, 2022

2021

The Hardness Analysis of Thompson Sampling for Combinatorial Semi-bandits with Greedy Oracle.

Fang Kong, Yueran Yang, Wei Chen, Shuai Li

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Online Influence Maximization under Linear Threshold Model.

Shuai Li, Fang Kong, Kejie Tang, Qizhi Li, Wei Chen

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020