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ORCIDFang Kong 0002
Person information
- unicode name: 孔芳
- affiliation: Southern University of Science and Technology, Shenzhen, China
- affiliation (former): Shanghai Jiao Tong University, China
Other persons with the same name
- Fang Kong — disambiguation page
- Fang Kong 0001
![[0000-0002-7102-0143]](https://dblp.org/img/orcid-mark.12x12.png "0000-0002-7102-0143")
— Soochow University, Institute of Artificial Intelligence, China
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2020 – today
- 2025
[c13]Fang Kong, Jingqi Tang, Mingzhu Li, Pinyan Lu, John C. S. Lui, Shuai Li:
Bandit Learning in Matching Markets with Indifference. ICLR 2025- [i11]Manhin Poon, Xiangxiang Dai, Xutong Liu, Fang Kong, John C. S. Lui, Jinhang Zuo:
Online Multi-LLM Selection via Contextual Bandits under Unstructured Context Evolution. CoRR abs/2506.17670 (2025) - 2024
- [j1]Fang Kong, Xiangcheng Zhang, Baoxiang Wang, Shuai Li:
Improved Regret Bounds for Linear Adversarial MDPs via Linear Optimization. Trans. Mach. Learn. Res. 2024 (2024) - [c12]Fang Kong, Shuai Li:
Improved Bandits in Many-to-One Matching Markets with Incentive Compatibility. AAAI 2024: 13256-13264 - [c11]Fang Kong, Penglei Zhao, Shichao Han, Yong Wang, Shuai Li:
Sequential Optimum Test with Multi-armed Bandits for Online Experimentation. CIKM 2024: 4645-4652 - [c10]Fang Kong, Zilong Wang, Shuai Li:
Improved Analysis for Bandit Learning in Matching Markets. NeurIPS 2024 - [c9]Yu Xia, Fang Kong, Tong Yu, Liya Guo, Ryan A. Rossi, Sungchul Kim, Shuai Li:
Which LLM to Play? Convergence-Aware Online Model Selection with Time-Increasing Bandits. WWW 2024: 4059-4070 - [i10]Fang Kong, Shuai Li:
Improved Bandits in Many-to-one Matching Markets with Incentive Compatibility. CoRR abs/2401.01528 (2024) - [i9]Yu Xia, Fang Kong, Tong Yu, Liya Guo, Ryan A. Rossi, Sungchul Kim, Shuai Li:
Which LLM to Play? Convergence-Aware Online Model Selection with Time-Increasing Bandits. CoRR abs/2403.07213 (2024) - 2023
- [c8]Fang Kong, Jize Xie, Baoxiang Wang, Tao Yao, Shuai Li:
Online Influence Maximization under Decreasing Cascade Model. AAMAS 2023: 2197-2204 - [c7]Fang Kong, Canzhe Zhao, Shuai Li:
Best-of-three-worlds Analysis for Linear Bandits with Follow-the-regularized-leader Algorithm. COLT 2023: 657-673 - [c6]Yichi Zhou, Fang Kong, Shuai Li:
Stochastic No-regret Learning for General Games with Variance Reduction. ICLR 2023 - [c5]Fang Kong, Shuai Li:
Player-optimal Stable Regret for Bandit Learning in Matching Markets. SODA 2023: 1512-1522 - [i8]Fang Kong, Xiangcheng Zhang, Baoxiang Wang, Shuai Li:
Improved Regret Bounds for Linear Adversarial MDPs via Linear Optimization. CoRR abs/2302.06834 (2023) - [i7]Fang Kong, Canzhe Zhao, Shuai Li:
Best-of-three-worlds Analysis for Linear Bandits with Follow-the-regularized-leader Algorithm. CoRR abs/2303.06825 (2023) - [i6]Fang Kong, Jize Xie, Baoxiang Wang, Tao Yao, Shuai Li:
Online Influence Maximization under Decreasing Cascade Model. CoRR abs/2305.15428 (2023) - [i5]Fang Kong, Shuai Li:
Player-optimal Stable Regret for Bandit Learning in Matching Markets. CoRR abs/2307.10890 (2023) - 2022
- [c4]Fang Kong, Yichi Zhou, Shuai Li:
Simultaneously Learning Stochastic and Adversarial Bandits with General Graph Feedback. ICML 2022: 11473-11482 - [c3]Fang Kong, Junming Yin, Shuai Li:
Thompson Sampling for Bandit Learning in Matching Markets. IJCAI 2022: 3164-3170 - [i4]Fang Kong, Junming Yin, Shuai Li:
Thompson Sampling for Bandit Learning in Matching Markets. CoRR abs/2204.12048 (2022) - [i3]Fang Kong, Yichi Zhou, Shuai Li:
Simultaneously Learning Stochastic and Adversarial Bandits with General Graph Feedback. CoRR abs/2206.07908 (2022) - 2021
- [c2]Fang Kong, Yueran Yang, Wei Chen, Shuai Li:
The Hardness Analysis of Thompson Sampling for Combinatorial Semi-bandits with Greedy Oracle. NeurIPS 2021: 26701-26713 - [i2]Fang Kong, Yueran Yang, Wei Chen, Shuai Li:
The Hardness Analysis of Thompson Sampling for Combinatorial Semi-bandits with Greedy Oracle. CoRR abs/2111.04295 (2021) - 2020
- [c1]Shuai Li, Fang Kong, Kejie Tang, Qizhi Li, Wei Chen:
Online Influence Maximization under Linear Threshold Model. NeurIPS 2020 - [i1]Shuai Li, Fang Kong, Kejie Tang, Qizhi Li, Wei Chen:
Online Influence Maximization under Linear Threshold Model. CoRR abs/2011.06378 (2020)
Coauthor Index
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