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Fang Kong
Fang Kong
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Online influence maximization under linear threshold model
S Li, F Kong, K Tang, Q Li, W Chen
Advances in neural information processing systems 33, 1192-1204, 2020
392020
Improved regret bounds for linear adversarial mdps via linear optimization
F Kong, X Zhang, B Wang, S Li
arXiv preprint arXiv:2302.06834, 2023
92023
Best-of-three-worlds analysis for linear bandits with follow-the-regularized-leader algorithm
F Kong, C Zhao, S Li
The Thirty Sixth Annual Conference on Learning Theory, 657-673, 2023
82023
Simultaneously learning stochastic and adversarial bandits with general graph feedback
F Kong, Y Zhou, S Li
International Conference on Machine Learning, 11473-11482, 2022
82022
Thompson sampling for bandit learning in matching markets
F Kong, J Yin, S Li
arXiv preprint arXiv:2204.12048, 2022
82022
Online Influence Maximization under Decreasing Cascade Model
F Kong, J Xie, B Wang, T Yao, S Li
arXiv preprint arXiv:2305.15428, 2023
32023
Player-optimal Stable Regret for Bandit Learning in Matching Markets
F Kong, S Li
Proceedings of the 2023 Annual ACM-SIAM Symposium on Discrete Algorithms …, 2023
22023
The hardness analysis of thompson sampling for combinatorial semi-bandits with greedy oracle
F Kong, Y Yang, W Chen, S Li
Advances in Neural Information Processing Systems 34, 26701-26713, 2021
22021
Stochastic no-regret learning for general games with variance reduction
Y Zhou, F Kong, S Li
The Eleventh International Conference on Learning Representations, 2022
12022
Which LLM to Play? Convergence-Aware Online Model Selection with Time-Increasing Bandits
Y Xia, F Kong, T Yu, L Guo, RA Rossi, S Kim, S Li
arXiv preprint arXiv:2403.07213, 2024
2024
Improved Bandits in Many-to-one Matching Markets with Incentive Compatibility
F Kong, S Li
arXiv preprint arXiv:2401.01528, 2024
2024
Simultaneously Learning Stochastic and Adversarial Markov Decision Process with Linear Function Approximation
F Kong, XC Zhang, B Wang, S Li
2022
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