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 | 40 | 2020 |
Improved regret bounds for linear adversarial mdps via linear optimization F Kong, X Zhang, B Wang, S Li arXiv preprint arXiv:2302.06834, 2023 | 9 | 2023 |
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 | 8 | 2023 |
Simultaneously learning stochastic and adversarial bandits with general graph feedback F Kong, Y Zhou, S Li International Conference on Machine Learning, 11473-11482, 2022 | 8 | 2022 |
Thompson sampling for bandit learning in matching markets F Kong, J Yin, S Li arXiv preprint arXiv:2204.12048, 2022 | 8 | 2022 |
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 | 4 | 2023 |
Online Influence Maximization under Decreasing Cascade Model F Kong, J Xie, B Wang, T Yao, S Li arXiv preprint arXiv:2305.15428, 2023 | 3 | 2023 |
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 | 2 | 2021 |
Stochastic no-regret learning for general games with variance reduction Y Zhou, F Kong, S Li The Eleventh International Conference on Learning Representations, 2022 | 1 | 2022 |
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 |