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Fang Kong 0002

孔芳

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unicode name: 孔芳
affiliation: Shanghai Jiao Tong University, China

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2020 – today

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2023
[c6]
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  dblp key:
  - conf/iclr/Zhou0023
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  - https://dblp.org/rec/conf/iclr/Zhou0023
Yichi Zhou, Fang Kong, Shuai Li:
Stochastic No-regret Learning for General Games with Variance Reduction. ICLR 2023
[c5]
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  - conf/soda/0002023
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  - https://dblp.org/rec/conf/soda/0002023
Fang Kong, Shuai Li:
Player-optimal Stable Regret for Bandit Learning in Matching Markets. SODA 2023: 1512-1522
[i5]
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  - journals/corr/abs-2307-10890
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Fang Kong, Shuai Li:
Player-optimal Stable Regret for Bandit Learning in Matching Markets. CoRR abs/2307.10890 (2023)
2022
[c4]
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  - conf/icml/0002ZL22
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Fang Kong, Yichi Zhou, Shuai Li:
Simultaneously Learning Stochastic and Adversarial Bandits with General Graph Feedback. ICML 2022: 11473-11482
[c3]
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  - conf/ijcai/0002YL22
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Fang Kong, Junming Yin, Shuai Li:
Thompson Sampling for Bandit Learning in Matching Markets. IJCAI 2022: 3164-3170
[i4]
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  - journals/corr/abs-2204-12048
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Fang Kong, Junming Yin, Shuai Li:
Thompson Sampling for Bandit Learning in Matching Markets. CoRR abs/2204.12048 (2022)
[i3]
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  - journals/corr/abs-2206-07908
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Fang Kong, Yichi Zhou, Shuai Li:
Simultaneously Learning Stochastic and Adversarial Bandits with General Graph Feedback. CoRR abs/2206.07908 (2022)
2021
[c2]
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  - electronic edition @ neurips.cc (open access)
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  - conf/nips/KongYCL21
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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]
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  - journals/corr/abs-2111-04295
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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]
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  - conf/nips/LiKTLC20
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Shuai Li, Fang Kong, Kejie Tang, Qizhi Li, Wei Chen:
Online Influence Maximization under Linear Threshold Model. NeurIPS 2020
[i1]
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  dblp key:
  - journals/corr/abs-2011-06378
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Shuai Li, Fang Kong, Kejie Tang, Qizhi Li, Wei Chen:
Online Influence Maximization under Linear Threshold Model. CoRR abs/2011.06378 (2020)

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