Publications

2024

(#corresponding author, *equal contribution)

• [AAAI’24] Federated Contextual Cascading Bandits with Asynchronous Communication and Heterogeneous Users
  Hantao Yang, Xutong Liu#, Zhiyong Wang, Hong Xie, John C.S. Lui, Defu Lian, and Enhong Chen.
  To appear in the AAAI Conference on Artificial Intelligence (AAAI), 2024. (2342/9862=23.75%).
  

• [INFOCOM’24] Learning Context-Aware Probabilistic Maximum Coverage Bandits: A Variance-Adaptive Approach
  Xutong Liu, Jinhang Zuo, Junkai Wang, Zhiyong Wang, Yuedong Xu, and John C.S. Lui.
  To appear in the IEEE International Conference on Computer Communications (INFOCOM), 2024. (256/1307=19.6%).
  

2023

• [NeurIPS’23] Online Clustering of Bandits with Misspecified User Models
  Zhiyong Wang, Jize Xie, Xutong Liu, Shuai Li, John C.S. Lui.
  To appear in Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS), 2023. (3222/12343=26.1%).
  

• [UAI’23] Exploration for Free: How Does Reward Heterogeneity Improve Regret in Cooperative Multi-agent Bandits?
  Xuchuang Wang, Lin Yang, Yu-zhen Janice Chen, Xutong Liu, Mohammad Hajiesmaili, Don Towsley, John C.S. Lui.
  The 39th Conference on Uncertainty in Artificial Intelligence (UAI), 2023.
  

• [ICML’23] Contextual Combinatorial Bandits with Probabilistically Triggered Arms
  Xutong Liu, Jinhang Zuo, Siwei Wang, John C.S. Lui, Mohammad Hajiesmaili, Adam Wierman, Wei Chen.
  The 40th International Conference on Machine Learning (ICML), 2023. (1827/6538=27.9%).
  [arXiv] [slides]

• [AISTATS’23] On-Demand Communication for Asynchronous Multi-Agent Bandits
  Yu-Zhen Janice Chen, Lin Yang, Xuchuang Wang, Xutong Liu, Mohammad Hajiesmaili, John C.S. Lui, Don Towsley.
  The 26th International Conference on Artificial Intelligence and Statistics (AISTATS), 2023.
  

• [ICLR’23] Achieve Near-Optimal Individual Regret and Low Communications in Multi-Agent Bandits
  Xuchuang Wang, Lin Yang, Yu-Zhen Janice Chen, Xutong Liu, Mohammad Hajiesmaili, Don Towsley, John C.S. Lui.
  The 11th International Conference on Learning Representations (ICLR), 2023.
  

• [INFOCOM’23] Variance-Adaptive Algorithm for Probabilistic Maximum Coverage Bandits with General Feedback
  Xutong Liu*, Jinhang Zuo*, Hong Xie, Carlee Joe-Wong, John C.S. Lui.
  IEEE International Conference on Computer Communications (INFOCOM), 2023. (252/1312=19.2%).
  [pdf] [slides]

• [AAAI’23] Efficient Explorative Key-term Selection Strategies for Conversational Contextual Bandits
  Zhiyong Wang, Xutong Liu, Shuai Li, John C.S. Lui.
  Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI), 2023. (1721/8777=19.6%).
  [slides] [poster]

2022

• [NeurIPS’22] Batch-Size Independent Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms or Independent Arms
  Xutong Liu, Jinhang Zuo, Siwei Wang, Carlee Joe-Wong, John C.S. Lui, Wei Chen.
  Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS), 2022. (2665/10411=25.6%).
  [arXiv] [paper] [slides] [poster]

• [UAI’22] Federated Online Clustering of Bandits
  Xutong Liu, Haoru Zhao, Tong Yu, Shuai Li, John C.S. Lui.
  The 38th Conference on Uncertainty in Artificial Intelligence (UAI), 2022. (230/712=32%).
  [paper] [arXiv] [slides] [poster] [code]

• [AISTATS’22] Online Competitive Influence Maximization
  Jinhang Zuo, Xutong Liu, Carlee Joe-Wong, John C.S. Lui, Wei Chen.
  The 25th International Conference on Artificial Intelligence and Statistics (AISTATS 2022), (492/1685=29%)
  [arXiv]

• [IEEE TMC] Learning with Guarantee via Constrained Multi-armed Bandit: Theory and Network Applications
  Kechao Cai, Xutong Liu, Yuzhen Janice Chen, and John C.S. Lui.
  IEEE Transactions on Mobile Computing (IEEE TMC), 2022.
  DOI: https://doi.org/10.1109/TMC.2022.3173792

2021 and before

• [ICML’21, Long Oral] Multi-layered Network Exploration via Random Walks: From Offline Optimization to Online Learning
  Xutong Liu, Jinhang Zuo, Xiaowei Chen, Wei Chen, John C.S. Lui.
  The 38th International Conference on Machine Learning (ICML), Long Oral, 2021. (166/5513=3%).
  [paper] [arXiv] [slides] [poster] [video]

• [NSJ] Learning to Count: a Deep Learning Framework for Graphlet Count Estimation
  Xutong Liu, Yu-Zhen Chen, John C.S. Lui., Konstantin Avrachenkov.
  Network Science Journal.
  DOI: https://doi.org/10.1017/nws.2020.35.

• [INFOCOM’18] An Online Learning Approach to Network Application Optimization with Guarantee
  Kechao Cai, Xutong Liu, Yuzhen Janice Chen, and John C.S. Lui.
  IEEE International Conference on Computer Communications (INFOCOM), 2018.

• [CNA’18] Graphlet Count Estimation via Convolutional Neural Networks
  Xutong Liu, Yu-Zhen Chen, John C.S. Lui, Konstantin Avrachenkov.
  COMPLEX NETWORKS, 2018.
  [PDF]