Publications
(*equal contribution, #corresponding author)
Preprints
[arXiv] Multi-Agent Conversational Online Learning for Adaptive LLM Response Identification
Xiangxiang Dai, Yuejin Xie, Maoli Liu, Xuchuang Wang, Zhuohua Li, Huanyu Wang, John C.S. Lui.[arXiv] Cost-Effective Online Multi-LLM Selection with Versatile Reward Models
Xiangxiang Dai, Jin Li, Xutong Liu, Anqi Yu, John C.S. Lui.
Conferences:
[SIGKDD] A Unified Online-Offline Framework for Co-Branding Campaign Recommendations
Xiangxiang Dai, Xiaowei Sun, Jinhang Zuo, Xutong Liu, John C.S. Lui.
Accepted in the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2025. (AR: 365/1988=18.4%)
[arXiv], [Code][SIGKDD] Leveraging the Power of Conversations: Optimal Key Term Selection in Conversational Contextual Bandits
Maoli Liu, Zhuohua Li, Xiangxiang Dai, John C.S. Lui.
Accepted in the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2025. (AR: 365/1988=18.4%)
[arXiv][ICML] Offline Learning for Combinatorial Multi-armed Bandits
Xutong Liu, Xiangxiang Dai#, Jinhang Zuo#, Siwei Wang, Carlee-Joe Wong, John C.S. Lui, Wei Chen.
Accepted in the International Conference on Machine Learning (ICML), 2025. (AR: 3260/12107=26.3%).
[arXiv][ICLR] Demystifying Online Clustering of Bandits: Enhanced Exploration Under Stochastic and Smoothed Adversarial Contexts
Zhuohua Li, Maoli Liu, Xiangxiang Dai, John C.S. Lui.
Accepted in the International Conference on Learning Representations (ICLR), 2025. (AR: 3706/11672=32.08%)
[OpenReview], [Poster], [arXiv][WWW] Towards Efficient Conversational Recommendations: Expected Value of Information Meets Bandit Learning
Zhuohua Li, Maoli Liu, Xiangxiang Dai, John C.S. Lui.
Accepted in the ACM The Web Conference (WWW), 2025. (AR: 409/2062=19.8%)
[Link], [Poster][SIGMETRICS] Combinatorial Logistic Bandits
Xutong Liu, Xiangxiang Dai, Xuchuang Wang, Mohammad Hajiesmaili, John C.S. Lui.
Accepted in the ACM International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS), 2025. (AR: 35/223 = 15.7%)
Best Paper Finalists (Top 5)
[arXiv], [Code][ACM MM] AxiomVision: Accuracy-Guaranteed Adaptive Visual Model Selection for Perspective-Aware Video Analytics
Xiangxiang Dai, Zeyu Zhang, Peng Yang, Yuedong Xu, Xutong Liu, John C.S. Lui.
Accepted in the ACM Multimedia (MM), 2024. (AR: 1149/4385 = 26.2%)
[Link], [arXiv], [Poster], [Code], [ACM showcase on Kudos][IWQoS] Quantifying the Merits of Network-Assist Online Learning in Optimizing Network Protocols
Xiangxiang Dai*, Zhiyong Wang*, Jiancheng Ye, and John C.S. Lui.
Accepted in the IEEE/ACM International Symposium on Quality of Service (IWQoS), 2024. (AR: 81/326 = 24.8%)
[Link], [PDF], [Slides]
Journals:
[T-ITS] Enhancing Cooperative LiDAR-based Perception Accuracy in Vehicular Edge Networks
Jiawei Hou, Peng Yang, Xiangxiang Dai, Tian Qin, Feng Lyu.
Accepted in the IEEE Transactions on Intelligent Transportation Systems (T-ITS), 2025.
[Link][TON] Variance-Aware Bandit Framework for Dynamic Probabilistic Maximum Coverage Problem with Triggered or Self-Reliant Arms
Xiangxiang Dai, Xutong Liu, Jinhang Zuo, Hong Xie, Carlee Joe-Wong, John C.S. Lui.
Accepted in the IEEE/ACM Transactions on Networking (TON), 2025.
[Link][TKDE] Conversational Recommendation with Online Learning and Clustering on Misspecified Users
Xiangxiang Dai*, Zhiyong Wang*, Jize Xie, Xutong Liu, John C.S. Lui.
Accepted in the IEEE Transactions on Knowledge and Data Engineering (TKDE), 2024.
[Link][TKDE] Online Learning and Detecting Corrupted Users for Conversational Recommendation Systems
Xiangxiang Dai*, Zhiyong Wang*, Jize Xie, Tong Yu, and John C.S. Lui.
Accepted in the IEEE Transactions on Knowledge and Data Engineering (TKDE), 2024.
[Link][TII] RESPIRE: Reducing Spatial–Temporal Redundancy for Efficient Edge-Based Industrial Video Analytics
Xiangxiang Dai, Peng Yang, Xinyu Zhang, Zhewei Dai, and Li Yu.
Accepted in the IEEE Transactions on Industrial Informatics (TII), 2022.
[Link]