@inproceedings{ghaffari2026online,title={Online Learning to Rank under Corruption: A Robust Cascading Bandits Approach},author={Ghaffari, Fatemeh and Sitaraman, Siddarth and Liu, Xutong and Wang, Xuchuang and Hajiesmaili, Mohammad},booktitle={ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},year={2026}}
@inproceedings{wang2026online,title={Online Optimal Probe Allocation for Quantum Network Tomography},author={Wang, Xuchuang and Chen, Yu-Zhen Janice and Guedes de Andrade, Matheus and Hajiesmaili, Mohammad and Lui, John C.S. and He, Ting and Towsley, Don},booktitle={International Conference on Quantum Communications, Networking, and Computing},year={2026}}
@inproceedings{yang2026cooperative,title={Cooperative Bandit Algorithms with Optimal Regret and Communication Costs},author={Lin, Yang and Wang, Xuchuang and Chen, Haoxu and Hajiesmaili, Mohammad and Zhang, Lijun and Lui, John C.S.},booktitle={IEEE/ACM Transactions on Networking},year={2026}}
@inproceedings{liu2026combinatorial,title={Combinatorial Logistic Online Learning and Its Applications in Nonlinear Networked Systems},author={Liu, Xutong and Dai, Xiangxiang and Wang, Xuchuang and Hajiesmaili, Mohammad and Lui, John C.S.},booktitle={IEEE/ACM Transactions on Networking},year={2026}}
@inproceedings{dai2026amulti,title={A Multi-Agent Conversational Bandit Approach to Online Evaluation and Selection of User-Aligned LLM Responses},author={Dai, Xiangxiang and Xie, Yuejin and Liu, Maoli and Wang, Xuchuang and Li, Zhuohua and Wang, Huanyu and Lui, John C.S.},booktitle={The 40th Annual AAAI Conference on Artificial Intelligence (AI Alignment Track)},year={2026}}
@inproceedings{xu2026asynchronous,title={Heterogeneous Multi-Agent Multi-Armed Bandits on Stochastic Block Models},author={Xu, Mengfan and Shan, Liran and Ghaffari, Fatemeh and Wang, Xuchuang and Liu, Xutong and Hajiesmaili, Mohammad},booktitle={ACM International Conference on Measurement and Modeling of Computer Systems},year={2026}}
@inproceedings{zhang2025federated,title={Federated Multi-armed Bandits with Efficient Bit-Level Communications},author={Zhang, Haoran and Yang, Xu and Wang, Xuchuang and Chen, Hao-Xu and Qiu, Hao and Yang, Lin and Gao, Yang},booktitle={Advances in Neural Information Processing Systems},year={2025}}
Fusing Reward and Dueling Feedback in Stochastic Bandits
Xuchuang Wang, Qirun Zeng , Jinhang Zuo , Xutong Liu , Mohammad Hajiesmaili , John C.S. Lui , and Adam Wierman
In Forty-second International Conference on Machine Learning , 2025
Why it matters: Develops bandit algorithms that simultaneously consume absolute reward feedback and pairwise dueling comparisons – a step toward learners that handle the heterogeneous human feedback found in modern RLHF-style applications.
@inproceedings{wang2025fusing,title={Fusing Reward and Dueling Feedback in Stochastic Bandits},author={Wang, Xuchuang and Zeng, Qirun and Zuo, Jinhang and Liu, Xutong and Hajiesmaili, Mohammad and Lui, John C.S. and Wierman, Adam},booktitle={Forty-second International Conference on Machine Learning},year={2025},significance={Develops bandit algorithms that simultaneously consume absolute reward feedback and pairwise dueling comparisons -- a step toward learners that handle the heterogeneous human feedback found in modern RLHF-style applications.}}
@inproceedings{zhang2025optimal,title={Optimal Regret Bounds for Federated Multi-armed Bandits with Fully Distributed Communication},author={Zhang, Haoran and Wang, Xuchuang and Chen, Hao-Xu and Qiu, Hao and Yang, Lin and Gao, Yang},booktitle={The 41th Conference on Uncertainty in Artificial Intelligence},year={2025},}
@inproceedings{ghaffari2025multi,title={Multi-Agent Stochastic Bandits Robust to Adversarial Corruptions},author={Ghaffari, Fatemeh and Wang, Xuchuang and Zuo, Jinhang and Hajiesmaili, Mohammad},booktitle={7th Annual Learning for Dynamics \& Control Conference},year={2025}}
Xuchuang Wang, Maoli Liu , Jinhang Zuo , Xutong Liu , John C.S. Lui , and Mohammad Hajiesmaili
In International Conference on Learning Representations , 2025
Why it matters: Provides regret guarantees for stochastic bandits against a strong adversary that can corrupt rewards after observing the learner’s actions, making bandit learning usable in settings where some feedback is manipulated.
@inproceedings{wang2025robust,title={Stochastic Bandits Robust to Adversarial Attacks},author={Wang, Xuchuang and Liu, Maoli and Zuo, Jinhang and Liu, Xutong and Lui, John C.S. and Hajiesmaili, Mohammad},booktitle={International Conference on Learning Representations},year={2025},significance={Provides regret guarantees for stochastic bandits against a strong adversary that can corrupt rewards after observing the learner's actions, making bandit learning usable in settings where some feedback is manipulated.}}
Asynchronous Multi-Agent Bandits: Fully Distributed vs. Leader-Coordinated Algorithms
Xuchuang Wang*, Yu-Zhen Janice Chen* , Lin Yang , Xutong Liu , Mohammad Hajiesmaili , Don Towsley , and John C.S. Lui
In ACM International Conference on Measurement and Modeling of Computer Systems , 2025
Why it matters: Drops the lock-step assumption that pervades multi-agent bandit analyses and compares fully distributed against leader-coordinated protocols, clarifying the communication-versus-regret tradeoff that governs how cooperating learners should share information.
@inproceedings{wang2025asynchronous,title={Asynchronous Multi-Agent Bandits: Fully Distributed vs. Leader-Coordinated Algorithms},author={Wang*, Xuchuang and Chen*, Yu-Zhen Janice and Yang, Lin and Liu, Xutong and Hajiesmaili, Mohammad and Towsley, Don and Lui, John C.S.},booktitle={ACM International Conference on Measurement and Modeling of Computer Systems},year={2025},significance={Drops the lock-step assumption that pervades multi-agent bandit analyses and compares fully distributed against leader-coordinated protocols, clarifying the communication-versus-regret tradeoff that governs how cooperating learners should share information.}}
@inproceedings{liu2025combinatorial,title={Combinatorial Logistic Bandits},author={Liu, Xutong and Dai, Xiangxiang and Wang, Xuchuang and Hajiesmaili, Mohammad and Lui, John C.S.},booktitle={ACM International Conference on Measurement and Modeling of Computer Systems},year={2025}}
Xuchuang Wang, Yu-Zhen Janice Chen , Matheus Andrade , Jonathan Allcock , Mohammad Hajiesmaili , John C.S. Lui , and Don Towsley
In The 39th Annual AAAI Conference on Artificial Intelligence , 2025
Why it matters: Studies best-arm identification when the learner can issue quantum queries to the reward oracle, quantifying the speedup that quantum-enhanced sequential decision-making offers over classical bandit algorithms.
@inproceedings{wang2025best,title={Best Arm Identification with Quantum Oracles},author={Wang, Xuchuang and Chen, Yu-Zhen Janice and Guedes de Andrade, Matheus and Allcock, Jonathan and Hajiesmaili, Mohammad and Lui, John C.S. and Towsley, Don},booktitle={The 39th Annual AAAI Conference on Artificial Intelligence},year={2025},significance={Studies best-arm identification when the learner can issue quantum queries to the reward oracle, quantifying the speedup that quantum-enhanced sequential decision-making offers over classical bandit algorithms.}}
@inproceedings{mirfakhar2025heterogeneous,title={Heterogeneous Multi-Agent Bandits with Parsimonious Hints},author={Mirfakhar, Amirmahdi and Wang, Xuchuang and Zuo, Jinhang and Zick, Yair and Hajiesmaili, Mohammad},booktitle={The 39th Annual AAAI Conference on Artificial Intelligence},year={2025}}
Xuchuang Wang, Maoli Liu , Xutong Liu , Zhuohua Li , Mohammad Hajiesmaili , John C.S. Lui , and Don Towsley
In Proceedings of the IEEE Conference on Computer Communications , 2025
Why it matters: Casts entanglement path selection in quantum networks as an online learning problem and gives a routing algorithm that adapts to unknown, noisy link fidelities – a building block for routing on the quantum internet.
@inproceedings{wang2025learn,title={Learning Best Paths in Quantum Networks},author={Wang, Xuchuang and Liu, Maoli and Liu, Xutong and Li, Zhuohua and Hajiesmaili, Mohammad and Lui, John C.S. and Towsley, Don},booktitle={Proceedings of the IEEE Conference on Computer Communications},year={2025},significance={Casts entanglement path selection in quantum networks as an online learning problem and gives a routing algorithm that adapts to unknown, noisy link fidelities -- a building block for routing on the quantum internet.}}
@inproceedings{liu2024combinatorial,title={Combinatorial Multivariant Multi-Armed Bandits with Applications to Episodic Reinforcement Learning and Beyond},author={Liu, Xutong and Wang, Siwei and Zuo, Jinhang and Zhong, Han and Wang, Xuchuang and Wang, Zhiyong and Li, Shuai and Hajiesmaili, Mohammad and Lui, John CS and Chen, Wei},booktitle={Forty-first International Conference on Machine Learning},year={2024}}
@inproceedings{liu2024link,title={LinkSelFiE: Link Selection and Fidelity Estimation in Quantum Networks},author={Liu, Maoli and Li, Zhuohua and Wang, Xuchuang and Lui, John C.S.},booktitle={Proceedings of the IEEE Conference on Computer Communications},year={2024}}
@inproceedings{wang2023fidelity,title={Multi-Fidelity Multi-Armed Bandits Revisited},author={Wang, Xuchuang and Wu, Qingyun and Chen, Wei and Lui, John C.S.},booktitle={Advances in Neural Information Processing Systems},year={2023}}
@inproceedings{wang2023analyzing,title={Analyzing Queueing Problems via Bandits with Linear Reward and Nonlinear Workload Fairness},author={Wang, Xuchuang and Xie, Hong and Lui, John C.S.},booktitle={The IEEE Transactions on Mobile Computing},year={2023}}
@article{wang2023optimizing,title={Optimizing Recommendations under Abandonment Risks: Models and Algorithms},author={Wang, Xuchuang and Xie, Hong and Wang, Pinghui and Lui, John C.S.},journal={Performance Evaluation},pages={102351},year={2023},publisher={Elsevier}}
@inproceedings{wang2023explore,title={Exploration for Free: How Does Reward Heterogeneity Improve Regret in Cooperative Multi-agent Bandits?},author={Wang, Xuchuang and Yang, Lin and Chen, Yu-Zhen Janice and Liu, Xutong and Hajiesmaili, Mohammad and Towsley, Don and Lui, John C.S.},booktitle={The 39th Conference on Uncertainty in Artificial Intelligence},year={2023},}
@inproceedings{wang2023achieve,title={Achieve Near-Optimal Individual Regret \& Low Communications in Multi-Agent Bandits},author={Wang, Xuchuang and Yang, Lin and Chen, Yu-Zhen Janice and Liu, Xutong and Hajiesmaili, Mohammad and Towsley, Don and Lui, John C.S.},booktitle={International Conference on Learning Representations},year={2023},}
@inproceedings{chen2023ondemand,title={On-Demand Communication for Asynchronous Multi-Agent Bandits},author={Chen, Yu-Zhen Janice and Yang, Lin and Wang, Xuchuang and Liu, Xutong and Hajiesmaili, Mohammad and Lui, John C.S. and Towsley, Don},booktitle={The 26th International Conference on Artificial Intelligence and Statistics},year={2023}}
@inproceedings{wang2022multi,title={Multi-Player Multi-Armed Bandits with Finite Shareable Resources Arms: Learning Algorithms \& Applications},author={Wang, Xuchuang and Xie, Hong and Lui, John C.S.},booktitle={Proceedings of the 31st International Joint Conference on
Artificial Intelligence, {IJCAI-22}},year={2022}}
@inproceedings{wang2022multiple,title={Multiple-Play Stochastic Bandits with Shareable Finite-Capacity Arms},author={Wang, Xuchuang and Xie, Hong and Lui, John C.S.},booktitle={Proceedings of the 39th International Conference on Machine Learning},pages={23181--23212},year={2022},organization={PMLR}}