Dissertation
Book Chapter
Journal Papers
Power-of-2-Arms for Adversarial Bandit Learning with Switching Costs
Ming Shi, Xiaojun Lin, and Lei Jiao
IEEE/ACM Transactions on Networking, vol. 33, no. 3, pp. 1112-1127, June 2025, DOI: 10.1109/TON.2024.3522073. [IEEE/ACM ToN]
Combining Regularization With Look-Ahead for Competitive Online Convex Optimization
Ming Shi, Xiaojun Lin, and Lei Jiao
IEEE/ACM Transactions on Networking, vol. 32, no. 3, pp. 2391-2405, June 2024, DOI: 10.1109/TNET.2024.3350990. [IEEE/ACM ToN]
Competitive Online Convex Optimization with Switching Costs and Ramp Constraints
Ming Shi, Xiaojun Lin, and Sonia Fahmy
IEEE/ACM Transactions on Networking, vol. 29, no. 2, pp. 876-889, April 2021, DOI: 10.1109/TNET.2021.3053910. [IEEE/ACM ToN]
Near-Optimal Partially Observable Reinforcement Learning with Partial Online State Information
Ming Shi, Yingbin Liang, and Ness B. Shroff
submitted to IEEE Transactions on Information Theory, 2025. [IEEE TIT]
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Theoretical Guarantees for Reinforcement Learning with Noisy Multi-Human Feedback
Ming Shi, Yingbin Liang, Ness B. Shroff, and Ananthram Swami
submitted to Journal of Machine Learning Research, January 2026. [JMLR]
Conference Papers
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Bi-Level Online Provisioning and Scheduling with Switching Costs and Cross-Level Constraints
*Jialei Liu*, C. Emre Koksal, Ming Shi
submitted to 24th International Symposium on Modeling and Optimization in Mobile, Ad hoc, and Wireless Networks. [WiOPT]
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Minimax Optimal Adversarial Reinforcement Learning
Yudan Wang, Kaiyi Ji, Ming Shi, Shaofeng Zou
14th International Conference on Learning Representations, Rio de Janeiro, Brazil, April 2026. [ICLR] (Acceptance rate: 28%.)
Communication–Corruption Coupling and Verification in Cooperative Multi-Objective Bandits
Ming Shi
submitted to IEEE International Symposium on Information Theory, January 2026. [IEEE ISIT]
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Mixture-of-Experts Actor-Critic for Regime-Switching MDPs: Impossibility Results and Performance Guarantees
Ming Shi
submitted to ACM SIGMETRICS, January 2026. [ACM SIGMETRICS]
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A Queueing-Theoretic Framework for Dynamic Attack Surfaces: Data-Integrated Risk Analysis and Adaptive Defense
Jihyeon Yun, Abdullah Etcibasi, Ming Shi, and C. Emre Koksal
submitted to ACM SIGMETRICS, January 2026. [ACM SIGMETRICS]
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Provably Efficient Multi-Objective Bandit Algorithms under Preference-Centric Customization
Linfeng Cao, Ming Shi, and Ness B. Shroff
The 40th Annual AAAI Conference on Artificial Intelligence, Singapore, January 2026. [AAAI] (Acceptance rate: 17.6%.)
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Provably Efficient Personalized Multi-Objective Bandits with Proactive Conversational Queries
Linfeng Cao, Ming Shi, and Ness B. Shroff
submitted to The International World Wide Web Conference, September 2025. [ACM WWW]
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Online Learning for Optimizing AoI-Energy Tradeoff under Unknown Channel Statistics
Mohamed A. Abd-Elmagid, Ming Shi, Eylem Ekici, and Ness B. Shroff
26rd International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing, October 2025. [ACM MobiHoc] (Acceptance rate: 23%.)
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Provably Efficient Reinforcement Learning for Linear MDPs under Instantaneous Safety Constraints in Non-Convex Feature Spaces
Amirhossein Roknilamouki, Arnob Ghosh, Ming Shi, Fatemeh Nourzad, Eylem Ekici, and Ness B. Shroff
42th International Conference on Machine Learning, Vancouver, Canada, July 2025. [ICML] (Acceptance rate: 26.9%.)
Designing Near-Optimal Partially Observable Reinforcement Learning
Ming Shi, Yingbin Liang, and Ness Shroff
IEEE Military Communications Conference, Washington DC, October 2024. [IEEE MILCOM]
A Near-Optimal Algorithm for Safe Reinforcement Learning Under Instantaneous Hard Constraints
Ming Shi, Yingbin Liang, and Ness Shroff
40th International Conference on Machine Learning, Hawaii, USA, July 2023. [ICML] (Acceptance rate: 27.96%.)
Near-Optimal Adversarial Reinforcement Learning with Switching Costs
Ming Shi, Yingbin Liang, and Ness Shroff
11th International Conference on Learning Representations, Kigali, Rwanda, May 2023. [ICLR] (Spotlight, with acceptance rate 8.0%.)
Leveraging Synergies Between AI and Networking to Build Next Generation Edge Networks
Ming Shi, Sen Lin (co-first author), Yingbin Liang, Ness Shroff, et al.
8th IEEE International Conference on Collaboration and Internet Computing, virtual conference, December 2022. [IEEE CIC]
Power-of-2-Arms for Bandit Learning with Switching Costs
Ming Shi, Xiaojun Lin, and Lei Jiao
23rd International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing, Seoul, South Korea, October 2022. [ACM MobiHoc] (Acceptance rate: 19.8%.)
Combining Regularization with Look-Ahead for Competitive Online Convex Optimization
Ming Shi, Xiaojun Lin, and Lei Jiao
IEEE Conference on Computer Communications, virtual conference, May 2021. [IEEE INFOCOM] (Acceptance rate: 19.9%.)
Competitive Online Convex Optimization with Switching Costs and Ramp Constraints
Ming Shi, Xiaojun Lin, Sonia Fahmy, and DongHoon Shin
IEEE Conference on Computer Communications, Honolulu, Hawaii, USA, April 2018. [IEEE INFOCOM] (Acceptance rate: 19.2%.)
Technical Reports
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