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]
Conference Papers
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Theoretical Guarantees for Reinforcement Learning with Human Feedback Uncertainty
Ming Shi, Yingbin Liang, Ness B. Shroff, and Ananthram Swami
submitted to 39th Annual Conference on Neural Information Processing Systems (NeurIPS), 2025.
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Provably Efficient Personalized Multi-Objective Bandits with Proactive Conversational Queries
Linfeng Cao, Ming Shi, and Ness B. Shroff
submitted to 39th Annual Conference on Neural Information Processing Systems (NeurIPS), 2025.
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Minimax Optimal Adversarial Reinforcement Learning
Yudan Wang, Kaiyi Ji, Ming Shi, Shaofeng Zou
submitted to 39th Annual Conference on Neural Information Processing Systems (NeurIPS), 2025.
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Attack Surface as a Dynamical Queueing System: Foundations and Reinforcement Learning for Optimal Defense Posture
Ming Shi and Emre Koksal
under review, 2025.
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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 (ACM MobiHoc) 2025.
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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 (ICML), Vancouver, Canada, July 2025.
Theoretical Hardness and Tractability of POMDPs in RL with Partial Online State Information
Ming Shi, Yingbin Liang, and Ness Shroff
preprint on arXiv, 2023.
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 (ICML), Hawaii, USA, July 2023.
Near-Optimal Adversarial Reinforcement Learning with Switching Costs
Ming Shi, Yingbin Liang, and Ness Shroff
11th International Conference on Learning Representations (ICLR), Kigali, Rwanda, May 2023. (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 (IEEE CIC), virtual conference, December 2022.
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 (ACM MobiHoc), Seoul, South Korea, October 2022. (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 (IEEE INFOCOM), virtual conference, May 2021. (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 (IEEE INFOCOM), Honolulu, Hawaii, USA, April 2018. (Acceptance rate: 19.2%.)
Technical Reports
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