Ming Shi

21 papers A* 6B 2Journal 11Unranked 2
YearRankTypeTitle / Venue / Authors
2026 A* conf
Provably Efficient Multi-Objective Bandit Algorithms Under Preference-Centric Customization.
AAAI
Linfeng Cao, Ming Shi, Ness B. Shroff
2026 J jnl
Regret Bounds for Reinforcement Learning from Multi-Source Imperfect Preferences.
CoRR
Ming Shi, Yingbin Liang, Ness B. Shroff, Ananthram Swami
2025 B conf
Online Learning for Optimizing AoI-Energy Tradeoff under Unknown Channel Statistics.
MobiHoc
Mohamed A. Abd-Elmagid, Ming Shi, Eylem Ekici, Ness B. Shroff
2025 J jnl
Online Learning for Optimizing AoI-Energy Tradeoff under Unknown Channel Statistics.
CoRR
Mohamed A. Abd-Elmagid, Ming Shi, Eylem Ekici, Ness B. Shroff
2025 J jnl
Power-of-2-Arms for Adversarial Bandit Learning With Switching Costs.
IEEE Trans. Netw.
Ming Shi, Xiaojun Lin, Lei Jiao
2025 J jnl
Provably Efficient Multi-Objective Bandit Algorithms under Preference-Centric Customization.
CoRR
Linfeng Cao, Ming Shi, Ness B. Shroff
2025 A* conf
Provably Efficient RL for Linear MDPs under Instantaneous Safety Constraints in Non-Convex Feature Spaces.
ICML
Amirhossein Roknilamouki, Arnob Ghosh, Ming Shi, Fatemeh Nourzad, Eylem Ekici, Ness B. Shroff
2025 J jnl
Provably Efficient RL for Linear MDPs under Instantaneous Safety Constraints in Non-Convex Feature Spaces.
CoRR
Amirhossein Roknilamouki, Arnob Ghosh, Ming Shi, Fatemeh Nourzad, Eylem Ekici, Ness B. Shroff
2024 J jnl
Combining Regularization With Look-Ahead for Competitive Online Convex Optimization.
IEEE/ACM Trans. Netw.
Ming Shi, Xiaojun Lin, Lei Jiao
2024 — conf
Designing Near-Optimal Partially Observable Reinforcement Learning.
MILCOM
Ming Shi, Yingbin Liang, Ness B. Shroff
2023 A* conf
A Near-Optimal Algorithm for Safe Reinforcement Learning Under Instantaneous Hard Constraints.
ICML
Ming Shi, Yingbin Liang, Ness B. Shroff
2023 J jnl
A Near-Optimal Algorithm for Safe Reinforcement Learning Under Instantaneous Hard Constraints.
CoRR
Ming Shi, Yingbin Liang, Ness B. Shroff
2023 A* conf
Near-Optimal Adversarial Reinforcement Learning with Switching Costs.
ICLR
Ming Shi, Yingbin Liang, Ness B. Shroff
2023 J jnl
Near-Optimal Adversarial Reinforcement Learning with Switching Costs.
CoRR
Ming Shi, Yingbin Liang, Ness B. Shroff
2023 J jnl
Theoretical Hardness and Tractability of POMDPs in RL with Partial Hindsight State Information.
CoRR
Ming Shi, Yingbin Liang, Ness B. Shroff
2022 — conf
Leveraging Synergies Between AI and Networking to Build Next Generation Edge Networks.
CIC
Sen Lin, Ming Shi, Anish Arora, Raef Bassily, Elisa Bertino, Constantine Caramanis, Kaushik R. Chowdhury, Eylem Ekici, Atilla Eryilmaz, Stratis Ioannidis, Nan Jiang, Gauri Joshi, Jim Kurose, Yingbin Liang, Zhiqiang Lin, Jia Liu, Mingyan Liu, Tommaso Melodia, Aryan Mokhtari, Rob Nowak, Sewoong Oh, Srini Parthasarathy, Chunyi Peng, Hulya Seferoglu, Ness B. Shroff, Sanjay Shakkottai, Kannan Srinivasan, Ameet Talwalkar, Aylin Yener, Lei Ying
2022 B conf
Power-of-2-arms for bandit learning with switching costs.
MobiHoc
Ming Shi, Xiaojun Lin, Lei Jiao
2021 A* conf
Combining Regularization with Look-Ahead for Competitive Online Convex Optimization.
INFOCOM
Ming Shi, Xiaojun Lin, Lei Jiao
2021 J jnl
Competitive Online Convex Optimization With Switching Costs and Ramp Constraints.
IEEE/ACM Trans. Netw.
Ming Shi, Xiaojun Lin, Sonia Fahmy
2019 J jnl
On the Value of Look-Ahead in Competitive Online Convex Optimization.
Proc. ACM Meas. Anal. Comput. Syst.
Ming Shi, Xiaojun Lin, Lei Jiao
2018 A* conf
Competitive Online Convex Optimization with Switching Costs and Ramp Constraints.
INFOCOM
Ming Shi, Xiaojun Lin, Sonia Fahmy, Dong-Hoon Shin