Competitive Online Convex Optimization with Switching Costs and Ramp Constraints. - Ming Shi 003; Xiaojun Lin 001; Sonia Fahmy; Dong-Hoon Shin (INFOCOM, 2018)

On the Value of Look-Ahead in Competitive Online Convex Optimization. - Ming Shi 003; Xiaojun Lin 001; Lei Jiao 002 (Proc. ACM Meas. Anal. Comput. Syst., 2019)

Competitive Online Convex Optimization With Switching Costs and Ramp Constraints. - Ming Shi 003; Xiaojun Lin 001; Sonia Fahmy (IEEE/ACM Trans. Netw., 2021)

Leveraging Synergies Between AI and Networking to Build Next Generation Edge Networks. - Sen Lin 001; Ming Shi 003; Anish Arora; Raef Bassily; Elisa Bertino; Constantine Caramanis; Kaushik R. Chowdhury; Eylem Ekici; Atilla Eryilmaz; Stratis Ioannidis; Nan Jiang 008; Gauri Joshi; et al. (CIC, 2022)

Power-of-2-arms for bandit learning with switching costs. - Ming Shi 003; Xiaojun Lin 001; Lei Jiao 002 (MobiHoc, 2022)

Near-Optimal Adversarial Reinforcement Learning with Switching Costs. - Ming Shi 003; Yingbin Liang; Ness B. Shroff (ICLR, 2023)

A Near-Optimal Algorithm for Safe Reinforcement Learning Under Instantaneous Hard Constraints. - Ming Shi 003; Yingbin Liang; Ness B. Shroff (ICML, 2023)

Theoretical Hardness and Tractability of POMDPs in RL with Partial Hindsight State Information. - Ming Shi 003; Yingbin Liang; Ness B. Shroff (CoRR, 2023)

Designing Near-Optimal Partially Observable Reinforcement Learning. - Ming Shi 003; Yingbin Liang; Ness B. Shroff (MILCOM, 2024)

Combining Regularization With Look-Ahead for Competitive Online Convex Optimization. - Ming Shi 003; Xiaojun Lin 001; Lei Jiao 002 (IEEE/ACM Trans. Netw., 2024)

Provably Efficient Multi-Objective Bandit Algorithms under Preference-Centric Customization. - Linfeng Cao; Ming Shi 003; Ness B. Shroff (CoRR, 2025)

Provably Efficient RL for Linear MDPs under Instantaneous Safety Constraints in Non-Convex Feature Spaces. - Amirhossein Roknilamouki; Arnob Ghosh; Ming Shi 003; Fatemeh Nourzad; Eylem Ekici; Ness B. Shroff (CoRR, 2025)