Publications (Google Scholar) (LinkedIn)

Dissertation

• On the Value of Prediction and Feedback for Online Decision Making With Switching Costs
  

  Ming Shi
  

  Purdue University Doctoral Dissertation, June 2022.

Book Chapter

• Adversarial Online Reinforcement Learning Under Limited Defender Resources
  

  Ming Shi, Yingbin Liang, and Ness B. Shroff
  

  Network Security Empowered by Artificial Intelligence, Springer, Series ISSN 1568-2633, June 2024.

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, December 2024, DOI: 10.1109/TON.2024.3522073. [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]

• 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]

Conference Papers

• Designing Near-Optimal Partially Observable Reinforcement Learning
  

  Ming Shi, Yingbin Liang, and Ness Shroff
  

  IEEE Military Communications Conference (IEEE MILCOM), Washington, DC, USA, October 2024.

• 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

• Power-of-2-Arms for Bandit Learning with Switching Costs
  

  Ming Shi, Xiaojun Lin, and Lei Jiao, October 2022.
  

• Combining Regularization with Look-Ahead for Competitive Online Convex Optimization
  

  Ming Shi, Xiaojun Lin, and Lei Jiao, May 2021.
  

• Competitive Online Convex Optimization with Switching Costs and Ramp Constraints
  

  Ming Shi, Xiaojun Lin and Sonia Fahmy, April 2021.