Publication

You can also find all my articles on my Google Scholar

Preprints

  1. Yuanyuan Shi, Baosen Zhang, “Learning in Cournot Games with Limited Information Feedback’’, arXiv Preprint.

  2. Yuanyuan Shi, Meng Qi, Chenxin Ma, Rong Yuan, Di Wu, and Zuojun (Max) Shen, “A Practical End-to-End Inventory Management Model with Deep Learning’’, submitted to Management Science.

  3. Yize Chen, Yuanyuan Shi, and Bao Zhang, “Data-Driven Optimal Voltage Regulation’’, submitted to Power Systems Computation Conference (PSCC), 2020.

Journal Papers

  1. Yuanyuan Shi, Bolun Xu, Yushi Tan, Daniel Kirschen, and Baosen Zhang, “Optimal Battery Control Under Cycle Aging Mechanisms in Pay for Performance Settings’’, IEEE Transactions on Automatic Control, 2019. [PDF] [Code]

  2. Bolun Xu, Yuanyuan Shi, Daniel Kirschen, and Baosen Zhang, “Optimal Battery Participation in Frequency Regulation Markets”, IEEE Transactions on Power Systems, 2018. [PDF] [Code]

  3. Yuanyuan Shi, Bolun Xu, Di Wang, and Baosen Zhang, “Using Battery Storage for Peak Shaving and Frequency Regulation: Joint Optimization for Superlinear Gains’’, IEEE Transactions on Power Systems, 2017. [PDF] [Code]

  4. Luowei Zhou, Yuanyuan Shi, Jiangliu Wang, and Pei Yang, “A Balanced Heuristic Mechanism for Multirobot Task Allocation of Intelligent Warehouses’’, Journal of Mathematical Problems in Engineering, 2014. [PDF]

Conference Papers

  1. Daniel J. Mankowitz, Nir Levine, Rae Jeong, Abbas Abdolmaleki, Jost Tobias Springenberg, Yuanyuan Shi, Jackie Kay, Todd Hester, Timothy Mann, Martin Riedmiller, “Robust Reinforcement Learning for Continuous Control with Model Misspecification’’, International Conference on Learning Representations (ICLR), 2020. [PDF]

  2. Yuanyuan Shi, Kai Xiao, Daniel J. Mankowitz, Rae Jeong, Nir Levine, Sven Gowal, Timothy Mann, and Todd Hester, “Data-Driven Robust Reinforcement Learning for Continuous Control’’, Safety and Robustness in Decision Making Workshop, Neural Information Processing Systems (NeurIPS), 2019. [PDF] [Poster]

  3. Kai Xiao, Sven Gowal, Todd Hester, Rae Jeong, Daniel J. Mankowitz, Yuanyuan Shi, and T.W. Weng, “Learning Neural Dynamics Simulators With Adversarial Specification Training’’, Safety and Robustness in Decision Making Workshop, Neural Information Processing Systems (NeurIPS), 2019. [PDF] [Poster]

  4. Yize Chen*, Yuanyuan Shi*, and Baosen Zhang, “Optimal Control Via Neural Networks: A Convex Approach’’, International Conference on Learning Representations (ICLR), 2019. (*equal contribution). [PDF] [Code]

  5. Yuanyuan Shi, Bolun Xu, Yushi Tan, and Baosen Zhang, “A convex cycle-based degradation model for battery energy storage planning and operation’’, American Control Conference (ACC), 2018 [PDF]

  6. Bolun Xu, Yuanyuan Shi, Daniel Kirschen, and Baosen Zhang, “Optimal regulation response of batteries under cycle aging mechanisms”, IEEE Conference on Decision and Control (CDC), 2017 [PDF]

  7. Yize Chen, Yuanyuan Shi, and Baosen Zhang. “Modeling and Optimization of Complex Building Energy Systems with Deep Neural Networks’’, Asilomar Conference, 2017. [PDF]

  8. Yuanyuan Shi, Bolun Xu, Baosen Zhang, and Di Wang, “Leveraging energy storage to optimize data center electricity cost in emerging power markets”, Seventh International Conference on Future Energy Systems, ACM (e-Energy), 2016. [PDF]