| Using battery storage for peak shaving and frequency regulation: Joint optimization for superlinear gains Y Shi, B Xu, D Wang, B Zhang IEEE transactions on power systems 33 (3), 2882-2894, 2017 | 464 | 2017 |
| Optimal control via neural networks: A convex approach Y Chen, Y Shi, B Zhang International Conference on Learning Representations (ICLR 2019), 2018 | 239 | 2018 |
| Optimal battery participation in frequency regulation markets B Xu, Y Shi, DS Kirschen, B Zhang IEEE Transactions on Power Systems 33 (6), 6715-6725, 2018 | 187 | 2018 |
| Optimal battery control under cycle aging mechanisms in pay for performance settings Y Shi, B Xu, Y Tan, D Kirschen, B Zhang IEEE Transactions on Automatic Control 64 (6), 2324-2339, 2018 | 138 | 2018 |
| Robust reinforcement learning for continuous control with model misspecification DJ Mankowitz, N Levine, R Jeong, Y Shi, J Kay, A Abdolmaleki, ... International Conference on Learning Representations (ICLR 2020), 2019 | 133 | 2019 |
| A practical end-to-end inventory management model with deep learning M Qi, Y Shi, Y Qi, C Ma, R Yuan, D Wu, ZJM Shen Available at SSRN 3737780, 2020 | 120 | 2020 |
| A convex cycle-based degradation model for battery energy storage planning and operation Y Shi, B Xu, Y Tan, B Zhang 2018 Annual American control conference (ACC), 4590-4596, 2018 | 98 | 2018 |
| Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds Y Huang, H Zhang, Y Shi, JZ Kolter, A Anandkumar Advances in Neural Information Processing Systems 34 (2021)., 2021 | 87 | 2021 |
| Data-driven optimal voltage regulation using input convex neural networks Y Chen, Y Shi, B Zhang Electric Power Systems Research 189, 106741, 2020 | 85* | 2020 |
| Leveraging energy storage to optimize data center electricity cost in emerging power markets Y Shi, B Xu, B Zhang, D Wang Proceedings of the Seventh International Conference on Future Energy Systems …, 2016 | 69 | 2016 |
| Modeling and optimization of complex building energy systems with deep neural networks Y Chen, Y Shi, B Zhang 2017 51st Asilomar Conference on Signals, Systems, and Computers, 1368-1373, 2017 | 68 | 2017 |
| Stability constrained reinforcement learning for real-time voltage control Y Shi, G Qu, S Low, A Anandkumar, A Wierman 2022 American Control Conference (ACC), 2715-2721, 2022 | 47 | 2022 |
| A balanced heuristic mechanism for multirobot task allocation of intelligent warehouses L Zhou, Y Shi, J Wang, P Yang Mathematical Problems in Engineering 2014 (1), 380480, 2014 | 41 | 2014 |
| Neural operators for bypassing gain and control computations in pde backstepping L Bhan, Y Shi, M Krstic IEEE Transactions on Automatic Control, 2023 | 37 | 2023 |
| Stable and Efficient Shapley Value-Based Reward Reallocation for Multi-Agent Reinforcement Learning of Autonomous Vehicles S Han, H Wang, S Su, Y Shi, F Miao IEEE International Conference on Robotics and Automation, 2022 | 32 | 2022 |
| Neural operators of backstepping controller and observer gain functions for reaction–diffusion PDEs M Krstic, L Bhan, Y Shi Automatica 164, 111649, 2024 | 26 | 2024 |
| Optimal regulation response of batteries under cycle aging mechanisms B Xu, Y Shi, DS Kirschen, B Zhang 2017 IEEE 56th Annual Conference on Decision and Control (CDC), 751-756, 2017 | 24 | 2017 |
| Operator learning for nonlinear adaptive control L Bhan, Y Shi, M Krstic Learning for Dynamics and Control Conference, 346-357, 2023 | 22 | 2023 |
| Safe reinforcement learning of control-affine systems with vertex networks L Zheng, Y Shi, LJ Ratliff, B Zhang Learning for Dynamics and Control, 336-347, 2021 | 20 | 2021 |
| Stability constrained reinforcement learning for decentralized real-time voltage control J Feng, Y Shi, G Qu, SH Low, A Anandkumar, A Wierman IEEE Transactions on Control of Network Systems, 2023 | 18 | 2023 |
Baosen ZhangKeith and Nancy Rattie Endowed Career Development Professor, University of WashingtonGeverifieerd e-mailadres voor uw.edu