Harnessing smoothness to accelerate distributed optimization G Qu, N Li IEEE Transactions on Control of Network Systems 5 (3), 1245-1260, 2017 | 708 | 2017 |
Accelerated distributed Nesterov gradient descent G Qu, N Li IEEE Transactions on Automatic Control 65 (6), 2566-2581, 2019 | 280* | 2019 |
Reinforcement learning for selective key applications in power systems: Recent advances and future challenges X Chen, G Qu, Y Tang, S Low, N Li IEEE Transactions on Smart Grid 13 (4), 2935-2958, 2022 | 250* | 2022 |
Optimal scheduling of battery charging station serving electric vehicles based on battery swapping X Tan, G Qu, B Sun, N Li, DHK Tsang IEEE Transactions on Smart Grid 10 (2), 1372-1384, 2017 | 161 | 2017 |
On the exponential stability of primal-dual gradient dynamics G Qu, N Li IEEE Control Systems Letters 3 (1), 43-48, 2018 | 144 | 2018 |
Real-time decentralized voltage control in distribution networks N Li, G Qu, M Dahleh 2014 52nd Annual Allerton Conference on Communication, Control, and …, 2014 | 138 | 2014 |
Optimal distributed feedback voltage control under limited reactive power G Qu, N Li IEEE Transactions on Power Systems 35 (1), 315-331, 2019 | 137 | 2019 |
A random forest method for real-time price forecasting in New York electricity market J Mei, D He, R Harley, T Habetler, G Qu 2014 IEEE PES general meeting| conference & exposition, 1-5, 2014 | 126 | 2014 |
Online optimization with predictions and switching costs: Fast algorithms and the fundamental limit Y Li, G Qu, N Li IEEE Transactions on Automatic Control 66 (10), 4761-4768, 2020 | 117* | 2020 |
Finite-Time Analysis of Asynchronous Stochastic Approximation and -Learning G Qu, A Wierman Conference on Learning Theory, 3185-3205, 2020 | 117 | 2020 |
Scalable reinforcement learning for multiagent networked systems G Qu, A Wierman, N Li Operations Research 70 (6), 3601-3628, 2022 | 110* | 2022 |
Distributed greedy algorithm for multi-agent task assignment problem with submodular utility functions G Qu, D Brown, N Li Automatica 105, 206-215, 2019 | 78* | 2019 |
Learning optimal power flow: Worst-case guarantees for neural networks A Venzke, G Qu, S Low, S Chatzivasileiadis 2020 IEEE International Conference on Communications, Control, and Computing …, 2020 | 72 | 2020 |
Distributed optimal voltage control with asynchronous and delayed communication S Magnússon, G Qu, N Li IEEE Transactions on Smart Grid 11 (4), 3469-3482, 2020 | 71 | 2020 |
Scalable multi-agent reinforcement learning for networked systems with average reward G Qu, Y Lin, A Wierman, N Li Advances in Neural Information Processing Systems 33, 2074-2086, 2020 | 63 | 2020 |
Multi-agent reinforcement learning in stochastic networked systems Y Lin, G Qu, L Huang, A Wierman Advances in neural information processing systems 34, 7825-7837, 2021 | 56* | 2021 |
Voltage control using limited communication S Magnússon, G Qu, C Fischione, N Li IEEE Transactions on Control of Network Systems 6 (3), 993-1003, 2019 | 38 | 2019 |
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 | 37 | 2022 |
Perturbation-based regret analysis of predictive control in linear time varying systems Y Lin, Y Hu, G Shi, H Sun, G Qu, A Wierman Advances in Neural Information Processing Systems 34, 5174-5185, 2021 | 35 | 2021 |
Combining model-based and model-free methods for nonlinear control: A provably convergent policy gradient approach G Qu, C Yu, S Low, A Wierman arXiv preprint arXiv:2006.07476, 2020 | 34* | 2020 |