no code implementations • 10 Nov 2023 • Tongxin Li
This policy employs a dynamic robustness budget, which is adapted in real-time based on the reinforcement learning policy's performance.
no code implementations • 27 May 2023 • Dongxiang Yan, Tongxin Li, Changhong Zhao, Han Wang, Yue Chen
This necessitates new business models in the power sector to hedge against uncertainties while imposing a strong coupling between the connected power and transportation networks.
no code implementations • 2 Jun 2022 • Tongxin Li, Ruixiao Yang, Guannan Qu, Yiheng Lin, Steven Low, Adam Wierman
Machine-learned black-box policies are ubiquitous for nonlinear control problems.
no code implementations • NeurIPS 2021 • Tongxin Li, Ruixiao Yang, Guannan Qu, Guanya Shi, Chenkai Yu, Adam Wierman, Steven H. Low
Motivated by online learning methods, we design a self-tuning policy that adaptively learns the trust parameter $\lambda$ with a competitive ratio that depends on $\varepsilon$ and the variation of system perturbations and predictions.
no code implementations • 27 May 2021 • Kaixin Zhang, Hongzhi Wang, Han Hu, Songling Zou, Jiye Qiu, Tongxin Li, Zhishun Wang
In this paper, we demonstrated TENSILE, a method of managing GPU memory in tensor granularity to reduce the GPU memory peak, considering the multiple dynamic workloads.
no code implementations • 17 Jan 2021 • Anqi Liu, Hao liu, Tongxin Li, Saeed Karimi-Bidhendi, Yisong Yue, Anima Anandkumar
Thus, we provide a principled approach to tackling the joint problem of causal discovery and latent variable inference.
no code implementations • 21 Dec 2020 • Tongxin Li, Bo Sun, Yue Chen, Zixin Ye, Steven H. Low, Adam Wierman
To be used effectively, an aggregator must be able to communicate the available flexibility of the loads they control, as known as the aggregate flexibility to a system operator.
Optimization and Control Systems and Control Systems and Control