activity
20182023
most citedReinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges

422 citations · 673 across the 48 of their papers we have counts for

collaborators
Showing 2021Show all

11 papers · 1 filter

math.OC2021★ 10 cited

Communication-Efficient Distributed SGD with Compressed Sensing

Yujie Tang, Vikram Ramanathan, Junshan Zhang +1

We consider large scale distributed optimization over a set of edge devices connected to a central server, where the limited communication bandwidth between the server and edge dev…

cs.DC2021★ 33 cited

Notebook-as-a-VRE (NaaVRE): from private notebooks to a collaborative cloud virtual research environment

Zhiming Zhao, Spiros Koulouzis, Riccardo Bianchi +8

Virtual Research Environments (VREs) provide user-centric support in the lifecycle of research activities, e.g., discovering and accessing research assets, or composing and executi…

math.OC2021★ 4 cited

Improve Single-Point Zeroth-Order Optimization Using High-Pass and Low-Pass Filters

Xin Chen, Yujie Tang, Na Li

Single-point zeroth-order optimization (SZO) is useful in solving online black-box optimization and control problems in time-varying environments, as it queries the function value…

eess.SY2021★ 11 cited

Safe Adaptive Learning-based Control for Constrained Linear Quadratic Regulators with Regret Guarantees

Yingying Li, Subhro Das, Jeff Shamma +1

We study the adaptive control of an unknown linear system with a quadratic cost function subject to safety constraints on both the states and actions. The challenges of this proble…

cs.SD2021★ 3 cited

Simple Attention Module based Speaker Verification with Iterative noisy label detection

Xiaoyi Qin, Na Li, Chao Weng +2

Recently, the attention mechanism such as squeeze-and-excitation module (SE) and convolutional block attention module (CBAM) has achieved great success in deep learning-based speak…

cs.NE2021

Self-adaptive Multi-task Particle Swarm Optimization

Xiaolong Zheng, Deyun Zhou, Na Li +3

Multi-task optimization (MTO) studies how to simultaneously solve multiple optimization problems for the purpose of obtaining better performance on each problem. Over the past few…