Publications (27)
Restricted Tweedie Stochastic Block Models
Jie Jian, Mu Zhu, Peijun Sang
The stochastic block model (SBM) is a widely used framework for community detection in networks, where the network structure is typically represented by an adjacency matrix. Howeve…
High-dimensional covariance matrix estimation using a low-rank and diagonal decomposition
Yilei Wu, Yingli Qin, Mu Zhu
We study high-dimensional covariance/precision matrix estimation under the assumption that the covariance/precision matrix can be decomposed into a low-rank component L and a diago…
Threshold-free Evaluation of Medical Tests for Classification and Prediction: Average Precision versus Area Under the ROC Curve
Wanhua Su, Yan Yuan, Mu Zhu
When evaluating medical tests or biomarkers for disease classification, the area under the receiver-operating characteristic (ROC) curve is a widely used performance metric that do…
Classifying Network Data with Deep Kernel Machines
Xiao Tang, Mu Zhu
Inspired by a growing interest in analyzing network data, we study the problem of node classification on graphs, focusing on approaches based on kernel machines. Conventionally, ke…
Cyber Deception for Mission Surveillance via Hypergame-Theoretic Deep Reinforcement Learning
Zelin Wan, Jin-Hee Cho, Mu Zhu +3
Unmanned Aerial Vehicles (UAVs) are valuable for mission-critical systems like surveillance, rescue, or delivery. Not surprisingly, such systems attract cyberattacks, including Den…
Decision Theory-Guided Deep Reinforcement Learning for Fast Learning
Zelin Wan, Jin-Hee Cho, Mu Zhu +3
This paper introduces a novel approach, Decision Theory-guided Deep Reinforcement Learning (DT-guided DRL), to address the inherent cold start problem in DRL. By integrating decisi…