3 citations · 3 across the 2 of their papers we have counts for
3 papers
cs.LG2025
Scalable, Explainable and Provably Robust Anomaly Detection with One-Step Flow Matching
Zhong Li, Qi Huang, Yuxuan Zhu +4
We introduce Time-Conditioned Contraction Matching (TCCM), a novel method for semi-supervised anomaly detection in tabular data. TCCM is inspired by flow matching, a recent generat…
cs.LG2024
FedTrans: Efficient Federated Learning via Multi-Model Transformation
Yuxuan Zhu, Jiachen Liu, Mosharaf Chowdhury +1
Federated learning (FL) aims to train machine learning (ML) models across potentially millions of edge client devices. Yet, training and customizing models for FL clients is notori…
cs.LG2022★ 3 cited
A Survey on Explainable Anomaly Detection
Zhong Li, Yuxuan Zhu, Matthijs van Leeuwen
In the past two decades, most research on anomaly detection has focused on improving the accuracy of the detection, while largely ignoring the explainability of the corresponding m…