Wenxiao Chen - IEEE Xplore Author Profile
Subscribe
ADVANCED SEARCH

Author details

< Back

Wenxiao Chen

Publications
5
Citations
145
Publications by Year
20182024
Co-Authors:
Show All Co-Authors (23)
Image of author Wenxiao Chen

Wenxiao Chen

Affiliation

Huawei

Publication Topics

Biography

Wenxiao Chen received the B.S. degree from Tsinghua University, Beijing, China, in 2017, where he is currently pursuing the Ph.D. degree with the Computer Science and Technology Department. His current research interests include time-series anomaly detection, machine learning, deep generative model, graph neural networks, root cause analysis, and out-of-distribution detection.(Based on document published on 29 July 2022).
Publications
5
Citations
145
Publications by Year
20182024
Co-Authors:
Show All Co-Authors (23)
Author's Published Works

Showing 1-5 of 5 results

  • Sort

Filter Results

Show

Sort By

Results

Causality Enhanced Graph Representation Learning for Alert-Based Root Cause Analysis

Zhaoyang Yu;Qianyu Ouyang;Changhua Pei;Xin Wang;Wenxiao Chen;Liangfei Su;Huai Jiang;Xuanrun Wang;Jianhui Li;Dan Pei

2024 IEEE 24th International Symposium on Cluster, Cloud and Internet Computing (CCGrid)
Year: 2024 | Conference Paper |
Accurate and efficient root cause identification in online service systems is critical for service stability and user experience. When a system failure occurs, numerous alerts are generated, but existing methods fail to effectively integrate all these multi-modal data to pinpoint the root causes. Moreover, most existing approaches are inefficient for large-scale online services due to their high r...Show More

Causality Enhanced Graph Representation Learning for Alert-Based Root Cause Analysis

Zhaoyang Yu;Qianyu Ouyang;Changhua Pei;Xin Wang;Wenxiao Chen;Liangfei Su;Huai Jiang;Xuanrun Wang;Jianhui Li;Dan Pei

Year: 2024 | Conference Paper |

Situation-Aware Multivariate Time Series Anomaly Detection Through Active Learning and Contrast VAE-Based Models in Large Distributed Systems

Zhihan Li;Youjian Zhao;Yitong Geng;Zhanxiang Zhao;Hanzhang Wang;Wenxiao Chen;Huai Jiang;Amber Vaidya;Liangfei Su;Dan Pei

IEEE Journal on Selected Areas in Communications
Year: 2022 | Volume: 40, Issue: 9 | Journal Article |
Cited by: Papers (16)
The massive amounts of monitoring data in network applications bring an urgent need for intelligent operation in large distributed systems. The key problem is precisely detecting anomalies in multivariate time series (MTS) monitoring metrics with the awareness of different application scenarios. Unsupervised MTS anomaly detection methods aim at detecting data anomalies from historical MTS without ...Show More

Situation-Aware Multivariate Time Series Anomaly Detection Through Active Learning and Contrast VAE-Based Models in Large Distributed Systems

Zhihan Li;Youjian Zhao;Yitong Geng;Zhanxiang Zhao;Hanzhang Wang;Wenxiao Chen;Huai Jiang;Amber Vaidya;Liangfei Su;Dan Pei

Year: 2022 | Volume: 40, Issue: 9 | Journal Article |

Unsupervised Clustering through Gaussian Mixture Variational AutoEncoder with Non-Reparameterized Variational Inference and Std Annealing

Zhihan Li;Youjian Zhao;Haowen Xu;Wenxiao Chen;Shangqing Xu;Yilin Li;Dan Pei

2020 International Joint Conference on Neural Networks (IJCNN)
Year: 2020 | Conference Paper |
Cited by: Papers (7)
Clustering has long been an important research topic in machine learning, and is highly valuable in many application tasks. In recent years, many methods have achieved high clustering performance by applying deep generative models. In this paper, we point out that directly using q(z|y, x) instead of resorting to the mean-field approximation (as is adopted in previous works) in Gaussian Mixture Var...Show More

Unsupervised Clustering through Gaussian Mixture Variational AutoEncoder with Non-Reparameterized Variational Inference and Std Annealing

Zhihan Li;Youjian Zhao;Haowen Xu;Wenxiao Chen;Shangqing Xu;Yilin Li;Dan Pei

Year: 2020 | Conference Paper |

Unsupervised Anomaly Detection for Intricate KPIs via Adversarial Training of VAE

Wenxiao Chen;Haowen Xu;Zeyan Li;Dan Pei;Jie Chen;Honglin Qiao;Yang Feng;Zhaogang Wang

IEEE INFOCOM 2019 - IEEE Conference on Computer Communications
Year: 2019 | Conference Paper |
Cited by: Papers (66)
To ensure the reliability of the Internet-based application services, KPIs (Key Performance Monitors) are closely monitored in real time and the anomalies presented in the KPIs must be discovered in time. While anomaly detection for the seasonal smooth service-level KPIs (e.g., number of transactions per minute) have been solved reasonably well in the literature, the intricate KPIs at the machine ...Show More

Unsupervised Anomaly Detection for Intricate KPIs via Adversarial Training of VAE

Wenxiao Chen;Haowen Xu;Zeyan Li;Dan Pei;Jie Chen;Honglin Qiao;Yang Feng;Zhaogang Wang

Year: 2019 | Conference Paper |

Robust and Unsupervised KPI Anomaly Detection Based on Conditional Variational Autoencoder

Zeyan Li;Wenxiao Chen;Dan Pei

2018 IEEE 37th International Performance Computing and Communications Conference (IPCCC)
Year: 2018 | Conference Paper |
Cited by: Papers (56)
To ensure undisrupted web-based services, operators need to closely monitor various KPIs (Key Performance Indicator, such as CPU usages, network throughput, page views, number of online users, and etc), detect anomalies in them, and trigger timely troubleshooting or mitigation. There can be hundreds of thousands to even millions of KPIs to be monitored, thus operators need automatic anomaly detect...Show More

Robust and Unsupervised KPI Anomaly Detection Based on Conditional Variational Autoencoder

Zeyan Li;Wenxiao Chen;Dan Pei

Year: 2018 | Conference Paper |

IEEE Account

Purchase Details

Profile Information

Need Help?

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.