96 citations · 320 across the 34 of their papers we have counts for
17 papers · 1 filter
Beyond the Permutation Symmetry of Transformers: The Role of Rotation for Model Fusion
Binchi Zhang, Zaiyi Zheng, Zhengzhang Chen +1
Symmetry in the parameter space of deep neural networks (DNNs) has proven beneficial for various deep learning applications. A well-known example is the permutation symmetry in Mul…
Iterative Feature Space Optimization through Incremental Adaptive Evaluation
Yanping Wu, Yanyong Huang, Zhengzhang Chen +5
Iterative feature space optimization involves systematically evaluating and adjusting the feature space to improve downstream task performance. However, existing works suffer from…
Exploring Multi-Modal Data with Tool-Augmented LLM Agents for Precise Causal Discovery
ChengAo Shen, Zhengzhang Chen, Dongsheng Luo +3
Causal discovery is an imperative foundation for decision-making across domains, such as smart health, AI for drug discovery and AIOps. Traditional statistical causal discovery met…
Online Multi-modal Root Cause Identification in Microservice Systems
Lecheng Zheng, Zhengzhang Chen, Haifeng Chen
Root Cause Analysis (RCA) is essential for pinpointing the root causes of failures in microservice systems. Traditional data-driven RCA methods are typically limited to offline app…
RIO-CPD: A Riemannian Geometric Method for Correlation-aware Online Change Point Detection
Chengyuan Deng, Zhengzhang Chen, Xujiang Zhao +4
Change point detection aims to identify abrupt shifts occurring at multiple points within a data sequence. This task becomes particularly challenging in the online setting, where d…
POND: Multi-Source Time Series Domain Adaptation with Information-Aware Prompt Tuning
Junxiang Wang, Guangji Bai, Wei Cheng +3
Time series domain adaptation stands as a pivotal and intricate challenge with diverse applications, including but not limited to human activity recognition, sleep stage classifica…
GLAD: Content-aware Dynamic Graphs For Log Anomaly Detection
Yufei Li, Yanchi Liu, Haoyu Wang +6
Logs play a crucial role in system monitoring and debugging by recording valuable system information, including events and states. Although various methods have been proposed to de…
Disentangled Causal Graph Learning for Online Unsupervised Root Cause Analysis
Dongjie Wang, Zhengzhang Chen, Yanjie Fu +2
The task of root cause analysis (RCA) is to identify the root causes of system faults/failures by analyzing system monitoring data. Efficient RCA can greatly accelerate system fail…
Hierarchical Graph Neural Networks for Causal Discovery and Root Cause Localization
Dongjie Wang, Zhengzhang Chen, Jingchao Ni +4
In this paper, we propose REASON, a novel framework that enables the automatic discovery of both intra-level (i.e., within-network) and inter-level (i.e., across-network) causal re…
Learning Disentangled Representations for Time Series
Yuening Li, Zhengzhang Chen, Daochen Zha +4
Time-series representation learning is a fundamental task for time-series analysis. While significant progress has been made to achieve accurate representations for downstream appl…
Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Series
Yinjun Wu, Jingchao Ni, Wei Cheng +7
Forecasting on sparse multivariate time series (MTS) aims to model the predictors of future values of time series given their incomplete past, which is important for many emerging…
T-Net: A Semi-supervised Deep Model for Turbulence Forecasting
Denghui Zhang, Yanchi Liu, Wei Cheng +5
Accurate air turbulence forecasting can help airlines avoid hazardous turbulence, guide the routes that keep passengers safe, maximize efficiency, and reduce costs. Traditional tur…
Multi-Scale One-Class Recurrent Neural Networks for Discrete Event Sequence Anomaly Detection
Zhiwei Wang, Zhengzhang Chen, Jingchao Ni +3
Discrete event sequences are ubiquitous, such as an ordered event series of process interactions in Information and Communication Technology systems. Recent years have witnessed in…
AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning
Yuening Li, Zhengzhang Chen, Daochen Zha +4
Outlier detection is an important data mining task with numerous practical applications such as intrusion detection, credit card fraud detection, and video surveillance. However, g…
Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs
Lei Cai, Zhengzhang Chen, Chen Luo +4
Detecting anomalies in dynamic graphs is a vital task, with numerous practical applications in areas such as security, finance, and social media. Previous network embedding based m…
Adversarial Defense Framework for Graph Neural Network
Shen Wang, Zhengzhang Chen, Jingchao Ni +4
Graph neural network (GNN), as a powerful representation learning model on graph data, attracts much attention across various disciplines. However, recent studies show that GNN is…
Entity Embedding-based Anomaly Detection for Heterogeneous Categorical Events
Ting Chen, Lu-An Tang, Yizhou Sun +2
Anomaly detection plays an important role in modern data-driven security applications, such as detecting suspicious access to a socket from a process. In many cases, such events ca…