most citedHierarchical Graph Neural Networks for Causal Discovery and Root Cause Localization

6 citations · 14 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG20234 cited

Reinforcement-Enhanced Autoregressive Feature Transformation: Gradient-steered Search in Continuous Space for Postfix Expressions

Dongjie Wang, Meng Xiao, Min Wu +3

Feature transformation aims to generate new pattern-discriminative feature space from original features to improve downstream machine learning (ML) task performances. However, the…

cs.LG2023

Self-optimizing Feature Generation via Categorical Hashing Representation and Hierarchical Reinforcement Crossing

Wangyang Ying, Dongjie Wang, Kunpeng Liu +2

Feature generation aims to generate new and meaningful features to create a discriminative representation space.A generated feature is meaningful when the generated feature is from…

cs.LG2023

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…

cs.LG20233 cited

Dish-TS: A General Paradigm for Alleviating Distribution Shift in Time Series Forecasting

Wei Fan, Pengyang Wang, Dongkun Wang +3

The distribution shift in Time Series Forecasting (TSF), indicating series distribution changes over time, largely hinders the performance of TSF models. Existing works towards dis…

cs.LG2023

Deep Graph Stream SVDD: Anomaly Detection in Cyber-Physical Systems

Ehtesamul Azim, Dongjie Wang, Yanjie Fu

Our work focuses on anomaly detection in cyber-physical systems. Prior literature has three limitations: (1) Failing to capture long-delayed patterns in system anomalies; (2) Ignor…

cs.LG20236 cited

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…