102 citations · 547 across the 60 of their papers we have counts for
7 papers · 1 filter
Meta-AAD: Active Anomaly Detection with Deep Reinforcement Learning
Daochen Zha, Kwei-Herng Lai, Mingyang Wan +1
High false-positive rate is a long-standing challenge for anomaly detection algorithms, especially in high-stake applications. To identify the true anomalies, in practice, analysts…
TODS: An Automated Time Series Outlier Detection System
Kwei-Herng Lai, Daochen Zha, Guanchu Wang +8
We present TODS, an automated Time Series Outlier Detection System for research and industrial applications. TODS is a highly modular system that supports easy pipeline constructio…
Policy-GNN: Aggregation Optimization for Graph Neural Networks
Kwei-Herng Lai, Daochen Zha, Kaixiong Zhou +1
Graph data are pervasive in many real-world applications. Recently, increasing attention has been paid on graph neural networks (GNNs), which aim to model the local graph structure…
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…
Towards Deeper Graph Neural Networks with Differentiable Group Normalization
Kaixiong Zhou, Xiao Huang, Yuening Li +3
Graph neural networks (GNNs), which learn the representation of a node by aggregating its neighbors, have become an effective computational tool in downstream applications. Over-sm…
Dual Policy Distillation
Kwei-Herng Lai, Daochen Zha, Yuening Li +1
Policy distillation, which transfers a teacher policy to a student policy has achieved great success in challenging tasks of deep reinforcement learning. This teacher-student frame…
PyODDS: An End-to-end Outlier Detection System with Automated Machine Learning
Yuening Li, Daochen Zha, Praveen Kumar Venugopal +2
Outlier detection is an important task for various data mining applications. Current outlier detection techniques are often manually designed for specific domains, requiring large…