Outlier detection is an important task for various data mining applications. Current outlier
detection techniques are often manually designed for specific domains, requiring large …

Outlier detection is an important data mining task with numerous applications such as
intrusion detection, credit card fraud detection, and video surveillance. However, given a …

Outlier detection is an important data mining task with numerous practical applications such
as intrusion detection, credit card fraud detection, and video surveillance. However, given a …

Feature preprocessing, which transforms raw input features into numerical representations,
is a crucial step in automated machine learning (AutoML) systems. However, the existing …

Financial multivariate time series anomaly detection has received widespread attention,
which is crucial for the reliable and safe operation of the financial system. Based on this …

Deep reinforcement learning has recently achieved remarkable success in various domains,
ranging from games [10, 11, 12], to real-world applications such as neural architecture …

Deep reinforcement learning (deep RL) has recently achieved remarkable success in
various domains, from simulated games to real-world applications. However, deep RL …

Identifying anomalies with complex patterns is different from the conventional anomaly
detection problem. Firstly, for cross-modal anomaly detection problems, a large portion of …