Machine learning plays a role in many deployed decision systems, often in ways that are
difficult or impossible to understand by human stakeholders. Explaining, in a human …

Anomaly detection is widely used for identifying critical errors and suspicious behaviors, but
current methods lack interpretability. We leverage common properties of existing methods …

We present a novel time series anomaly detection method that achieves excellent detection
accuracy while offering a superior level of explainability. Our proposed method, TimeVQVAE …

Detecting rare events is essential in various fields, eg, in cyber security or maintenance.
Often, human experts are supported by anomaly detection systems as continuously …

Industry 4.0 has facilitated the access to sensor and actuator data from manufacturing
systems, leading to studies on data-driven anomaly detection, but limited attention has been …

Machine learning (ML) methods have experienced significant growth in the past decade, yet
their practical application in high-impact real-world domains has been hindered by their …

The popularity of deep learning methods in the time series domain boosts interest in
interpretability studies, including counterfactual (CF) methods. CF methods identify minimal …

The purpose of the study is to develop a systemthat automatically processes data based on
existing and newly entered data, especially with the aim of ensuring high data quality by …

The complexity of modern electro-mechanical systems require the development of
sophisticated diagnostic methods like anomaly detection capable of detecting deviations …

This paper introduces Personalized Path Recourse, a novel method that generates recourse
paths for a reinforcement learning agent. The goal is to edit a given path of actions to …