Anomalies are rare observations (eg, data records or events) that deviate significantly from
the others in the sample. Over the past few decades, research on anomaly mining has …

Outlier detection refers to the identification of data points that deviate from a general data
distribution. Existing unsupervised approaches often suffer from high computational cost …

Current techniques for explaining outliers cannot tell what caused the outliers. We present a
formal method to identify" root causes" of outliers, amongst variables. The method requires a …

Instantial variation (IV) refers to variation that is due not to population differences or errors,
but rather to within-subject variation, that is the intrinsic and characteristic patterns of …

In spite of the rapid advancements in unsupervised log anomaly detection techniques, the
current mainstream models still necessitate specific training for individual system datasets …

In medical settings, Individual Variation (IV) refers to variation that is due not to population
differences or errors, but rather to within-subject variation, that is the intrinsic and …

Recent work conceptualized root cause analysis (RCA) of anomalies via quantitative
contribution analysis using causal counterfactuals in structural causal models (SCMs). The …

Given a complex graph database of node-and edge-attributed multi-graphs as well as
associated metadata for each graph, how can we spot the anomalous instances? Many real …

Graph-based fraud detection is an important task in many real-world domains such as
insurance, finance, and cybersecurity. Even if existing semi-supervised models have proven …

This Ph. D. thesis focuses on anomaly explanation, which has been much less explored in
the literature than the explanation of neural networks and classifiers outputs. Its first …