  Exploiting the rapid advances in probabilistic inference, in particular
variational Bayes and variational autoencoders (VAEs), for anomaly detection
(AD) tasks remains an open research question. Previous works argued that
training VAE models only with inliers is insufficient and the framework should
be significantly modified in order to discriminate the anomalous instances. In
this work, we exploit the deep conditional variational autoencoder (CVAE) and
we define an original loss function together with a metric that targets
hierarchically structured data AD. Our motivating application is a real world
problem: monitoring the trigger system which is a basic component of many
particle physics experiments at the CERN Large Hadron Collider (LHC). In the
experiments we show the superior performance of this method for classical
machine learning (ML) benchmarks and for our application.
