Learning Exceptional Subgroups by End-to-End Maximizing KL-divergence
Sascha Xu Nils Philipp Walter Janis Kalofolias Jilles Vreeken
Finding and describing sub-populations that are exceptional regarding a
target property has important applications in many scientific disciplines, from
identifying disadvantaged demographic groups in census data to finding
conductive molecules within gold nanoparticles. Current approaches to finding
such subgroups require pre-discretized predictive variables, do not permit
non-trivial target distributions, do not scale to large datasets, and struggle
to find diverse results.
  To address these limitations, we propose Syflow, an end-to-end optimizable
approach in which we leverage normalizing flows to model arbitrary target
distributions, and introduce a novel neural layer that results in easily
interpretable subgroup descriptions. We demonstrate on synthetic and real-world
data, including a case study, that Syflow reliably finds highly exceptional
subgroups accompanied by insightful descriptions.