  Change detection is of fundamental importance when analyzing data streams.
Detecting changes both quickly and accurately enables monitoring and prediction
systems to react, e.g., by issuing an alarm or by updating a learning
algorithm. However, detecting changes is challenging when observations are
high-dimensional. In high-dimensional data, change detectors should not only be
able to identify when changes happen, but also in which subspace they occur.
Ideally, one should also quantify how severe they are. Our approach, ABCD, has
these properties. ABCD learns an encoder-decoder model and monitors its
accuracy over a window of adaptive size. ABCD derives a change score based on
Bernstein's inequality to detect deviations in terms of accuracy, which
indicate changes. Our experiments demonstrate that ABCD outperforms its best
competitor by up to 20% in F1-score on average. It can also accurately estimate
changes' subspace, together with a severity measure that correlates with the
ground truth.
