  We show how weakly supervised machine learning can improve the sensitivity of
LHC mono-jet searches to new physics models with anomalous jet dynamics. The
Classification Without Labels (CWoLa) method is used to extract all the
information available from low-level detector information without any reference
to specific new physics models. For the example of a strongly interacting dark
matter model, we employ simulated data to show that the discovery potential of
an existing generic search can be boosted considerably.
