  There has been substantial progress in applying machine learning techniques
to classification problems in collider and jet physics. But as these techniques
grow in sophistication, they are becoming more sensitive to subtle features of
jets that may not be well modeled in simulation. Therefore, relying on
simulations for training will lead to sub-optimal performance in data, but the
lack of true class labels makes it difficult to train on real data. To address
this challenge we introduce a new approach, called Tag N' Train (TNT), that can
be applied to unlabeled data that has two distinct sub-objects. The technique
uses a weak classifier for one of the objects to tag signal-rich and
background-rich samples. These samples are then used to train a stronger
classifier for the other object. We demonstrate the power of this method by
applying it to a dijet resonance search. By starting with autoencoders trained
directly on data as the weak classifiers, we use TNT to train substantially
improved classifiers. We show that Tag N' Train can be a powerful tool in
model-agnostic searches and discuss other potential applications.
