  Discriminating quark-like from gluon-like jets is, in many ways, a key
challenge for many LHC analyses. First, we use a known difference in Pythia and
Herwig simulations to show how decorrelated taggers would break down when the
most distinctive feature is aligned with theory uncertainties. We propose
conditional training on interpolated samples, combined with a controlled
Bayesian network, as a more resilient framework. The interpolation parameter
can be used to optimize the training evaluated on a calibration dataset, and to
test the stability of this optimization. The interpolated training might also
be useful to track generalization errors when training networks on simulation.
