  We present a novel integrator based on normalizing flows which can be used to
improve the unweighting efficiency of Monte-Carlo event generators for collider
physics simulations. In contrast to machine learning approaches based on
surrogate models, our method generates the correct result even if the
underlying neural networks are not optimally trained. We exemplify the new
strategy using the example of Drell-Yan type processes at the LHC, both at
leading and partially at next-to-leading order QCD.
