  We explore the use of autoregressive flows, a type of generative model with
tractable likelihood, as a means of efficient generation of physical particle
collider events. The usual maximum likelihood loss function is supplemented by
an event weight, allowing for inference from event samples with variable, and
even negative event weights. To illustrate the efficacy of the model, we
perform experiments with leading-order top pair production events at an
electron collider with importance sampling weights, and with
next-to-leading-order top pair production events at the LHC that involve
negative weights.
