  Monte Carlo event generators are an essential tool for data analysis in
collider physics. To include subleading quantum corrections, these generators
often need to produce negative weight events, which leads to statistical
dilution of the datasets and downstream computational costs for detector
simulation. Building on the recent proposal of a positive resampler method to
rebalance weights within histogram bins, we introduce neural resampling: an
unbinned approach to Monte Carlo reweighting based on neural networks that
scales well to high-dimensional and variable-dimensional phase space. We pay
particular attention to preserving the statistical properties of the event
sample, such that neural resampling not only maintains the mean value of any
observable but also its Monte Carlo uncertainty. This uncertainty preservation
scheme is general and can also be applied to binned (non-neural network)
resampling. To illustrate our neural resampling approach, we present a case
study from the Large Hadron Collider of top quark pair production at
next-to-leading order matched to a parton shower.
