  Recent innovations from machine learning allow for data unfolding, without
binning and including correlations across many dimensions. We describe a set of
known, upgraded, and new methods for ML-based unfolding. The performance of
these approaches are evaluated on the same two datasets. We find that all
techniques are capable of accurately reproducing the particle-level spectra
across complex observables. Given that these approaches are conceptually
diverse, they offer an exciting toolkit for a new class of measurements that
can probe the Standard Model with an unprecedented level of detail and may
enable sensitivity to new phenomena.
