  The precise reconstruction of jet transverse momenta in heavy-ion collisions
is a challenging task. A major obstacle is the large number of (mainly)
low-$p_{\rm T}$ particles overlaying the jets. Strong region-to-region
fluctuations of this background complicate the jet measurement and lead to
significant uncertainties. In this paper, a novel approach to correct jet
momenta (or energies) for the underlying background in heavy-ion collisions is
introduced. The proposed method makes use of common Machine Learning techniques
to estimate the jet transverse momentum based on several parameters, including
properties of the jet constituents. Using a toy model and HIJING simulations,
the performance of the new method is shown to be superior to the established
standard area-based background estimator. The application of the new method to
data promises the measurement of jets down to extremely low transverse momenta,
unprecedented thus far in data on heavy-ion collisions.
