  We propose a simple method by which to choose sample weights for problems
with highly imbalanced or skewed traits. Rather than naively discretizing
regression labels to find binned weights, we take a more principled approach --
we derive sample weights from the transfer function between an estimated source
and specified target distributions. Our method outperforms both unweighted and
discretely-weighted models on both regression and classification tasks. We also
open-source our implementation of this method
(https://github.com/Daniel-Wu/Continuous-Weight-Balancing) to the scientific
community.
