  Regression-via-Classification (RvC) is the process of converting a regression
problem to a classification one. Current approaches for RvC use ad-hoc
discretization strategies and are suboptimal. We propose a neural regression
tree model for RvC. In this model, we employ a joint optimization framework
where we learn optimal discretization thresholds while simultaneously
optimizing the features for each node in the tree. We empirically show the
validity of our model by testing it on two challenging regression tasks where
we establish the state of the art.
