# Neural Regression Trees

arXiv: https://arxiv.org/abs/1810.00974 · HTML: https://mlsanity.com/paper/1810.00974/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2018.

## Built on by (most cited first, 4 of 4)

- [Hierarchical Routing Mixture of Experts](https://mlsanity.com/paper/1903.07756/) (2019)
- [Tessellated Linear Model for Age Prediction from Voice](https://mlsanity.com/paper/2501.09229/) (2025)
- [Construction de variables a l'aide de classifieurs comme aide a la regression](https://mlsanity.com/paper/2112.03703/) (2021)
- [Constructing Variables Using Classifiers as an Aid to Regression: An Empirical Assessment](https://mlsanity.com/paper/2403.06829/) (2024)

## Abstract (arXiv)

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.