# Random Hinge Forest for Differentiable Learning

arXiv: https://arxiv.org/abs/1802.03882 · HTML: https://mlsanity.com/paper/1802.03882/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2018.

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

- [Why do tree-based models still outperform deep learning on tabular data?](https://mlsanity.com/paper/2207.08815/) (2022)
- [DANets: Deep Abstract Networks for Tabular Data Classification and Regression](https://mlsanity.com/paper/2112.02962/) (2021)
- [Flexible Modeling and Multitask Learning using Differentiable Tree Ensembles](https://mlsanity.com/paper/2205.09717/) (2022)
- [Efficient Click-Through Rate Prediction for Developing Countries via Tabular Learning](https://mlsanity.com/paper/2104.07553/) (2021)

## Abstract (arXiv)

We propose random hinge forests, a simple, efficient, and novel variant of decision forests. Importantly, random hinge forests can be readily incorporated as a general component within arbitrary computation graphs that are optimized end-to-end with stochastic gradient descent or variants thereof. We derive random hinge forest and ferns, focusing on their sparse and efficient nature, their min-max margin property, strategies to initialize them for arbitrary network architectures, and the class of optimizers most suitable for optimizing random hinge forest. The performance and versatility of random hinge forests are demonstrated by experiments incorporating a variety of of small and large UCI machine learning data sets and also ones involving the MNIST, Letter, and USPS image datasets. We compare random hinge forests with random forests and the more recent backpropagating deep neural decision forests.