# AutoEncoder by Forest

arXiv: https://arxiv.org/abs/1709.09018 · HTML: https://mlsanity.com/paper/1709.09018/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2017.

## Built on by (most cited first, 8 of 8)

- [On Explaining Random Forests with SAT](https://mlsanity.com/paper/2105.10278/) (2021)
- [Adversarial random forests for density estimation and generative modeling](https://mlsanity.com/paper/2205.09435/) (2022)
- [Locally-Minimal Probabilistic Explanations](https://mlsanity.com/paper/2312.11831/) (2023)
- [Autoencoding Random Forests](https://mlsanity.com/paper/2505.21441/) (2025)
- [OF-AE: Oblique Forest AutoEncoders](https://mlsanity.com/paper/2301.00880/) (2023)
- [FPGA Deployment of LFADS for Real-time Neuroscience Experiments](https://mlsanity.com/paper/2402.04274/) (2024)
- [Sparse Additive Model Pruning for Order-Based Causal Structure Learning](https://mlsanity.com/paper/2602.15306/) (2026)
- [Kernel of Partition Paths: A Unified Representation for Tree Ensembles](https://mlsanity.com/paper/2606.18853/) (2026)

## Abstract (arXiv)

Auto-encoding is an important task which is typically realized by deep neural networks (DNNs) such as convolutional neural networks (CNN). In this paper, we propose EncoderForest (abbrv. eForest), the first tree ensemble based auto-encoder. We present a procedure for enabling forests to do backward reconstruction by utilizing the equivalent classes defined by decision paths of the trees, and demonstrate its usage in both supervised and unsupervised setting. Experiments show that, compared with DNN autoencoders, eForest is able to obtain lower reconstruction error with fast training speed, while the model itself is reusable and damage-tolerable.