# Towards Robust Classification with Deep Generative Forests

arXiv: https://arxiv.org/abs/2007.05721 · HTML: https://mlsanity.com/paper/2007.05721/

## Key facts

- Cited by 5 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 11 Jul 2020.

## Built on by (most cited first, 5 of 5)

- [Robustness quantification: a new method for assessing the reliability of the predictions of a classifier](https://mlsanity.com/paper/2503.22418/) (2025)
- [Robustness and uncertainty: two complementary aspects of the reliability of the predictions of a classifier](https://mlsanity.com/paper/2512.15492/) (2025)
- [Robustness Quantification and Uncertainty Quantification: Comparing Two Methods for Assessing the Reliability of Classifier Predictions](https://mlsanity.com/paper/2603.22988/) (2026)
- [Robustness Quantification for Discriminative Models: a New Robustness Metric and its Application to Dynamic Classifier Selection](https://mlsanity.com/paper/2603.23318/) (2026)
- [Local Robustness Quantification for Naive Bayes Classifiers and Generative Forests: a General Approach](https://mlsanity.com/paper/2609.11366/) (2026)

## Abstract (arXiv)

Decision Trees and Random Forests are among the most widely used machine learning models, and often achieve state-of-the-art performance in tabular, domain-agnostic datasets. Nonetheless, being primarily discriminative models they lack principled methods to manipulate the uncertainty of predictions. In this paper, we exploit Generative Forests (GeFs), a recent class of deep probabilistic models that addresses these issues by extending Random Forests to generative models representing the full joint distribution over the feature space. We demonstrate that GeFs are uncertainty-aware classifiers, capable of measuring the robustness of each prediction as well as detecting out-of-distribution samples.