# AMF: Aggregated Mondrian Forests for Online Learning

arXiv: https://arxiv.org/abs/1906.10529 · HTML: https://mlsanity.com/paper/1906.10529/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2019.

## Built on by (most cited first, 17 of 17)

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- [Adaptive Bayesian Online Learning via Expert Aggregation](https://mlsanity.com/paper/2607.20239/) (2026)
- [WildWood: a new Random Forest algorithm](https://mlsanity.com/paper/2109.08010/) (2021)
- [Online GentleAdaBoost -- Technical Report](https://mlsanity.com/paper/2308.14004/) (2023)
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- [Variational Polya Tree](https://mlsanity.com/paper/2510.22651/) (2025)
- [Adaptive Learning Strategies for AoA-Based Outdoor Localization: A Comprehensive Framework](https://mlsanity.com/paper/2605.05055/) (2026)

## Abstract (arXiv)

Random Forests (RF) is one of the algorithms of choice in many supervised learning applications, be it classification or regression. The appeal of such tree-ensemble methods comes from a combination of several characteristics: a remarkable accuracy in a variety of tasks, a small number of parameters to tune, robustness with respect to features scaling, a reasonable computational cost for training and prediction, and their suitability in high-dimensional settings. The most commonly used RF variants however are "offline" algorithms, which require the availability of the whole dataset at once. In this paper, we introduce AMF, an online random forest algorithm based on Mondrian Forests. Using a variant of the Context Tree Weighting algorithm, we show that it is possible to efficiently perform an exact aggregation over all prunings of the trees; in particular, this enables to obtain a truly online parameter-free algorithm which is competitive with the optimal pruning of the Mondrian tree, and thus adaptive to the unknown regularity of the regression function. Numerical experiments show that AMF is competitive with respect to several strong baselines on a large number of datasets for multi-class classification.