# Stochastic Gradient Trees

arXiv: https://arxiv.org/abs/1901.07777 · HTML: https://mlsanity.com/paper/1901.07777/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2019.

## Built on by (most cited first, 11 of 11)

- [Active learning for data streams: a survey](https://mlsanity.com/paper/2302.08893/) (2023)
- [Probabilistic Gradient Boosting Machines for Large-Scale Probabilistic Regression](https://mlsanity.com/paper/2106.01682/) (2021)
- [Learning Accurate Decision Trees with Bandit Feedback via Quantized Gradient Descent](https://mlsanity.com/paper/2102.07567/) (2021)
- [Dynamic Model Tree for Interpretable Data Stream Learning](https://mlsanity.com/paper/2203.16181/) (2022)
- [RoME: A Robust Mixed-Effects Bandit Algorithm for Optimizing Mobile Health Interventions](https://mlsanity.com/paper/2312.06403/) (2023)
- [Online hierarchical partitioning of the output space in extreme multi-label data stream](https://mlsanity.com/paper/2507.20894/) (2025)
- [Streaming Machine Learning and Online Active Learning for Automated Visual Inspection](https://mlsanity.com/paper/2110.09396/) (2021)
- [Calibrated Probabilistic Forecasts for Arbitrary Sequences](https://mlsanity.com/paper/2409.19157/) (2024)
- [Hoeffding adaptive trees for multi-label classification on data streams](https://mlsanity.com/paper/2410.20242/) (2024)
- [Soft Hoeffding Tree: A Transparent and Differentiable Model on Data Streams](https://mlsanity.com/paper/2411.04812/) (2024)
- [On Imbalanced Regression with Hoeffding Trees](https://mlsanity.com/paper/2602.22101/) (2026)

## Abstract (arXiv)

We present an algorithm for learning decision trees using stochastic gradient information as the source of supervision. In contrast to previous approaches to gradient-based tree learning, our method operates in the incremental learning setting rather than the batch learning setting, and does not make use of soft splits or require the construction of a new tree for every update. We demonstrate how one can apply these decision trees to different problems by changing only the loss function, using classification, regression, and multi-instance learning as example applications. In the experimental evaluation, our method performs similarly to standard incremental classification trees, outperforms state of the art incremental regression trees, and achieves comparable performance with batch multi-instance learning methods.