# XBART: Accelerated Bayesian Additive Regression Trees

arXiv: https://arxiv.org/abs/1810.02215 · HTML: https://mlsanity.com/paper/1810.02215/

## Key facts

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

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

- [Generalized Bayesian Additive Regression Trees Models: Beyond Conditional Conjugacy](https://mlsanity.com/paper/2202.09924/) (2022)
- [Stochastic Tree Ensembles for Estimating Heterogeneous Effects](https://mlsanity.com/paper/2209.06998/) (2022)
- [Very fast Bayesian Additive Regression Trees on GPU](https://mlsanity.com/paper/2410.23244/) (2024)
- [Neural Greedy Pursuit for Feature Selection](https://mlsanity.com/paper/2207.09390/) (2022)

## Abstract (arXiv)

Bayesian additive regression trees (BART) (Chipman et. al., 2010) is a powerful predictive model that often outperforms alternative models at out-of-sample prediction. BART is especially well-suited to settings with unstructured predictor variables and substantial sources of unmeasured variation as is typical in the social, behavioral and health sciences. This paper develops a modified version of BART that is amenable to fast posterior estimation. We present a stochastic hill climbing algorithm that matches the remarkable predictive accuracy of previous BART implementations, but is many times faster and less memory intensive. Simulation studies show that the new method is comparable in computation time and more accurate at function estimation than both random forests and gradient boosting.