@inproceedings{978face127294beb9b0be16c73430bac,
title = "XBART: Accelerated Bayesian Additive Regression Trees",
abstract = "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. {\textcopyright} 2019 by the author(s).",
author = "Jingyu He and Saar Yalov and Hahn, \{P. Richard\}",
year = "2019",
month = apr,
language = "English",
series = "AISTATS - International Conference on Artificial Intelligence and Statistics",
publisher = "PMLR",
pages = "1130--1138",
editor = "Kamalika Chaudhuri and Masashi Sugiyama",
booktitle = "The 22nd International Conference on Artificial Intelligence and Statistics",
note = "22nd International Conference on Artificial Intelligence and Statistics (AISTATS 2019), AISTATS2019 ; Conference date: 16-04-2019 Through 18-04-2019",
url = "https://www.aistats.org/aistats2019/",
}