VaRT: Variational Regression Trees
Abstract
Decision trees are a well-established tool in machine learning for classification and regression tasks. In this paper, we introduce a novel non-parametric Bayesian model that uses variational inference to approximate a posterior distribution over the space of stochastic decision trees. We evaluate the model's performance on 18 datasets and demonstrate its competitiveness with other state-of-the-art methods in regression tasks. We also explore its application to causal inference problems. We provide a fully vectorized implementation of our algorithm in PyTorch.
Cite
Text
Salazar. "VaRT: Variational Regression Trees." Neural Information Processing Systems, 2023.Markdown
[Salazar. "VaRT: Variational Regression Trees." Neural Information Processing Systems, 2023.](https://mlanthology.org/neurips/2023/salazar2023neurips-vart/)BibTeX
@inproceedings{salazar2023neurips-vart,
title = {{VaRT: Variational Regression Trees}},
author = {Salazar, Sebastian},
booktitle = {Neural Information Processing Systems},
year = {2023},
url = {https://mlanthology.org/neurips/2023/salazar2023neurips-vart/}
}