# Using BART to Perform Pareto Optimization and Quantify its Uncertainties

arXiv: https://arxiv.org/abs/2101.02558 · HTML: https://mlsanity.com/paper/2101.02558/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 29 references that are in the map.
- First posted on arXiv 4 Jan 2021.

## Builds on (most cited first, 1 of 1)

- [Assessing variable activity for Bayesian regression trees](https://mlsanity.com/paper/2005.13622/) (2020)

## Abstract (arXiv)

Techniques to reduce the energy burden of an industrial ecosystem often require solving a multiobjective optimization problem. However, collecting experimental data can often be either expensive or time-consuming. In such cases, statistical methods can be helpful. This article proposes Pareto Front (PF) and Pareto Set (PS) estimation methods using Bayesian Additive Regression Trees (BART), which is a non-parametric model whose assumptions are typically less restrictive than popular alternatives, such as Gaussian Processes (GPs). These less restrictive assumptions allow BART to handle scenarios (e.g. high-dimensional input spaces, nonsmooth responses, large datasets) that GPs find difficult. The performance of our BART-based method is compared to a GP-based method using analytic test functions, demonstrating convincing advantages. Finally, our BART-based methodology is applied to a motivating engineering problem. Supplementary materials, which include a theorem proof, algorithms, and R code, for this article are available online.