# High-Dimensional Bayesian Optimization via Tree-Structured Additive Models

arXiv: https://arxiv.org/abs/2012.13088 · HTML: https://mlsanity.com/paper/2012.13088/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Dec 2020.

## Built on by (most cited first, 22 of 22)

- [Vanilla Bayesian Optimization Performs Great in High Dimensions](https://mlsanity.com/paper/2402.02229/) (2024)
- [Monte Carlo Tree Search based Variable Selection for High Dimensional Bayesian Optimization](https://mlsanity.com/paper/2210.01628/) (2022)
- [Are Random Decompositions all we need in High Dimensional Bayesian Optimisation?](https://mlsanity.com/paper/2301.12844/) (2023)
- [Standard Gaussian Process is All You Need for High-Dimensional Bayesian Optimization](https://mlsanity.com/paper/2402.02746/) (2024)
- [A survey and benchmark of high-dimensional Bayesian optimization of discrete sequences](https://mlsanity.com/paper/2406.04739/) (2024)
- [Understanding High-Dimensional Bayesian Optimization](https://mlsanity.com/paper/2502.09198/) (2025)
- [Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spaces](https://mlsanity.com/paper/2207.00879/) (2022)
- [We Still Don't Understand High-Dimensional Bayesian Optimization](https://mlsanity.com/paper/2512.00170/) (2025)
- [Minimizing UCB: a Better Local Search Strategy in Local Bayesian Optimization](https://mlsanity.com/paper/2405.15285/) (2024)
- [High Dimensional Bayesian Optimization using Lasso Variable Selection](https://mlsanity.com/paper/2504.01743/) (2025)
- [Practical First-Order Bayesian Optimization Algorithms](https://mlsanity.com/paper/2306.10815/) (2023)
- [Enhancing Trust-Region Bayesian Optimization via Newton Methods](https://mlsanity.com/paper/2508.18423/) (2025)
- [Do We Really Need to Approach the Entire Pareto Front in Many-Objective Bayesian Optimisation?](https://mlsanity.com/paper/2604.09417/) (2026)
- [High-dimensional Level Set Estimation with Trust Regions and Double Acquisition Functions](https://mlsanity.com/paper/2602.12391/) (2026)
- [Optimistic Optimization of Gaussian Process Samples](https://mlsanity.com/paper/2209.00895/) (2022)
- [Dependency Structure Search Bayesian Optimization for Decision Making Models](https://mlsanity.com/paper/2308.00629/) (2023)
- [Swine Diet Design using Multi-objective Regionalized Bayesian Optimization](https://mlsanity.com/paper/2409.12919/) (2024)
- [On Thompson Sampling and Bilateral Uncertainty in Additive Bayesian Optimization](https://mlsanity.com/paper/2510.11792/) (2025)
- [Leveraging Discrete Function Decomposability for Scientific Design](https://mlsanity.com/paper/2511.03032/) (2025)
- [Converge Then Diversify: Decoupling Convergence and Diversity in Multi-Objective Bayesian Optimisation](https://mlsanity.com/paper/2609.13396/) (2026)
- [Agentic High-Dimensional Bayesian Optimization with Hypothesis- and Evidence-Guided Search](https://mlsanity.com/paper/2609.34281/) (2026)
- [Human-inspired, Task-Dimension-Guided Exploration for Efficient Learning in High Dimensions](https://mlsanity.com/paper/2609.36672/) (2026)

## Abstract (arXiv)

Bayesian Optimization (BO) has shown significant success in tackling expensive low-dimensional black-box optimization problems. Many optimization problems of interest are high-dimensional, and scaling BO to such settings remains an important challenge. In this paper, we consider generalized additive models in which low-dimensional functions with overlapping subsets of variables are composed to model a high-dimensional target function. Our goal is to lower the computational resources required and facilitate faster model learning by reducing the model complexity while retaining the sample-efficiency of existing methods. Specifically, we constrain the underlying dependency graphs to tree structures in order to facilitate both the structure learning and optimization of the acquisition function. For the former, we propose a hybrid graph learning algorithm based on Gibbs sampling and mutation. In addition, we propose a novel zooming-based algorithm that permits generalized additive models to be employed more efficiently in the case of continuous domains. We demonstrate and discuss the efficacy of our approach via a range of experiments on synthetic functions and real-world datasets.