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Rethinking Trust Region Bayesian Optimization in High Dimensions

arXiv 2604.22967 ↗First posted 24 Apr 2026stat.ML cs.LGcited by 0

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Abstract (arXiv)

Trust Region Bayesian Optimization (TuRBO) is an effective strategy for alleviating the curse of dimensionality in high-dimensional black-box optimization. However, inappropriate lengthscale design can cause the local Gaussian process (GP) model within the trust region to degenerate, leading to suboptimal performance in high dimensions. In this work, we show that TuRBO's local GP may remain either excessively complex or overly simple as the dimension D and trust region side length L vary. To address this issue, we propose a straightforward variant, AdaScale-TuRBO, which scales the GP lengthscale with both the problem dimension and trust region size, thereby preserving kernel geometry and maintaining consistent prior complexity. Empirically, we show that AdaScale-TuRBO can robustly outperform standard TuRBO and other popular high-dimensional BO methods on synthetic benchmarks and real-world trajectory planning tasks.

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References · 25

  1. 2019Scalable Global Optimization via Local Bayesian Optimizationcited by 359
  2. 2023Unexpected Improvements to Expected Improvement for Bayesian Optimizationcited by 144
  3. 2021High-Dimensional Bayesian Optimization with Sparse Axis-Aligned Subspacescited by 139
  4. 2015High Dimensional Bayesian Optimisation and Bandits via Additive Modelscited by 137
  5. 2017Batched Large-scale Bayesian Optimization in High-dimensional Spacescited by 116
  6. 2021Multi-Objective Bayesian Optimization over High-Dimensional Search Spacescited by 87
  7. 2024Vanilla Bayesian Optimization Performs Great in High Dimensionscited by 87
  8. 2020Scalable Constrained Bayesian Optimizationcited by 84
  9. 2021A survey on high-dimensional Gaussian process modeling with application to Bayesian optimizationcited by 76
  10. 2021Local policy search with Bayesian optimizationcited by 37
  11. 2022Monte Carlo Tree Search based Variable Selection for High Dimensional Bayesian Optimizationcited by 37
  12. 2023Are Random Decompositions all we need in High Dimensional Bayesian Optimisation?cited by 35
13 more
  1. 2024Standard Gaussian Process is All You Need for High-Dimensional Bayesian Optimizationcited by 33
  2. 2022Local Bayesian optimization via maximizing probability of descentcited by 29
  3. 2025Understanding High-Dimensional Bayesian Optimizationcited by 19
  4. 2024Minimizing UCB: a Better Local Search Strategy in Local Bayesian Optimizationcited by 9
  5. 2025We Still Don't Understand High-Dimensional Bayesian Optimizationcited by 9
  6. 2025NeST-BO: Fast Local Bayesian Optimization via Newton-Step Targeting of Gradient and Hessian Informationcited by 6
  7. 2023High-dimensional Bayesian Optimization with Group Testingcited by 5
  8. 2026BayeSQP: Bayesian Optimization through Sequential Quadratic Programmingcited by 5
  9. 2024CAGES: Cost-Aware Gradient Entropy Search for Efficient Local Multi-Fidelity Bayesian Optimizationcited by 4
  10. 2024Regional Expected Improvement for Efficient Trust Region Selection in High-Dimensional Bayesian Optimizationcited by 3
  11. 2025Multi-Objective Bayesian Optimization for Networked Black-Box Systems: A Path to Greener Profits and Smarter Designscited by 3
  12. 2025Enhancing Trust-Region Bayesian Optimization via Newton Methodscited by 2
  13. 2025Local Entropy Search over Descent Sequences for Bayesian Optimizationcited by 1

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