# STREGO: A Scalable Implementation of a Trust-Region Framework for Noisy Bayesian Optimization

arXiv: https://arxiv.org/abs/2610.14338 · HTML: https://mlsanity.com/paper/2610.14338/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 52 references that are in the map.
- First posted on arXiv 9 Oct 2026.

## Builds on (most cited first, 24 of 24)

- [Scalable Global Optimization via Local Bayesian Optimization](https://mlsanity.com/paper/1910.01739/) (2019)
- [High-Dimensional Bayesian Optimization with Sparse Axis-Aligned Subspaces](https://mlsanity.com/paper/2103.00349/) (2021)
- [High Dimensional Bayesian Optimisation and Bandits via Additive Models](https://mlsanity.com/paper/1503.01673/) (2015)
- [COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting](https://mlsanity.com/paper/1603.08785/) (2016)
- [Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement](https://mlsanity.com/paper/2105.08195/) (2021)
- [Vanilla Bayesian Optimization Performs Great in High Dimensions](https://mlsanity.com/paper/2402.02229/) (2024)
- [BOCK : Bayesian Optimization with Cylindrical Kernels](https://mlsanity.com/paper/1806.01619/) (2018)
- [Increasing the Scope as You Learn: Adaptive Bayesian Optimization in Nested Subspaces](https://mlsanity.com/paper/2304.11468/) (2023)
- [Greed is Good: Exploration and Exploitation Trade-offs in Bayesian Optimisation](https://mlsanity.com/paper/1911.12809/) (2019)
- [TREGO: a Trust-Region Framework for Efficient Global Optimization](https://mlsanity.com/paper/2101.06808/) (2021)
- [SMT 2.0: A Surrogate Modeling Toolbox with a focus on Hierarchical and Mixed Variables Gaussian Processes](https://mlsanity.com/paper/2305.13998/) (2023)
- [Stochastic Optimization Using a Trust-Region Method and Random Models](https://mlsanity.com/paper/1504.04231/) (2015)
- [Correcting boundary over-exploration deficiencies in Bayesian optimization with virtual derivative sign observations](https://mlsanity.com/paper/1704.00963/) (2017)
- [An efficient application of Bayesian optimization to an industrial MDO framework for aircraft design](https://mlsanity.com/paper/2006.08434/) (2020)
- [Relaxing the Additivity Constraints in Decentralized No-Regret High-Dimensional Bayesian Optimization](https://mlsanity.com/paper/2305.19838/) (2023)
- [Regional Expected Improvement for Efficient Trust Region Selection in High-Dimensional Bayesian Optimization](https://mlsanity.com/paper/2412.11456/) (2024)
- [A Globally Convergent Evolutionary Strategy for Stochastic Constrained Optimization with Applications to Reinforcement Learning](https://mlsanity.com/paper/2202.10464/) (2022)
- [A Bi-Objective Optimization Based Acquisition Strategy for Batch Bayesian Global Optimization](https://mlsanity.com/paper/2402.00726/) (2024)
- [CMA-ES with Adaptive Reevaluation for Multiplicative Noise](https://mlsanity.com/paper/2405.11471/) (2024)
- [Bencher: Simple and Reproducible Benchmarking for Black-Box Optimization](https://mlsanity.com/paper/2505.21321/) (2025)
- [Rethinking Trust Region Bayesian Optimization in High Dimensions](https://mlsanity.com/paper/2604.22967/) (2026)
- [A Memetic Procedure for Global Multi-Objective Optimization](https://mlsanity.com/paper/2201.11444/) (2022)
- [Gearing Gaussian process modeling and sequential design towards stochastic simulators](https://mlsanity.com/paper/2412.07306/) (2024)
- [Non-monotone direct-search methods for deterministic and stochastic derivative-free optimization](https://mlsanity.com/paper/2609.11567/) (2026)

## Abstract (arXiv)

TREGO, a trust-region Bayesian optimization (BO) method for expensive blackbox problems, has convergence guarantees, but only for exact function values. In high dimension, its performance tends to deteriorate. This paper asks whether a trust-region BO method can be extended to handle noisy evaluations while being competitive in high dimension. We first generalize TREGO into a trust-region framework for noisy BO and extend its convergence analysis to noisy function estimates that are sufficiently accurate with sufficiently high probability. We then propose STREGO, a scalable implementation of this framework. Its global phase selects a batch of candidates by solving a bi-objective problem on the posterior mean of a Gaussian process and the integrated variance reduction. Unlike the predictive variance, this second objective does not favor boundary points merely because they are far from the data. Its local phase minimizes the posterior mean of a second Gaussian process, trained only on points close to the incumbent. STREGO is compared with TREGO, TURBO, BAXUS and NSMA-BO on synthetic and real-world problems of dimension 4 to 124, with and without noise. It gives the best average results in every synthetic setting tested and the best or tied-best solution on four of the six real-world problems. An implementation of STREGO is available at https://github.com/Mayousk/STREGO.