# LABCAT: Locally adaptive Bayesian optimization using principal-component-aligned trust regions

arXiv: https://arxiv.org/abs/2311.11328 · HTML: https://mlsanity.com/paper/2311.11328/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 81 references that are in the map.
- First posted on arXiv 19 Nov 2023.

## Builds on (most cited first, 16 of 16)

- [Scalable Global Optimization via Local Bayesian Optimization](https://mlsanity.com/paper/1910.01739/) (2019)
- [COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting](https://mlsanity.com/paper/1603.08785/) (2016)
- [A Literature Survey of Benchmark Functions For Global Optimization Problems](https://mlsanity.com/paper/1308.4008/) (2013)
- [Dealing with Integer-valued Variables in Bayesian Optimization with Gaussian Processes](https://mlsanity.com/paper/1706.03673/) (2017)
- [TREGO: a Trust-Region Framework for Efficient Global Optimization](https://mlsanity.com/paper/2101.06808/) (2021)
- [Time Efficiency in Optimization with a Bayesian-Evolutionary Algorithm](https://mlsanity.com/paper/2005.04166/) (2020)
- [Optimization, fast and slow: optimally switching between local and Bayesian optimization](https://mlsanity.com/paper/1805.08610/) (2018)
- [Comparison of High-Dimensional Bayesian Optimization Algorithms on BBOB](https://mlsanity.com/paper/2303.00890/) (2023)
- [Bayesian Optimization with Exponential Convergence](https://mlsanity.com/paper/1604.01348/) (2016)
- [High Dimensional Bayesian Optimization Assisted by Principal Component Analysis](https://mlsanity.com/paper/2007.00925/) (2020)
- [Bayesian Multi-Scale Optimistic Optimization](https://mlsanity.com/paper/1402.7005/) (2014)
- [Practical Bayesian Optimization for Model Fitting with Bayesian Adaptive Direct Search](https://mlsanity.com/paper/1705.04405/) (2017)
- [Funneled Bayesian Optimization for Design, Tuning and Control of Autonomous Systems](https://mlsanity.com/paper/1610.00366/) (2016)
- [High Dimensional Bayesian Optimization with Kernel Principal Component Analysis](https://mlsanity.com/paper/2204.13753/) (2022)
- [Fast methods for training Gaussian processes on large data sets](https://mlsanity.com/paper/1604.01250/) (2016)
- [Advancing Bayesian Optimization: The Mixed-Global-Local (MGL) Kernel and Length-Scale Cool Down](https://mlsanity.com/paper/1612.03117/) (2016)

## Built on by (most cited first, 2 of 2)

- [Efficient Sliced Wasserstein Distance Computation via Adaptive Bayesian Optimization](https://mlsanity.com/paper/2509.17405/) (2025)
- [LAGO: A Local-Global Optimization Framework Combining Trust Region Methods and Bayesian Optimization](https://mlsanity.com/paper/2603.02970/) (2026)

## Abstract (arXiv)

Bayesian optimization (BO) is a popular method for optimizing expensive black-box functions. BO has several well-documented shortcomings, including computational slowdown with longer optimization runs, poor suitability for non-stationary or ill-conditioned objective functions, and poor convergence characteristics. Several algorithms have been proposed that incorporate local strategies, such as trust regions, into BO to mitigate these limitations; however, none address all of them satisfactorily. To address these shortcomings, we propose the LABCAT algorithm, which extends trust-region-based BO by adding a rotation aligning the trust region with the weighted principal components and an adaptive rescaling strategy based on the length-scales of a local Gaussian process surrogate model with automatic relevance determination. Through extensive numerical experiments using a set of synthetic test functions and the well-known COCO benchmarking software, we show that the LABCAT algorithm outperforms several state-of-the-art BO and other black-box optimization algorithms.