# Are we Forgetting about Compositional Optimisers in Bayesian Optimisation?

arXiv: https://arxiv.org/abs/2012.08240 · HTML: https://mlsanity.com/paper/2012.08240/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Dec 2020.

## Built on by (most cited first, 9 of 9)

- [High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning](https://mlsanity.com/paper/2106.03609/) (2021)
- [GAUCHE: A Library for Gaussian Processes in Chemistry](https://mlsanity.com/paper/2212.04450/) (2022)
- [Are Random Decompositions all we need in High Dimensional Bayesian Optimisation?](https://mlsanity.com/paper/2301.12844/) (2023)
- [AntBO: Towards Real-World Automated Antibody Design with Combinatorial Bayesian Optimisation](https://mlsanity.com/paper/2201.12570/) (2022)
- [Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimization](https://mlsanity.com/paper/2306.09803/) (2023)
- [Sample-Efficient Optimisation with Probabilistic Transformer Surrogates](https://mlsanity.com/paper/2205.13902/) (2022)
- [Applications of Gaussian Processes at Extreme Lengthscales: From Molecules to Black Holes](https://mlsanity.com/paper/2303.14291/) (2023)
- [Scalable Batch Bayesian Optimization Via Subspace Acquisition Functions](https://mlsanity.com/paper/2411.16206/) (2024)
- [Privacy-Aware Collaborative and Distributed Bayesian Optimization](https://mlsanity.com/paper/2607.11600/) (2026)

## Abstract (arXiv)

Bayesian optimisation presents a sample-efficient methodology for global optimisation. Within this framework, a crucial performance-determining subroutine is the maximisation of the acquisition function, a task complicated by the fact that acquisition functions tend to be non-convex and thus nontrivial to optimise. In this paper, we undertake a comprehensive empirical study of approaches to maximise the acquisition function. Additionally, by deriving novel, yet mathematically equivalent, compositional forms for popular acquisition functions, we recast the maximisation task as a compositional optimisation problem, allowing us to benefit from the extensive literature in this field. We highlight the empirical advantages of the compositional approach to acquisition function maximisation across 3958 individual experiments comprising synthetic optimisation tasks as well as tasks from Bayesmark. Given the generality of the acquisition function maximisation subroutine, we posit that the adoption of compositional optimisers has the potential to yield performance improvements across all domains in which Bayesian optimisation is currently being applied.