# Incorporating Expert Prior in Bayesian Optimisation via Space Warping

arXiv: https://arxiv.org/abs/2003.12250 · HTML: https://mlsanity.com/paper/2003.12250/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2020.

## Built on by (most cited first, 23 of 23)

- [PFNs4BO: In-Context Learning for Bayesian Optimization](https://mlsanity.com/paper/2305.17535/) (2023)
- [πBO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization](https://mlsanity.com/paper/2204.11051/) (2022)
- [Looping in the Human Collaborative and Explainable Bayesian Optimization](https://mlsanity.com/paper/2310.17273/) (2023)
- [Language-Based Bayesian Optimization Research Assistant (BORA)](https://mlsanity.com/paper/2501.16224/) (2025)
- [Efficient Black-box Adversarial Attacks via Bayesian Optimization Guided by a Function Prior](https://mlsanity.com/paper/2405.19098/) (2024)
- [Bayesian Optimization Augmented with Actively Elicited Expert Knowledge](https://mlsanity.com/paper/2208.08742/) (2022)
- [HypBO: Accelerating Black-Box Scientific Experiments Using Experts' Hypotheses](https://mlsanity.com/paper/2308.11787/) (2023)
- [Principled Bayesian Optimisation in Collaboration with Human Experts](https://mlsanity.com/paper/2410.10452/) (2024)
- [Hyperparameter Optimization via Interacting with Probabilistic Circuits](https://mlsanity.com/paper/2505.17804/) (2025)
- [Domain Knowledge Injection in Bayesian Search for New Materials](https://mlsanity.com/paper/2311.15162/) (2023)
- [Expert-guided Bayesian Optimisation for Human-in-the-loop Experimental Design of Known Systems](https://mlsanity.com/paper/2312.02852/) (2023)
- [Human-Algorithm Collaborative Bayesian Optimization for Engineering Systems](https://mlsanity.com/paper/2404.10949/) (2024)
- [Bayesian Optimization with Informative Covariance](https://mlsanity.com/paper/2208.02704/) (2022)
- [Dynamic Priors in Bayesian Optimization for Hyperparameter Optimization](https://mlsanity.com/paper/2511.02570/) (2025)
- [Practitioner Motives to Use Different Hyperparameter Optimization Methods](https://mlsanity.com/paper/2203.01717/) (2022)
- [Multi-objective Hyperparameter Optimization in the Age of Deep Learning](https://mlsanity.com/paper/2511.08371/) (2025)
- [Human-in-the-Loop Meta Bayesian Optimization for Fusion Energy and Scientific Applications](https://mlsanity.com/paper/2605.00068/) (2026)
- [Regret Analysis of Guided Diffusion for Black-Box Optimization over Structured Inputs](https://mlsanity.com/paper/2605.10385/) (2026)
- [A Comparative Study of Hyperparameter Tuning Methods](https://mlsanity.com/paper/2408.16425/) (2024)
- [Counterfactual Credit Guided Bayesian Optimization](https://mlsanity.com/paper/2510.04676/) (2025)
- [Elicitation-Augmented Bayesian Optimization](https://mlsanity.com/paper/2605.12079/) (2026)
- [DisCoMBO: Steering Expert-in-the-Loop Black Box Optimization via Distributional Conformance](https://mlsanity.com/paper/2609.36472/) (2026)
- [Harnessing Large Language Models to Compile Task-Relevant Context into Bayesian Optimisation](https://mlsanity.com/paper/2609.36788/) (2026)

## Abstract (arXiv)

Bayesian optimisation is a well-known sample-efficient method for the optimisation of expensive black-box functions. However when dealing with big search spaces the algorithm goes through several low function value regions before reaching the optimum of the function. Since the function evaluations are expensive in terms of both money and time, it may be desirable to alleviate this problem. One approach to subside this cold start phase is to use prior knowledge that can accelerate the optimisation. In its standard form, Bayesian optimisation assumes the likelihood of any point in the search space being the optimum is equal. Therefore any prior knowledge that can provide information about the optimum of the function would elevate the optimisation performance. In this paper, we represent the prior knowledge about the function optimum through a prior distribution. The prior distribution is then used to warp the search space in such a way that space gets expanded around the high probability region of function optimum and shrinks around low probability region of optimum. We incorporate this prior directly in function model (Gaussian process), by redefining the kernel matrix, which allows this method to work with any acquisition function, i.e. acquisition agnostic approach. We show the superiority of our method over standard Bayesian optimisation method through optimisation of several benchmark functions and hyperparameter tuning of two algorithms: Support Vector Machine (SVM) and Random forest.