# Expert-guided Bayesian Optimisation for Human-in-the-loop Experimental Design of Known Systems

arXiv: https://arxiv.org/abs/2312.02852 · HTML: https://mlsanity.com/paper/2312.02852/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 12 references that are in the map.
- First posted on arXiv 5 Dec 2023.

## Builds on (most cited first, 5 of 5)

- [Batch Bayesian Optimization via Local Penalization](https://mlsanity.com/paper/1505.08052/) (2015)
- [πBO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization](https://mlsanity.com/paper/2204.11051/) (2022)
- [Discovering Many Diverse Solutions with Bayesian Optimization](https://mlsanity.com/paper/2210.10953/) (2022)
- [Incorporating Expert Prior in Bayesian Optimisation via Space Warping](https://mlsanity.com/paper/2003.12250/) (2020)
- [BO-Muse: A human expert and AI teaming framework for accelerated experimental design](https://mlsanity.com/paper/2303.01684/) (2023)

## Built on by (most cited first, 3 of 3)

- [Language-Based Bayesian Optimization Research Assistant (BORA)](https://mlsanity.com/paper/2501.16224/) (2025)
- [How Useful is Intermittent, Asynchronous Expert Feedback for Bayesian Optimization?](https://mlsanity.com/paper/2406.06459/) (2024)
- [Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial](https://mlsanity.com/paper/2604.01328/) (2026)

## Abstract (arXiv)

Domain experts often possess valuable physical insights that are overlooked in fully automated decision-making processes such as Bayesian optimisation. In this article we apply high-throughput (batch) Bayesian optimisation alongside anthropological decision theory to enable domain experts to influence the selection of optimal experiments. Our methodology exploits the hypothesis that humans are better at making discrete choices than continuous ones and enables experts to influence critical early decisions. At each iteration we solve an augmented multi-objective optimisation problem across a number of alternate solutions, maximising both the sum of their utility function values and the determinant of their covariance matrix, equivalent to their total variability. By taking the solution at the knee point of the Pareto front, we return a set of alternate solutions at each iteration that have both high utility values and are reasonably distinct, from which the expert selects one for evaluation. We demonstrate that even in the case of an uninformed practitioner, our algorithm recovers the regret of standard Bayesian optimisation.