# Data-driven Prior Learning for Bayesian Optimisation

arXiv: https://arxiv.org/abs/2311.14653 · HTML: https://mlsanity.com/paper/2311.14653/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 17 references that are in the map.
- First posted on arXiv 24 Nov 2023.

## Builds on (most cited first, 10 of 10)

- [Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro](https://mlsanity.com/paper/1912.11554/) (2019)
- [PFNs4BO: In-Context Learning for Bayesian Optimization](https://mlsanity.com/paper/2305.17535/) (2023)
- [Few-Shot Bayesian Optimization with Deep Kernel Surrogates](https://mlsanity.com/paper/2101.07667/) (2021)
- [Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning](https://mlsanity.com/paper/1909.12552/) (2019)
- [Approximate Inference for Fully Bayesian Gaussian Process Regression](https://mlsanity.com/paper/1912.13440/) (2019)
- [Transfer Learning for Bayesian Optimization: A Survey](https://mlsanity.com/paper/2302.05927/) (2023)
- [What do you Mean? The Role of the Mean Function in Bayesian Optimisation](https://mlsanity.com/paper/2004.08349/) (2020)
- [Bayesian Optimisation for Active Monitoring of Air Pollution](https://mlsanity.com/paper/2202.07595/) (2022)
- [Pre-training helps Bayesian optimization too](https://mlsanity.com/paper/2207.03084/) (2022)
- [Optimising Placement of Pollution Sensors in Windy Environments](https://mlsanity.com/paper/2012.10770/) (2020)

## Built on by (most cited first, 1 of 1)

- [Transfer Learning in Bayesian Optimization for Aircraft Design](https://mlsanity.com/paper/2603.28999/) (2026)

## Abstract (arXiv)

Transfer learning for Bayesian optimisation has generally assumed a strong similarity between optimisation tasks, with at least a subset having similar optimal inputs. This assumption can reduce computational costs, but it is violated in a wide range of optimisation problems where transfer learning may nonetheless be useful. We replace this assumption with a weaker one only requiring the shape of the optimisation landscape to be similar, and analyse the recent method Prior Learning for Bayesian Optimisation - PLeBO - in this setting. By learning priors for the hyperparameters of the Gaussian process surrogate model we can better approximate the underlying function, especially for few function evaluations. We validate the learned priors and compare to a breadth of transfer learning approaches, using synthetic data and a recent air pollution optimisation problem as benchmarks. We show that PLeBO and prior transfer find good inputs in fewer evaluations.