# The case for fully Bayesian optimisation in small-sample trials

arXiv: https://arxiv.org/abs/2208.13960 · HTML: https://mlsanity.com/paper/2208.13960/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 31 references that are in the map.
- First posted on arXiv 30 Aug 2022.

## Builds on (most cited first, 7 of 7)

- [Pyro: Deep Universal Probabilistic Programming](https://mlsanity.com/paper/1810.09538/) (2018)
- [Predictive Entropy Search for Efficient Global Optimization of Black-box Functions](https://mlsanity.com/paper/1406.2541/) (2014)
- [High-Dimensional Bayesian Optimization with Sparse Axis-Aligned Subspaces](https://mlsanity.com/paper/2103.00349/) (2021)
- [A survey on policy search algorithms for learning robot controllers in a handful of trials](https://mlsanity.com/paper/1807.02303/) (2018)
- [Theoretical Analysis of Bayesian Optimisation with Unknown Gaussian Process Hyper-Parameters](https://mlsanity.com/paper/1406.7758/) (2014)
- [Approximate Inference for Fully Bayesian Gaussian Process Regression](https://mlsanity.com/paper/1912.13440/) (2019)
- [How Bayesian Should Bayesian Optimisation Be?](https://mlsanity.com/paper/2105.00894/) (2021)

## Abstract (arXiv)

While sample efficiency is the main motive for use of Bayesian optimisation when black-box functions are expensive to evaluate, the standard approach based on type II maximum likelihood (ML-II) may fail and result in disappointing performance in small-sample trials. The paper provides three compelling reasons to adopt fully Bayesian optimisation (FBO) as an alternative. First, failures of ML-II are more commonplace than implied by the existing studies using the contrived settings. Second, FBO is more robust than ML-II, and the price of robustness is almost trivial. Third, FBO has become simple to implement and fast enough to be practical. The paper supports the argument using relevant experiments, which reflect the current practice regarding models, algorithms, and software platforms. Since the benefits seem to outweigh the costs, researchers should consider adopting FBO for their applications so that they can guard against potential failures that end up wasting precious research resources.