# Preferential Bayesian optimisation with skew gaussian processes

@article{Benavoli2021PreferentialBO, title={Preferential Bayesian optimisation with skew gaussian processes}, author={A. Benavoli and Dario Azzimonti and D. Piga}, journal={Proceedings of the Genetic and Evolutionary Computation Conference Companion}, year={2021} }

Preferential Bayesian optimisation (PBO) deals with optimisation problems where the objective function can only be accessed via preference judgments, such as "this is better than that" between two candidate solutions (like in A/B tests). The state-of-the-art approach to PBO uses a Gaussian process to model the preference function and a Bernoulli likelihood to model the observed pair-wise comparisons. Laplace's method is then employed to compute posterior inferences and, in particular, to buildβ¦Β Expand

#### 2 Citations

A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with Skew Gaussian Processes

- Mathematics, Computer Science
- ArXiv
- 2020

It is proved that SkewGP is conjugate with both the normal and affine probit likelihood, and more in general, with their product, which allows to handle classification, preference, numeric and ordinal regression, and mixed problems in a unified framework. Expand

Bayesian Optimisation for Sequential Experimental Design with Applications in Additive Manufacturing

- Computer Science
- ArXiv
- 2021

This work aims to bring attention to the benefits of applying BO in designing experiments and to provide a BO manual, covering both methodology and software, for the convenience of anyone who wants to apply or learn BO. Expand

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