Bayesian optimization is a global optimization strategy for (potentially noisy) functions with unknown derivatives. With well-chosen priors, it can find optima with fewer function evaluations than alternatives, making it well suited for the optimization of costly objective functions. Well known examples include hyper-parameter tuning of machine learning models (see e.g. Taking the Human Out of the Loop: A Review of Bayesian Optimization). The Julia package BayesianOptimization.jl currently supports only basic Bayesian optimization methods.

Keywords for this software

Anything in here will be replaced on browsers that support the canvas element

References in zbMATH (referenced in 1 article )

Showing result 1 of 1.
Sorted by year (citations)

  1. Jamie Fairbrother, Christopher Nemeth, Maxime Rischard, Johanni Brea, Thomas Pinder: GaussianProcesses.jl: A Nonparametric Bayes Package for the Julia Language (2022) not zbMATH