Speaker
Henry Moss
(Lancaster University)
Description
Bayesian optimisation (BO) pairs Gaussian-process surrogates with exploration-aware acquisition rules to locate the optimum of costly, black-box functions in just a handful of trials. In this introductory talk we unpack how GPs supply calibrated uncertainty that powers the explore-exploit trade-off, walk through the classical BO loop and its staple acquisition functions, and outline practical considerations for noisy, constrained, and moderately high-dimensional settings. We then cast an eye to the GenAI era, sketching how BO’s core ideas adapt to this new landscape.
