125 citations · 203 across the 16 of their papers we have counts for
16 papers
A Probabilistic Model for Aircraft in Climb using Monotonic Functional Gaussian Process Emulators
Nick Pepper, Marc Thomas, George De Ath +4
Ensuring vertical separation is a key means of maintaining safe separation between aircraft in congested airspace. Aircraft trajectories are modelled in the presence of significant…
Stochastic Downscaling to Chaotic Weather Regimes using Spatially Conditioned Gaussian Random Fields with Adaptive Covariance
Rachel Prudden, Niall Robinson, Peter Challenor +1
Downscaling aims to link the behaviour of the atmosphere at fine scales to properties measurable at coarser scales, and has the potential to provide high resolution information at…
How Bayesian Should Bayesian Optimisation Be?
George De Ath, Richard Everson, Jonathan Fieldsend
Bayesian optimisation (BO) uses probabilistic surrogate models - usually Gaussian processes (GPs) - for the optimisation of expensive black-box functions. At each BO iteration, the…
Asynchronous ε-Greedy Bayesian Optimisation
George De Ath, Richard M. Everson, Jonathan E. Fieldsend
Batch Bayesian optimisation (BO) is a successful technique for the optimisation of expensive black-box functions. Asynchronous BO can reduce wallclock time by starting a new evalua…
What do you Mean? The Role of the Mean Function in Bayesian Optimisation
George De Ath, Jonathan E. Fieldsend, Richard M. Everson
Bayesian optimisation is a popular approach for optimising expensive black-box functions. The next location to be evaluated is selected via maximising an acquisition function that…
-shotgun: -greedy Batch Bayesian Optimisation
George De Ath, Richard M. Everson, Jonathan E. Fieldsend +1
Bayesian optimisation is a popular, surrogate model-based approach for optimising expensive black-box functions. Given a surrogate model, the next location to expensively evaluate…