3 papers
cs.LG2026
Generative Models as Adaptive Trust Regions for Discrete Bayesian Optimisation
Michael Dodds, Erik Bodin, Sebastian W. Ober +3
Bayesian optimisation (BO) is a powerful framework for sample-efficient optimisation of expensive black-box functions, but its application to high-dimensional discrete scientific d…
cs.LG2025
Return of the Latent Space COWBOYS: Re-thinking the use of VAEs for Bayesian Optimisation of Structured Spaces
Henry B. Moss, Sebastian W. Ober, Tom Diethe
Bayesian optimisation in the latent space of a Variational AutoEncoder (VAE) is a powerful framework for optimisation tasks over complex structured domains, such as the space of sc…
cs.LG2025
Big Batch Bayesian Active Learning by Considering Predictive Probabilities
Sebastian W. Ober, Samuel Power, Tom Diethe +1
We observe that BatchBALD, a popular acquisition function for batch Bayesian active learning for classification, can conflate epistemic and aleatoric uncertainty, leading to subopt…