9 citations · 23 across the 9 of their papers we have counts for
3 papers · 1 filter
SABLE: Scalable Affine Bayesian Latent-space Exploration
Erik Bodin, Lisa Maria Kreusser, Donney Fan +3
Complex objects such as engineering designs and proteins are often difficult to optimise because their design spaces exhibit high-dimensional structure and objective evaluations ar…
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…
Making Differentiable Architecture Search less local
Erik Bodin, Federico Tomasi, Zhenwen Dai
Neural architecture search (NAS) is a recent methodology for automating the design of neural network architectures. Differentiable neural architecture search (DARTS) is a promising…