5 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…
Acquisition Function-Guided Generation for Bayesian Optimization over Discrete Spaces
Tommaso Ancilli, Thomas Wedenig, Henry Moss +1
Bayesian optimization (BO) over discrete spaces is challenging due to missing gradients and the sparsity of valid solutions. Latent-space BO sidesteps both issues by fitting a surr…
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…
Search at the Cost of Sampling: Nearly-Instant Latent Space Bayesian Optimization
Donney Fan, Colin Doumont, Aleksandra Kalisz +4
Generative models are increasingly central to many de novo discovery pipelines, in which designs are generated at scale and filtered through virtual screens to determine a set of c…
How to Spend Your Oracle Budget: Practical Guidance for Protein Structure Prediction Models
Aleksandra Kalisz, Jack Simons, Krisztina Sinkovics +4
Foundation models for protein structure prediction remain unreliable on certain targets. External oracles can flag and correct these failures, but biological oracles are expensive,…