9 citations · 23 across the 9 of their papers we have counts for
11 papers
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…
Sample-Efficient Optimisation over the Outputs of Generative Models
Samuel Willis, Paul Duckworth, Jack Simons +10
Modern generative AI models, such as diffusion and flow matching models, can sample from rich data distributions. However, many applications, especially in science and engineering,…
Linear combinations of latents in generative models: subspaces and beyond
Erik Bodin, Alexandru Stere, Dragos D. Margineantu +2
Sampling from generative models has become a crucial tool for applications like data synthesis and augmentation. Diffusion, Flow Matching and Continuous Normalising Flows have show…
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…
Black-box density function estimation using recursive partitioning
Erik Bodin, Zhenwen Dai, Neill D. F. Campbell +1
We present a novel approach to Bayesian inference and general Bayesian computation that is defined through a sequential decision loop. Our method defines a recursive partitioning o…
Compositional uncertainty in deep Gaussian processes
Ivan Ustyuzhaninov, Ieva Kazlauskaite, Markus Kaiser +3
Gaussian processes (GPs) are nonparametric priors over functions. Fitting a GP implies computing a posterior distribution of functions consistent with the observed data. Similarly,…
Modulating Surrogates for Bayesian Optimization
Erik Bodin, Markus Kaiser, Ieva Kazlauskaite +3
Bayesian optimization (BO) methods often rely on the assumption that the objective function is well-behaved, but in practice, this is seldom true for real-world objectives even if…
Gaussian Process Deep Belief Networks: A Smooth Generative Model of Shape with Uncertainty Propagation
Alessandro Di Martino, Erik Bodin, Carl Henrik Ek +1
The shape of an object is an important characteristic for many vision problems such as segmentation, detection and tracking. Being independent of appearance, it is possible to gene…
Nonparametric Inference for Auto-Encoding Variational Bayes
Erik Bodin, Iman Malik, Carl Henrik Ek +1
We would like to learn latent representations that are low-dimensional and highly interpretable. A model that has these characteristics is the Gaussian Process Latent Variable Mode…
Latent Gaussian Process Regression
Erik Bodin, Neill D. F. Campbell, Carl Henrik Ek
We introduce Latent Gaussian Process Regression which is a latent variable extension allowing modelling of non-stationary multi-modal processes using GPs. The approach is built on…