5 papers
Boundary Variance Inflation Causes Acquisition Bias in Gaussian Processes
Maria Bånkestad, Sanna Jarl, Jens Sjölund
Gaussian processes with stationary kernels on bounded domains exhibit inflated posterior variance near the boundary. Despite being a long-recognized artifact in geostatistics and a…
A differentiable machine learning small-angle X-ray scattering analysis framework for structure elucidation of lipid nanoparticles
Maria Bånkestad, Sandra Barman, Magnus Röding +11
Lipid nanoparticles (LNPs) are efficient delivery systems for negatively charged nucleic acids. Their multi-component architecture yields a core-shell structure. Small-angle X-ray…
Observation-dependent Bayesian active learning via input-warped Gaussian processes
Sanna Jarl, Maria BÃ¥nkestad, Jonathan J. S. Scragg +1
Bayesian active learning relies on the precise quantification of predictive uncertainty to explore unknown function landscapes. While Gaussian process surrogates are the standard f…
Ising on the Graph: Task-specific Graph Subsampling via the Ising Model
Maria BÃ¥nkestad, Jennifer R. Andersson, Sebastian Mair +1
Reducing a graph while preserving its overall properties is an important problem with many applications. Typically, reduction approaches either remove edges (sparsification) or mer…
Flexible SE(2) graph neural networks with applications to PDE surrogates
Maria BÃ¥nkestad, Olof Mogren, Aleksis Pirinen
This paper presents a novel approach for constructing graph neural networks equivariant to 2D rotations and translations and leveraging them as PDE surrogates on non-gridded domain…