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L. M. Kreusser

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#artificial intelligence Preprint Oct 2026

SABLE: Scalable Affine Bayesian Latent-space Exploration

It is shown that the off-span norm implicitly selected by affine EI can lead to underexploration, and SABLE instead prescribes it using a finite-horizon schedule, without restricting search to a learned subspace.

Erik Bodin, L. Kreusser, Donney Fan et al. · 0 citations
Sep 2026

Learning Efficient and Provably Convergent Symmetric Splitting Methods

Abstract. Splitting methods are widely used for solving initial value problems (IVPs) due to their ability to simplify complicated evolutions into more manageable subproblems. These subproblems can be solved efficiently and accurately, leveraging properties like linearity, sparsity, and reduced stiffness. Traditionall...

L. M. Kreusser, Henry E. Lockyer, E. Müller et al. · 0 citations

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