5 papers
Iterative solvers for partial differential equations with dissipative structure: Operator preconditioning and optimal control
Volker Mehrmann, Manuel Schaller, Martin Stoll
This work considers the iterative solution of large-scale problems subject to non-symmetric matrices or operators arising in discretizations of (port-)Hamiltonian partial different…
KEEP: Integrating Medical Ontologies with Clinical Data for Robust Code Embeddings
Ahmed Elhussein, Paul Meddeb, Abigail Newbury +3
Machine learning in healthcare requires effective representation of structured medical codes, but current methods face a trade off: knowledge graph based approaches capture formal…
Fast and Simple Multiclass Data Segmentation: An Eigendecomposition and Projection-Free Approach
Chiara Faccio, Margherita Porcelli, Francesco Rinaldi +1
Graph-based machine learning has seen an increased interest over the last decade with many connections to other fields of applied mathematics. Learning based on partial differentia…
Low-rank computation of the posterior mean in Multi-Output Gaussian Processes
Sebastian Esche, Martin Stoll
Gaussian processes (GP) are a versatile tool in machine learning and computational science. We here consider the case of multi-output Gaussian processes (MOGP) and present low-rank…
Preconditioned Additive Gaussian Processes with Fourier Acceleration
Theresa Wagner, Tianshi Xu, Franziska Nestler +2
Gaussian processes (GPs) are crucial in machine learning for quantifying uncertainty in predictions. However, their associated covariance matrices, defined by kernel functions, are…