18 citations · 80 across the 22 of their papers we have counts for
25 papers
Neural network surrogates with uncertainty quantification for inverse problems in partial differential equations
Christian Jimenez-Beltran, Aretha L. Teckentrup, Antonio Vergari +1
Inverse problems for differential equations arise throughout science and engineering, where one seeks to infer unknown model parameters from noisy or incomplete observations. Tradi…
Asymptotic and pre-asymptotic convergence of sparse grids for anisotropic kernel interpolation
Elliot J. Addy, Aretha L. Teckentrup
Sparse grids are popular tools for high-dimensional function approximation. In this work, we study the use of sparse grids for interpolation using separable Matérn kernels $Φ_{\bol…
Lengthscale-informed sparse grids for kernel methods in high dimensions
Elliot J. Addy, Jonas Latz, Aretha L. Teckentrup
Kernel interpolation, especially in the context of Gaussian process emulation, is a widely used technique in surrogate modelling, where the goal is to cheaply approximate an input-…
Sparse Techniques for Regression in Deep Gaussian Processes
Jonas Latz, Aretha L. Teckentrup, Simon Urbainczyk
Gaussian processes (GPs) have gained popularity as flexible machine learning models for regression and function approximation with an in-built method for uncertainty quantification…
Deep Gaussian Process Priors for Bayesian Image Reconstruction
Jonas Latz, Aretha L. Teckentrup, Simon Urbainczyk
In image reconstruction, an accurate quantification of uncertainty is of great importance for informed decision making. Here, the Bayesian approach to inverse problems can be used:…
Probabilistic Richardson Extrapolation
Chris. J. Oates, Toni Karvonen, Aretha L. Teckentrup +2
For over a century, extrapolation methods have provided a powerful tool to improve the convergence order of a numerical method. However, these tools are not well-suited to modern c…