18 citations · 80 across the 22 of their papers we have counts for
3 papers · 1 filter
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…
Gaussian processes for Bayesian inverse problems associated with linear partial differential equations
Tianming Bai, Aretha L. Teckentrup, Konstantinos C. Zygalakis
This work is concerned with the use of Gaussian surrogate models for Bayesian inverse problems associated with linear partial differential equations. A particular focus is on the r…
Introduction To Gaussian Process Regression In Bayesian Inverse Problems, With New ResultsOn Experimental Design For Weighted Error Measures
Tapio Helin, Andrew Stuart, Aretha Teckentrup +1
Bayesian posterior distributions arising in modern applications, including inverse problems in partial differential equation models in tomography and subsurface flow, are often com…