3 citations · 5 across the 5 of their papers we have counts for
5 papers
Near-Optimal Approximations for Bayesian Inference in Function Space
Veit Wild, James Wu, Dino Sejdinovic +1
We propose a scalable inference algorithm for Bayes posteriors defined on a reproducing kernel Hilbert space (RKHS). Given a likelihood function and a Gaussian random element repre…
A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods
Veit David Wild, Sahra Ghalebikesabi, Dino Sejdinovic +1
We establish the first mathematically rigorous link between Bayesian, variational Bayesian, and ensemble methods. A key step towards this it to reformulate the non-convex optimisat…
Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep Learning
Veit D. Wild, Robert Hu, Dino Sejdinovic
We develop a framework for generalized variational inference in infinite-dimensional function spaces and use it to construct a method termed Gaussian Wasserstein inference (GWI). G…
Variational Gaussian Processes: A Functional Analysis View
Veit Wild, George Wynne
Variational Gaussian process (GP) approximations have become a standard tool in fast GP inference. This technique requires a user to select variational features to increase efficie…
Connections and Equivalences between the Nyström Method and Sparse Variational Gaussian Processes
Veit Wild, Motonobu Kanagawa, Dino Sejdinovic
We investigate the connections between sparse approximation methods for making kernel methods and Gaussian processes (GPs) scalable to large-scale data, focusing on the Nyström met…