Veit D. Wild

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Dino SejdinovicProfessor of Statistical Machine Learning, University of AdelaideVerified email at adelaide.edu.au
Motonobu KanagawaEURECOMVerified email at eurecom.fr
Jeremias KnoblauchAssociate professor & EPSRC Fellow @ University College LondonVerified email at ucl.ac.uk
Sahra GhalebikesabiPhD Candidate, University of OxfordVerified email at univ.ox.ac.uk
Seth Flaxmanassociate professor at OxfordVerified email at cs.ox.ac.uk
Sarah FilippiReader, Imperial College LondonVerified email at imperial.ac.uk
George WynneUniversity of Bristol
Robert Hu

Veit D. Wild

Verified email at stats.ox.ac.uk - Homepage


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Connections and Equivalences between the Nyström Method and Sparse Variational Gaussian Processes VD Wild, M Kanagawa, D Sejdinovic arXiv preprint arXiv:2106.01121, 2021	21	2021
Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep Learning VD Wild, R Hu, D Sejdinovic NeurIPS 2022, 2022	19	2022
A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods VD Wild, S Ghalebikesabi, D Sejdinovic, J Knoblauch NeurIPS 2023, 2023	15	2023
Variational Gaussian Processes: A Functional Analysis View VD Wild, G Wynne International Conference on Artificial Intelligence and Statistics, 4955-4971, 2022	12	2022
Bayesian Kernel Two-Sample Testing Q Zhang, VD Wild, S Filippi, S Flaxman, D Sejdinovic Journal of Computational and Graphical Statistics, 1-24, 2022	9	2022
Near-Optimal Approximations for Bayesian Inference in Function Space V Wild, J Wu, D Sejdinovic, J Knoblauch arXiv preprint arXiv:2502.18279, 2025		2025

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