Convergence Guarantees for Gaussian Process Approximations Under Several Observation Models. - George Wynne; François-Xavier Briol; Mark A. Girolami (CoRR, 2020)

Maximum Likelihood Estimation and Uncertainty Quantification for Gaussian Process Approximation of Deterministic Functions. - Toni Karvonen; George Wynne; Filip Tronarp; Chris J. Oates; Simo Särkkä (SIAM/ASA J. Uncertain. Quantification, 2020)

Convergence Guarantees for Gaussian Process Means With Misspecified Likelihoods and Smoothness. - George Wynne; François-Xavier Briol; Mark Girolami (J. Mach. Learn. Res., 2021)

Grassmann Stein Variational Gradient Descent. - Xing Liu; Harrison Zhu; Jean-Francois Ton; George Wynne; Andrew B. Duncan (AISTATS, 2022)

Variational Gaussian Processes: A Functional Analysis View. - George Wynne; Veit Wild (AISTATS, 2022)

A Kernel Two-Sample Test for Functional Data. - George Wynne; Andrew B. Duncan (J. Mach. Learn. Res., 2022)

On Reconstructing Training Data From Bayesian Posteriors and Trained Models. - George Wynne (CoRR, 2025)