32 citations · 55 across the 9 of their papers we have counts for
9 papers
On Reconstructing Training Data From Bayesian Posteriors and Trained Models
George Wynne
Publicly releasing the specification of a model with its trained parameters means an adversary can attempt to reconstruct information about the training data via training data reco…
Bayes Hilbert Spaces for Posterior Approximation
George Wynne
Performing inference in Bayesian models requires sampling algorithms to draw samples from the posterior. This becomes prohibitively expensive as the size of data sets increase. Con…
A Fourier representation of kernel Stein discrepancy with application to Goodness-of-Fit tests for measures on infinite dimensional Hilbert spaces
George Wynne, Mikołaj Kasprzak, Andrew B. Duncan
Kernel Stein discrepancy (KSD) is a widely used kernel-based measure of discrepancy between probability measures. It is often employed in the scenario where a user has a collection…
Grassmann Stein Variational Gradient Descent
Xing Liu, Harrison Zhu, Jean-François Ton +2
Stein variational gradient descent (SVGD) is a deterministic particle inference algorithm that provides an efficient alternative to Markov chain Monte Carlo. However, SVGD has been…
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…
Statistical Depth Meets Machine Learning: Kernel Mean Embeddings and Depth in Functional Data Analysis
George Wynne, Stanislav Nagy
Statistical depth is the act of gauging how representative a point is compared to a reference probability measure. The depth allows introducing rankings and orderings to data livin…
A Kernel Two-Sample Test for Functional Data
George Wynne, Andrew B. Duncan
We propose a nonparametric two-sample test procedure based on Maximum Mean Discrepancy (MMD) for testing the hypothesis that two samples of functions have the same underlying distr…
Maximum likelihood estimation and uncertainty quantification for Gaussian process approximation of deterministic functions
Toni Karvonen, George Wynne, Filip Tronarp +2
Despite the ubiquity of the Gaussian process regression model, few theoretical results are available that account for the fact that parameters of the covariance kernel typically ne…
Convergence Guarantees for Gaussian Process Means With Misspecified Likelihoods and Smoothness
George Wynne, François-Xavier Briol, Mark Girolami
Gaussian processes are ubiquitous in machine learning, statistics, and applied mathematics. They provide a flexible modelling framework for approximating functions, whilst simultan…