I'm a PhD Student at Imperial College London in the Mathematics Department, Statistics Section.
My email is: [g.wynne18]@[ic.ac.uk] without the braces.
My research interests broadly lie in kernel methods and Gaussian processes applied to functional data, in particular to statistical tests.
The tools I mostly use are
reproducing kernel Hilbert space theory,
scattered data approximation
kernel mean embeddings.
Our session "Probabilistic Numerical Integration" was accepted as a session at Bayesian Young Statisticians Meeting: Online 2020 LINK where
I shall be talking about our paper on the impact of maximum likelihood parameter estimation on uncertainty quantification in Bayesian cubature this had to be cancelled too :(
I'm giving a talk on (you guessed it) kernel based two-sample tests for functional data at the 1st edition of the school in Machine Learning and Dynamic Processes and Time Series Analysis at Scuola Normale Superiore, Pisa LINK
A video on our pre-print A Kernel Two-Sample Test For Functional Data" was just uploaded onto the Fields Institute YouTube channel for the Second Symposium on Machine Learning and Dynamical Systems, video here
Our preprint "A Kernel Two-Sample Test For Functional Data" was just put on arXiv https://arxiv.org/abs/2008.11095
The paper "Maximum likelihood estimation and uncertainty quantification for Gaussian process approximation of deterministic functions" by Toni Karvonen, GW, Filip Tronarp, Chris J. Oates, Simo Särrkä has been accepted at SIAM/ASA Journal of Uncertainty Quantification
Title: A Kernel Two-Sample Test For Functional Data
Authors: GW, Andrew B. Duncan
Title: Maximum likelihood estimation and uncertainty quantification for Gaussian process approximation of deterministic functions
Authors: Toni Karvonen, GW, Filip Tronarp, Chris J. Oates, Simo Särrkä
Type: To appear in SIAM/ASA Journal of Uncertianty Quantification
Title: Convergence Guarantees for Gaussian Process Means with Misspecified Likelihoods and Smoothness
Authors: GW, François-Xavier Briol, Mark Girolami
October 2020: I gave a talk for a CoSInES workshop on applying our functional two-sample testing procedure to scans of steel
September 2020: I am presenting a video at the Second Symposium on Machine Learning and Dynamical Systems
August 2020: I am presenting a poster at the Bernouolli-IMS One World Symposium in the Statistics of Stochastic Processes section
May 2020: I gave a talk at the UCL AI CDT seminar about kernel two-sample tests for functional data
March 2020: I was going to be co-organising two-part minisymposium at SIAM UQ 2020 conference on "Kernel Methods in Uncertainty Quantification" but it got canceled due to Covid-19 :(
Feburary 2020: Presented a poster on kernel two-sample tests for functional data at the Workshop Functional Inference and Machine Intelligence at EURECOM, Antibes
This is note about properties of reproducing kernel Hilbert spaces intended for graduate students which includes a literature guide for textbooks and survey papers. It will be updated as I find the time to add to it. Last updated 18/5/2020