3 papers
cs.RO2026
A Distributed Gaussian Process Model for Multi-Robot Mapping
Seth Nabarro, Mark van der Wilk, Andrew J. Davison
We propose DistGP: a multi-robot learning method for collaborative learning of a global function using only local experience and computation. We utilise a sparse Gaussian process (…
cs.LG2023
Learning in Deep Factor Graphs with Gaussian Belief Propagation
Seth Nabarro, Mark van der Wilk, Andrew J Davison
We propose an approach to do learning in Gaussian factor graphs. We treat all relevant quantities (inputs, outputs, parameters, latents) as random variables in a graphical model, a…
stat.ML2018
Spatiotemporal Prediction of Ambulance Demand using Gaussian Process Regression
Seth Nabarro, Tristan Fletcher, John Shawe-Taylor
Accurately predicting when and where ambulance call-outs occur can reduce response times and ensure the patient receives urgent care sooner. Here we present a novel method for ambu…