no code implementations • ICML 2020 • Kangrui Wang, Oliver Hamelijnck, Theodoros Damoulas, Mark Steel
We describe a framework for constructing non-separable non-stationary random fields that is based on an infinite mixture of convolved stochastic processes.
1 code implementation • NeurIPS 2021 • Oliver Hamelijnck, William J. Wilkinson, Niki A. Loppi, Arno Solin, Theodoros Damoulas
We introduce a scalable approach to Gaussian process inference that combines spatio-temporal filtering with natural gradient variational inference, resulting in a non-conjugate GP method for multivariate data that scales linearly with respect to time.
1 code implementation • 3 Nov 2020 • Juan Maroñas, Oliver Hamelijnck, Jeremias Knoblauch, Theodoros Damoulas
Gaussian Processes (GPs) can be used as flexible, non-parametric function priors.
1 code implementation • NeurIPS 2019 • Oliver Hamelijnck, Theodoros Damoulas, Kangrui Wang, Mark Girolami
We consider evidence integration from potentially dependent observation processes under varying spatio-temporal sampling resolutions and noise levels.