18 citations · 32 across the 6 of their papers we have counts for
6 papers
Deep Gaussian Processes on Directed Acyclic Graphs
Federico L. Perlino, Oliver Hamelijnck, Adam M. Johansen +1
Many real-world processes can be represented as compositions of functions along a directed acyclic graph (DAG). In causal modelling, these correspond to the underlying mechanisms;…
Federated Generalised Variational Inference: A Robust Probabilistic Federated Learning Framework
Terje Mildner, Oliver Hamelijnck, Paris Giampouras +1
We introduce FedGVI, a probabilistic Federated Learning (FL) framework that is robust to both prior and likelihood misspecification. FedGVI addresses limitations in both frequentis…
Physics-Informed Variational State-Space Gaussian Processes
Oliver Hamelijnck, Arno Solin, Theodoros Damoulas
Differential equations are important mechanistic models that are integral to many scientific and engineering applications. With the abundance of available data there has been a gro…
Spatio-Temporal Variational Gaussian Processes
Oliver Hamelijnck, William J. Wilkinson, Niki A. Loppi +2
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…
Transforming Gaussian Processes With Normalizing Flows
Juan Maroñas, Oliver Hamelijnck, Jeremias Knoblauch +1
Gaussian Processes (GPs) can be used as flexible, non-parametric function priors. Inspired by the growing body of work on Normalizing Flows, we enlarge this class of priors through…
Multi-resolution Multi-task Gaussian Processes
Oliver Hamelijnck, Theodoros Damoulas, Kangrui Wang +1
We consider evidence integration from potentially dependent observation processes under varying spatio-temporal sampling resolutions and noise levels. We develop a multi-resolution…