59 citations · 275 across the 37 of their papers we have counts for
9 papers · 1 filter
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…
Generating Origin-Destination Matrices in Neural Spatial Interaction Models
Ioannis Zachos, Mark Girolami, Theodoros Damoulas
Agent-based models (ABMs) are proliferating as decision-making tools across policy areas in transportation, economics, and epidemiology. In these models, a central object of intere…
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…
Causal Entropy Optimization
Nicola Branchini, Virginia Aglietti, Neil Dhir +1
We study the problem of globally optimizing the causal effect on a target variable of an unknown causal graph in which interventions can be performed. This problem arises in many a…
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…
An Expectation-Based Network Scan Statistic for a COVID-19 Early Warning System
Chance Haycock, Edward Thorpe-Woods, James Walsh +4
One of the Greater London Authority's (GLA) response to the COVID-19 pandemic brings together multiple large-scale and heterogeneous datasets capturing mobility, transportation and…
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…
Variational Autoencoding of PDE Inverse Problems
Daniel J. Tait, Theodoros Damoulas
Specifying a governing physical model in the presence of missing physics and recovering its parameters are two intertwined and fundamental problems in science. Modern machine learn…
Distribution Regression for Sequential Data
Maud Lemercier, Cristopher Salvi, Theodoros Damoulas +2
Distribution regression refers to the supervised learning problem where labels are only available for groups of inputs instead of individual inputs. In this paper, we develop a rig…