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;…
Robust Bayesian Inference for Measurement Error Misspecification: The Berkson and Classical Cases
Charita Dellaporta, Theodoros Damoulas
Measurement error occurs when a covariate influencing a response variable is corrupted by noise. This can lead to misleading inference outcomes, particularly in problems where accu…
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…
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…
Distributionally Robust Optimisation with Bayesian Ambiguity Sets
Charita Dellaporta, Patrick O'Hara, Theodoros Damoulas
Decision making under uncertainty is challenging since the data-generating process (DGP) is often unknown. Bayesian inference proceeds by estimating the DGP through posterior belie…