3 papers
stat.ML2026
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;…
cs.LG2025
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…
cs.LG2025
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…