59 citations · 275 across the 37 of their papers we have counts for
38 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…
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…
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…
Table inference for combinatorial origin-destination choices in agent-based population synthesis
Ioannis Zachos, Theodoros Damoulas, Mark Girolami
A key challenge in agent-based mobility simulations is the synthesis of individual agent socioeconomic profiles. Such profiles include locations of agent activities, which dictate…
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…
Quantifying Consistency and Information Loss for Causal Abstraction Learning
Fabio Massimo Zennaro, Paolo Turrini, Theodoros Damoulas
Structural causal models provide a formalism to express causal relations between variables of interest. Models and variables can represent a system at different levels of abstracti…
Jointly Learning Consistent Causal Abstractions Over Multiple Interventional Distributions
Fabio Massimo Zennaro, Máté Drávucz, Geanina Apachitei +2
An abstraction can be used to relate two structural causal models representing the same system at different levels of resolution. Learning abstractions which guarantee consistency…
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…
Towards Computing an Optimal Abstraction for Structural Causal Models
Fabio Massimo Zennaro, Paolo Turrini, Theodoros Damoulas
Working with causal models at different levels of abstraction is an important feature of science. Existing work has already considered the problem of expressing formally the relati…
Uncertainty-aware deep learning methods for robust diabetic retinopathy classification
Joel Jaskari, Jaakko Sahlsten, Theodoros Damoulas +5
Automatic classification of diabetic retinopathy from retinal images has been widely studied using deep neural networks with impressive results. However, there is a clinical need f…
Robust Bayesian Inference for Simulator-based Models via the MMD Posterior Bootstrap
Charita Dellaporta, Jeremias Knoblauch, Theodoros Damoulas +1
Simulator-based models are models for which the likelihood is intractable but simulation of synthetic data is possible. They are often used to describe complex real-world phenomena…
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…
Dynamic Causal Bayesian Optimization
Virginia Aglietti, Neil Dhir, Javier González +1
This paper studies the problem of performing a sequence of optimal interventions in a causal dynamical system where both the target variable of interest and the inputs evolve over…
Higher Order Kernel Mean Embeddings to Capture Filtrations of Stochastic Processes
Cristopher Salvi, Maud Lemercier, Chong Liu +3
Stochastic processes are random variables with values in some space of paths. However, reducing a stochastic process to a path-valued random variable ignores its filtration, i.e. t…
A variational Bayesian spatial interaction model for estimating revenue and demand at business facilities
Shanaka Perera, Virginia Aglietti, Theodoros Damoulas
We study the problem of estimating potential revenue or demand at business facilities and understanding its generating mechanism. This problem arises in different fields such as op…
SigGPDE: Scaling Sparse Gaussian Processes on Sequential Data
Maud Lemercier, Cristopher Salvi, Thomas Cass +3
Making predictions and quantifying their uncertainty when the input data is sequential is a fundamental learning challenge, recently attracting increasing attention. We develop Sig…
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…
Near Real-Time Social Distance Estimation in London
James Walsh, Oluwafunmilola Kesa, Andrew Wang +6
During the COVID-19 pandemic, policy makers at the Greater London Authority, the regional governance body of London, UK, are reliant upon prompt and accurate data sources. Large we…
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-task Causal Learning with Gaussian Processes
Virginia Aglietti, Theodoros Damoulas, Mauricio Álvarez +1
This paper studies the problem of learning the correlation structure of a set of intervention functions defined on the directed acyclic graph (DAG) of a causal model. This is usefu…
Exoplanet Validation with Machine Learning: 50 new validated Kepler planets
David J. Armstrong, Jevgenij Gamper, Theodoros Damoulas
Over 30% of the ~4000 known exoplanets to date have been discovered using 'validation', where the statistical likelihood of a transit arising from a false positive (FP), non-planet…
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…