59 citations · 275 across the 37 of their papers we have counts for
15 papers · 1 filter
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;…
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…
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…
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…
Probabilistic sequential matrix factorization
Ömer Deniz Akyildiz, Gerrit J. J. van den Burg, Theodoros Damoulas +1
We introduce the probabilistic sequential matrix factorization (PSMF) method for factorizing time-varying and non-stationary datasets consisting of high-dimensional time-series. In…
Structured Variational Inference in Continuous Cox Process Models
Virginia Aglietti, Edwin V. Bonilla, Theodoros Damoulas +1
We propose a scalable framework for inference in an inhomogeneous Poisson process modeled by a continuous sigmoidal Cox process that assumes the corresponding intensity function is…
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…
Non-linear Multitask Learning with Deep Gaussian Processes
Ayman Boustati, Theodoros Damoulas, Richard S. Savage
We present a multi-task learning formulation for Deep Gaussian processes (DGPs), through non-linear mixtures of latent processes. The latent space is composed of private processes…
Generalized Variational Inference: Three arguments for deriving new Posteriors
Jeremias Knoblauch, Jack Jewson, Theodoros Damoulas
We advocate an optimization-centric view on and introduce a novel generalization of Bayesian inference. Our inspiration is the representation of Bayes' rule as infinite-dimensional…
Spatio-temporal Bayesian On-line Changepoint Detection with Model Selection
Jeremias Knoblauch, Theodoros Damoulas
Bayesian On-line Changepoint Detection is extended to on-line model selection and non-stationary spatio-temporal processes. We propose spatially structured Vector Autoregressions (…
Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with -Divergences
Jeremias Knoblauch, Jack Jewson, Theodoros Damoulas
We present the very first robust Bayesian Online Changepoint Detection algorithm through General Bayesian Inference (GBI) with -divergences. The resulting inference procedure is…
Efficient Inference in Multi-task Cox Process Models
Virginia Aglietti, Theodoros Damoulas, Edwin Bonilla
We generalize the log Gaussian Cox process (LGCP) framework to model multiple correlated point data jointly. The observations are treated as realizations of multiple LGCPs, whose l…