13 citations · 20 across the 8 of their papers we have counts for
13 papers
Amortized Variational Inference of Latent Intensities from Replicated Point Patterns
Wenqing Liu, Xenia Miscouridou, Déborah Sulem
Bayesian inference for latent point process intensities often relies on per-dataset sampling or optimization, making repeated inference computationally expensive. We propose a neur…
Fair Variable Selection
Deborah Sulem, Jack Jewson
Algorithms are increasingly being used to help automate and improve data-driven decisions, but care must be taken to prevent such algorithms from learning discriminatory patterns f…
Flexible and Scalable Bayesian Modelling of Spatio-Temporal Hawkes Processes
Wenqing Liu, Xenia Miscouridou, Déborah Sulem
Existing spatio-temporal Hawkes process models typically rely on either parametric or semiparametric assumptions, limiting the model's ability to capture complex endogenous and exo…
Posterior concentration in spatio-temporal Hawkes processes
Xenia Miscouridou, Deborah Sulem
We develop a Bayesian nonparametric framework for inference in spatio-temporal Hawkes processes, extending existing theoretical results beyond the purely temporal setting. Our fram…
Bayesian computation for high-dimensional Gaussian Graphical Models with spike-and-slab priors
Deborah Sulem, Jack Jewson, David Rossell
Gaussian graphical models are widely used to infer dependence structures. Bayesian methods are appealing to quantify uncertainty associated with structural learning, i.e., the plau…
Estimation in linear high dimensional Hawkes processes: a Bayesian approach
Judith Rousseau, Vincent Rivoirard, Déborah Sulem
In this paper we study the frequentist properties of Bayesian approaches in linear high dimensional Hawkes processes in a sparse regime where the number of interaction functions ac…