12 citations · 36 across the 10 of their papers we have counts for
11 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…
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…
Dynamic sparse graphs with overlapping communities
Xenia Miscouridou, Francesca Panero, Antreas Laos
Dynamic community detection concerns inferring how community memberships evolve over time, including the emergence, persistence, merging, and dissolution of groups in temporal netw…
Concept Reachability in Diffusion Models: Beyond Dataset Constraints
Marta Aparicio Rodriguez, Xenia Miscouridou, Anastasia Borovykh
Despite significant advances in quality and complexity of the generations in text-to-image models, prompting does not always lead to the desired outputs. Controlling model behaviou…
Cox-Hawkes: doubly stochastic spatiotemporal Poisson processes
Xenia Miscouridou, Samir Bhatt, George Mohler +2
Hawkes processes are point process models that have been used to capture self-excitatory behavior in social interactions, neural activity, earthquakes and viral epidemics. They can…
Unifying incidence and prevalence under a time-varying general branching process
Mikko S. Pakkanen, Xenia Miscouridou, Matthew J. Penn +5
Renewal equations are a popular approach used in modelling the number of new infections, i.e., incidence, in an outbreak. We develop a stochastic model of an outbreak based on a ti…
Gaussian Process Nowcasting: Application to COVID-19 Mortality Reporting
Iwona Hawryluk, Henrique Hoeltgebaum, Swapnil Mishra +7
Updating observations of a signal due to the delays in the measurement process is a common problem in signal processing, with prominent examples in a wide range of fields. An impor…
Relaxed-Responsibility Hierarchical Discrete VAEs
Matthew Willetts, Xenia Miscouridou, Stephen Roberts +1
Successfully training Variational Autoencoders (VAEs) with a hierarchy of discrete latent variables remains an area of active research. Vector-Quantised VAEs are a powerful approac…
A unified construction for series representations and finite approximations of completely random measures
Juho Lee, Xenia Miscouridou, François Caron
Infinite-activity completely random measures (CRMs) have become important building blocks of complex Bayesian nonparametric models. They have been successfully used in various appl…
Modelling sparsity, heterogeneity, reciprocity and community structure in temporal interaction data
Xenia Miscouridou, François Caron, Yee Whye Teh
We propose a novel class of network models for temporal dyadic interaction data. Our goal is to capture a number of important features often observed in social interactions: sparsi…