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…
Estimating the history of a random recursive tree
Simon Briend, Christophe Giraud, Gábor Lugosi +1
This paper studies the problem of estimating the order of arrival of the vertices in a random recursive tree. Specifically, we study two fundamental models: the uniform attachment…
An Empirical Study of Self-supervised Learning with Wasserstein Distance
Makoto Yamada, Yuki Takezawa, Guillaume Houry +4
In this study, we delve into the problem of self-supervised learning (SSL) utilizing the 1-Wasserstein distance on a tree structure (a.k.a., Tree-Wasserstein distance (TWD)), where…
Scalable and adaptive variational Bayes methods for Hawkes processes
Deborah Sulem, Vincent Rivoirard, Judith Rousseau
Hawkes processes are often applied to model dependence and interaction phenomena in multivariate event data sets, such as neuronal spike trains, social interactions, and financial…
Graph similarity learning for change-point detection in dynamic networks
Deborah Sulem, Henry Kenlay, Mihai Cucuringu +1
Dynamic networks are ubiquitous for modelling sequential graph-structured data, e.g., brain connectome, population flows and messages exchanges. In this work, we consider dynamic n…
Diverse Counterfactual Explanations for Anomaly Detection in Time Series
Deborah Sulem, Michele Donini, Muhammad Bilal Zafar +6
Data-driven methods that detect anomalies in times series data are ubiquitous in practice, but they are in general unable to provide helpful explanations for the predictions they m…
Bayesian estimation of nonlinear Hawkes process
Deborah Sulem, Vincent Rivoirard, Judith Rousseau
Multivariate point processes are widely applied to model event-type data such as natural disasters, online message exchanges, financial transactions or neuronal spike trains. One v…
Regularized spectral methods for clustering signed networks
Mihai Cucuringu, Apoorv Vikram Singh, Déborah Sulem +1
We study the problem of -way clustering in signed graphs. Considerable attention in recent years has been devoted to analyzing and modeling signed graphs, where the affinity mea…