100 citations · 718 across the 51 of their papers we have counts for
56 papers
Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds
Paul Rosa, Viacheslav Borovitskiy, Alexander Terenin +1
Gaussian processes are used in many machine learning applications that rely on uncertainty quantification. Recently, computational tools for working with these models in geometric…
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…
A flexible, random histogram kernel for discrete-time Hawkes processes
Raiha Browning, Judith Rousseau, Kerrie Mengersen
Hawkes processes are a self-exciting stochastic process used to describe phenomena whereby past events increase the probability of the occurrence of future events. This work presen…
Evidence estimation in finite and infinite mixture models and applications
Adrien Hairault, Christian P. Robert, Judith Rousseau
Estimating the model evidence - or mariginal likelihood of the data - is a notoriously difficult task for finite and infinite mixture models and we reexamine here different Monte C…
Estimating a density near an unknown manifold: a Bayesian nonparametric approach
Clément Berenfeld, Paul Rosa, Judith Rousseau
We study the Bayesian density estimation of data living in the offset of an unknown submanifold of the Euclidean space. In this perspective, we introduce a new notion of anisotropi…
Fast Bayesian Coresets via Subsampling and Quasi-Newton Refinement
Cian Naik, Judith Rousseau, Trevor Campbell
Bayesian coresets approximate a posterior distribution by building a small weighted subset of the data points. Any inference procedure that is too computationally expensive to be r…