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stat.ME2025
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…
stat.ME2025
Empirical Bayes for Data Integration
Paul Rognon-Vael, David Rossell
We discuss the use of empirical Bayes for data integration, in the sense of transfer learning. Our main interest is in settings where one wishes to learn structure (e.g. feature se…
math.ST2025
Improving variable selection properties with data integration and transfer learning
Paul Rognon-Vael, David Rossell, Piotr Zwiernik
We study variable selection (also called support recovery) in high-dimensional sparse linear regression when one has external information on which variables are likely to be associ…