Section: Research Program

Statistical inference: (multiple) tests and confidence regions (including post-selection)

Celeste considers the problems of quantifying the uncertainty of predictions or estimations (thanks to confidence intervals) and of providing significance levels ($p$-values, corrected for multiplicity if needed) for each “discovery” made by a learning algorithm. This is an important practical issue when performing feature selection – one then speaks of post-selection inference – change-point detection or outlier detection, to name but a few. We tackle it in particular through a collaboration with the Parietal team (Inria Saclay) and LBBE (CNRS), with applications in neuroimaging and genomics.