1 citations · 1 across the 6 of their papers we have counts for
6 papers
Semi-knockoffs: a model-agnostic conditional independence testing method with finite-sample guarantees
Angel Reyero-Lobo, Bertrand Thirion, Pierre Neuvial
Conditional independence testing (CIT) is essential for reliable scientific discovery. It prevents spurious findings and enables controlled feature selection. Recent CIT methods ha…
Cluster Size Matters: A Comparative Study of Notip and pARI for Post Hoc Inference in fMRI
Nils Peyrouset, Pierre Neuvial, Bertrand Thirion
All Resolutions Inference (ARI) is a post hoc inference method for functional Magnetic Resonance Imaging (fMRI) data analysis that provides valid lower bounds on the proportion of…
A Principled Approach for Defining and Comparing Variable Importance Measures
Angel Reyero-Lobo, Pierre Neuvial, Bertrand Thirion
Variable importance measures (VIMs) aim to quantify the contribution of each input covariate to the predictability of a given output. With the growing interest in explainable AI, n…
Inference post region selection
Dominique Bontemps, François Bachoc, Pierre Neuvial
Post-selection inference consists in providing statistical guarantees, based on a data set, that are robust to a prior model selection step on the same data set. In this paper, we…
Conditional Feature Importance revisited: Double Robustness, Efficiency and Inference
Angel Reyero-Lobo, Pierre Neuvial, Bertrand Thirion
Conditional Feature Importance (CFI) is a classical variable importance measure that accounts for the relationship between the studied feature and the others. However, CFI has not…
When Knockoffs fail: diagnosing and fixing non-exchangeability of Knockoffs
Alexandre Blain, Angel Reyero Lobo, Julia Linhart +2
Knockoffs are a popular statistical framework that addresses the challenging problem of conditional variable selection in high-dimensional settings with statistical control. Such s…