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kernelPSI: a Post-Selection Inference Framework for Nonlinear Variable Selection
Lotfi Slim · Clément Chatelain · Chloe-Agathe Azencott · Jean-Philippe Vert

Tue Jun 11 03:10 PM -- 03:15 PM (PDT) @ Room 101

Model selection is an essential task for many applications in scientific discovery. The most common approaches rely on univariate linear measures of association between each feature and the outcome. Such classical selection procedures fail to take into account nonlinear effects and interactions between features. Kernel-based selection procedures have been proposed as a solution. However, current strategies for kernel selection fail to measure the significance of a joint model constructed through the combination of the basis kernels. In the present work, we exploit recent advances in post-selection inference to propose a valid statistical test for the association of a joint model of the selected kernels with the outcome. The kernels are selected via a step-wise procedure which we model as a succession of quadratic constraints in the outcome variable.

Author Information

Lotfi Slim (Mines ParisTech (ARMINES))
Clément Chatelain (Sanofi R&D)
Chloe-Agathe Azencott (MINES ParisTech)
Jean-Philippe Vert (Google)

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