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Thesis : Explainable Sparse Models: a Marriage between Machine Learning and Decision Theory

SCAI PhD thesis
The aim of this thesis is to propose new approaches based on non-additive integrals to construct explainable sparse models for supervised learning (with possible applications for developing new strategies for personalized medicine, with the Pitié-Salpêtrière hospital).

This PhD research project has been submitted for a funding request to “Sorbonne Center for Artificial Intelligence (SCAI)”. The PhD candidate selected by the project leader will therefore participate in the project selection process (including a file and an interview) to obtain funding.

More details here

Contact :Patrice Perny

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