Description
Date depot: 7 septembre 2021
Titre: Explainable Sparse Models: a Marriage between Machine Learning and Decision Theory
Directeur de thèse:
Patrice PERNY (LIP6)
Directrice de thèse:
Nataliya SOKOLOVSKA (LCQB)
Domaine scientifique: Sciences et technologies de l'information et de la communication
Thématique CNRS : Intelligence artificielle
Resumé: The aim of this thesis is to propose new approaches based on non-additive integrals to construct explainable sparse models. In the last decade, an increasing attention was devoted to non-additive integrals, such as Choquet and Sugeno integrals, enabling to model interactions among variables and providing a fine control of synergies among them. They are more and more used in the context of supervised learning, i.e., where an algorithm has access to observations and their labels. The non-additive integrals are introduced into loss functions to learn reliable predictive models. In AI, the Choquet integral was successfully used to extend the logistic regression, and the Sugeno integral was applied to ordinal aggregation problems. In decision theory, these integrals are widely used to aggregate values attached to multiple interacting criteria while keeping a compact and interpretable model. In this thesis, we would like to take the best of the two worlds, machine learning and decision theory, both actively developed in Artificial Intelligence, to propose adaptive and interpretable evaluation models based on non-additive integrals and contribute to produce reliable predictive models. In particular, we are interested to efficiently optimise an objective (loss) function which is based on a compact Choquet integral. The high number of weighting parameters to be learned in such models may be a practical issue for large size problems. We aim to design methods allowing to produce compact models achieving a good tradeoff between simplicity and expressivity.
Doctorant.e: Herin Margot