- Computer Science Laboratory

OUAGUENOUNI Mohamed

PhD Student at Sorbonne University (ATER, Sorbonne Université)
Team : DECISION
    Sorbonne Université - LIP6
    Boîte courrier 169
    Couloir 26-00, Étage 4, Bureau 404
    4 place Jussieu
    75252 PARIS CEDEX 05
    FRANCE

+33 1 44 27 87 41
Mohamed.Ouaguenouni (at) nulllip6.fr
https://lip6.fr/Mohamed.Ouaguenouni

Supervision : Olivier SPANJAARD
Co-supervision : GILBERT Hugo, ÖZTÜRK Meltem

Learning and predicting preferences over sets under element interactions and preference cycles

This thesis lies at the intersection of decision theory, artificial intelligence, and machine learning, focusing on preference learning and modeling on sets. The presented work addresses the modeling of decision-maker preferences expressed through pairwise comparisons over sets of elements, pursuing two distinct objectives: predicting unobserved preferences and prescribing potentially optimal alternatives. In this framework, we pay special attention to preference models with interactions, their theoretical properties, and the practical challenges raised by real-world applications.

The contributions are structured along three complementary axes: the first axis extends the Robust Ordinal Regression (ROR) method to enhance prediction robustness in the presence of multiple compatible models, particularly when handling positive or negative interactions between elements in a set. The second axis introduces a hybrid approach combining Gaussian processes and linear programming, establishing a probabilistic framework for managing inconsistencies in preference statements. The third axis extends the classical additive model by incorporating bilinear terms, enabling the representation of intransitive preferences while maintaining computational tractability of model parameter learning.


Phd defence : 06/05/2025

Jury members :

Sébastien Destercke, Directeur de recherche au CNRS, Université de Technologie de Compiègne [Rapporteur]
Eyke Hüllermeier, Professor, Ludwig-Maximilians-Universität München [Rapporteur]
Christophe Labreuche, Ingénieur de recherche, Thales Research and Technology
Wassila Ouerdane, Professeure, CentraleSupélec
Nataliya Sokolovska, Professeure, Sorbonne Université
Olivier Spanjaard, Professeur, Sorbonne Université
Hugo Gilbert, Maître de conférences, Université Paris Dauphine-PSL
Meltem Öztürk, Professeure, Université Paris Dauphine-PSL

2022-2025 Publications