The eighty-third UQSay seminar on UQ, DACE and related topics will take place online on Thursday afternoon, February 13, 2025.
2–3 PM — Margot Hérin (LIP6, Sorbonne University) — [slides]
Algorithms for learning capacity-based preference models
Preference models from Decision Theory are used to describe, explain, or predict human behavior in evaluation or decision-making tasks. Beyond this descriptive role, a key feature of these models is their ability to guarantee normative properties, ensuring the internal consistency of the modeled value system and the resulting decisions. Hence, they can also be used to assist individuals in making a relevant choice based on their preferences or provide machines with the ability to autonomously yet controllably make sophisticated decisions in complex environments, involving multi-criteria or collective decision-making, or decision-making under uncertainty.
In this talk, we consider aggregation functions weighted by a non-additive set function (called capacity), such as multilinear utilities or Choquet integrals. The non-additivity of the capacity makes it possible to model criteria interactions, leaving room for a diversity of attitudes in criteria aggregation. However, allowing for these interactions dramatically increases the complexity of the preference learning task and may prevent the model from being interpretable, due to the combinatorial nature of the possible interactions.
We address this challenge by learning a sparse Möbius transform of the capacity, where the few non-zero Möbius masses indicate the significant positive or negative synergies between criteria. Specifically, we propose a learning method based on iterative reweighted least squares (IRLS) for sparse recovery, and dualization to improve scalability, making it possible to handle aggregation problems involving more than 20 criteria. We also present an online learning algorithm based on regularized dual averaging (RDA), designed for decision-making contexts where preference examples become available sequentially, but also well suited to handle large-scale preference databases (large number of preferences or criteria examples). In addition, the inclusion of normative constraints on the capacity (e.g., monotonicity, supermodularity) is made possible by combining RDA with the method of alternating direction multipliers (ADMM).
References:
- Learning Preference Models with Sparse Interactions of Criteria, IJCAI 2023
- Online Learning of Capacity-Based Preference Models, IJCAI 2024
Joint work with P. Perny (Sorbonne University) & N. Sokolovska (Sorbonne University).
Organizing committee: Pierre Barbillon (MIA-Paris), Julien Bect (L2S), Nicolas Bousquet (EDF R&D), Vincent Chabridon (EDF R&D), Amélie Fau (LMPS), Filippo Gatti (LMPS), Clément Gauchy (CEA), Bertrand Iooss (EDF R&D), Alexandre Janon (LMO), Sidonie Lefebvre (ONERA), Didier Lucor (LISN), Sébastien Petit (LNE), Emmanuel Vazquez (L2S), Xujia Zhu (L2S).
Coordinators: Sidonie Lefebvre (ONERA) & Xujia Zhu (L2S)
Practical details: the seminar will be held online using Microsoft Teams.
If you want to attend this seminar (or any of the forthcoming online UQSay seminars), and if you do not already have access to the UQSay group on Teams, simply send an email and you will be invited. Please specify which email address the invitation must be sent to (this has to be the address associated with your Teams account).
You will find the link to the seminar on the "General" UQSay channel on Teams, approximately 15 minutes before the beginning.
The technical side of things: you can use Teams either directly from your web browser or using the "fat client", which is available for most platforms (Windows, Linux, Mac, Android & iOS). We strongly recommend the latter option whenever possible. Please give it a try before the seminar to anticipate potential problems.
