Titre :

Decision-focused predictions via pessimistic bilevel optimization: a computational study

Auteur(s) :

Bucarey, Víctor [Auteur]
Universidad de O'Higgins [UOH]
Calderón, Sophia [Auteur]
Universidad de O'Higgins [UOH]
Muñoz, Gonzalo [Auteur]
Universidad de Chile = University of Chile [Santiago] [UCHILE]
Semet, Frédéric [Auteur]
Integrated Optimization with Complex Structure [INOCS]

Titre de la manifestation scientifique :

Integration of Constraint Programming, Artificial Intelligence, and Operations Research (CPAIOR 2024)

Titre de la revue :

Lecture Notes in Computer Science

Éditeur :

Springer Nature Switzerland

Lieu de publication :

Cham

Date de publication :

2024

Mot(s)-clé(s) en anglais :

Machine Learning (cs.LG)
Optimization and Control (math.OC)
FOS: Computer and information sciences
FOS: Mathematics
90C30

Discipline(s) HAL :

Computer Science [cs]/Operations Research [math.OC]
Informatique [cs]/Intelligence artificielle [cs.AI]

Résumé en anglais : [en]

Dealing with uncertainty in optimization parameters is an important and longstanding challenge. Typically, uncertain parameters are predicted accurately, and then a deterministic optimization problem is solved. However, ...
Lire la suite >Dealing with uncertainty in optimization parameters is an important and longstanding challenge. Typically, uncertain parameters are predicted accurately, and then a deterministic optimization problem is solved. However, the decisions produced by this so-called \emph{predict-then-optimize} procedure can be highly sensitive to uncertain parameters. In this work, we contribute to recent efforts in producing \emph{decision-focused} predictions, i.e., to build predictive models that are constructed with the goal of minimizing a \emph{regret} measure on the decisions taken with them. We formulate the exact expected regret minimization as a pessimistic bilevel optimization model. Then, using duality arguments, we reformulate it as a non-convex quadratic optimization problem. Finally, we show various computational techniques to achieve tractability. We report extensive computational results on shortest-path instances with uncertain cost vectors. Our results indicate that our approach can improve training performance over the approach of Elmachtoub and Grigas (2022), a state-of-the-art method for decision-focused learning.Lire moins >

Langue :

Anglais

Audience :

Internationale

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL