Decision-focused predictions via pessimistic ...
Document type :
Direction scientifique d'une publication (ouvrage, numéro spécial de revue, proceedings): Proceedings
Title :
Decision-focused predictions via pessimistic bilevel optimization: a computational study
Author(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]
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]
Conference title :
Integration of Constraint Programming, Artificial Intelligence, and Operations Research (CPAIOR 2024)
Journal title :
Lecture Notes in Computer Science
Publisher :
Springer Nature Switzerland
Publication place :
Cham
Publication date :
2024
English keyword(s) :
Machine Learning (cs.LG)
Optimization and Control (math.OC)
FOS: Computer and information sciences
FOS: Mathematics
90C30
Optimization and Control (math.OC)
FOS: Computer and information sciences
FOS: Mathematics
90C30
HAL domain(s) :
Computer Science [cs]/Operations Research [math.OC]
Informatique [cs]/Intelligence artificielle [cs.AI]
Informatique [cs]/Intelligence artificielle [cs.AI]
English abstract : [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, ...
Show more >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.Show less >
Show more >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.Show less >
Language :
Anglais
Audience :
Internationale
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