Learning Joint Models of Prediction and Optimization

James Kotary, Vincenzo Di Vito, Jacob Christopher, Pascal Van Hentenryck, Ferdinando Fioretto

Research output: Chapter in Book/Entry/PoemConference contribution

Abstract

The Predict-Then-Optimize framework uses machine learning models to predict unknown parameters of an optimization problem from exogenous features before solving. This setting is common to many real-world decision processes, and recently it has been shown that decision quality can be substantially improved by solving and differentiating the optimization problem within an end-to-end training loop. However, this approach requires significant computational effort in addition to handcrafted, problem-specific rules for backpropagation through the optimization step, challenging its applicability to a broad class of optimization problems. This paper proposes an alternative method, in which optimal solutions are learned directly from the observable features by joint predictive models. The approach is generic, and based on an adaptation of the Learning-to-Optimize paradigm, from which a rich variety of existing techniques can be employed. Experimental evaluations show the ability of several Learning-to-Optimize methods to provide efficient and accurate solutions to an array of challenging Predict-Then-Optimize problems.

Original languageEnglish (US)
Title of host publicationECAI 2024 - 27th European Conference on Artificial Intelligence, Including 13th Conference on Prestigious Applications of Intelligent Systems, PAIS 2024, Proceedings
EditorsUlle Endriss, Francisco S. Melo, Kerstin Bach, Alberto Bugarin-Diz, Jose M. Alonso-Moral, Senen Barro, Fredrik Heintz
PublisherIOS Press BV
Pages2476-2483
Number of pages8
ISBN (Electronic)9781643685489
DOIs
StatePublished - Oct 16 2024
Externally publishedYes
Event27th European Conference on Artificial Intelligence, ECAI 2024 - Santiago de Compostela, Spain
Duration: Oct 19 2024Oct 24 2024

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume392
ISSN (Print)0922-6389
ISSN (Electronic)1879-8314

Conference

Conference27th European Conference on Artificial Intelligence, ECAI 2024
Country/TerritorySpain
CitySantiago de Compostela
Period10/19/2410/24/24

ASJC Scopus subject areas

  • Artificial Intelligence

Fingerprint

Dive into the research topics of 'Learning Joint Models of Prediction and Optimization'. Together they form a unique fingerprint.

Cite this