Integrating Reasoning on Combinatorial Optimisation Problems into Machine Learni

Bordeaux Data Science
Bordeaux Data Science
Groupe public

University of Bordeaux - Talence Campus

351 Cours de la Libération · Talence

Comment nous trouver

Bâtiment A33, salle 385, si besoin : francois.clautiaux [AT]

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Talk d'Emir Demirovick University of Melbourne (Australia).

We study the predict+optimise problem, where machine learning and combinatorial optimisation must interact to achieve a common goal. These problems are important when optimisation needs to be performed on input parameters that are not fully observed but must instead be estimated using machine learning. Our aim is to develop machine learning algorithms that take into account the underlying combinatorial optimisation problem. While a plethora of sophisticated algorithms and approaches are available in machine learning and optimisation respectively, an established methodology for solving problems which require both machine learning and combinatorial optimisation remains an open question. In this talk, we introduce the problem, discuss its difficulties, and present our progress based on our papers from CPAIOR'19 and IJCAI'19.

Mardi 9 juillet à l'IMB, 11h, à l'université de Bordeaux, bâtiment A33, salle 385