Thèse Learning And Stochasticity In Prophet Inequalities H/F - Doctorat.Gouv.Fr

Établissement : Institut Polytechnique de Paris École nationale de la statistique et de l'administration économique École doctorale : Mathématiques Hadamard Laboratoire de recherche : CREST - Centre de recherche en économie et statistique Direction de la thèse : Vianney PERCHET ORCID 000000029333264X Début de la thèse : 2026-10-01 Date limite de candidature : 2026-06-30T23:59:59 This project aims to establish the theory and to design and study algorithms for decision-making processes that integrate AI-generated predictions into classical algorithms, ensuring they are more powerful when predictions are accurate and provably reliable when predictions are poor. Rather than motivating this by revisiting the limitations of end-to-end AI systems (as outlined before), we focus on what this integration requires in practice and how we will evaluate success.

From this perspective, the objective is to develop and analyse learning and to leverage stochastic structures in this setting, as showed possible by early works Prophet Inequality

Maths
CS
Probability