: dc.contributor.author: Perreault‐Lafleur, Carl; Carvalho, Margarida; Desaulniers, Guy dc.date.issued: 2023-04-14 dc.description.abstract: Nowadays, reaching a high level of employee satisfaction in efficient schedules is an important and difficult task faced by companies. We tackle a new variant of the personnel scheduling problem under unknown demand by considering employee satisfaction via endogenous uncertainty depending on the combination of their preferred and received schedules. We address this problem in the context of reserve staff scheduling, an unstudied operational problem from the transit industry. To handle the challenges brought by the two uncertainty sources, regular employee and reserve employee absences, we formulate this problem as a two‐stage stochastic integer program with mixed‐integer recourse. The first‐stage decisions consist in finding the days off of the reserve employees. After the unknown regular employee absences are revealed, the second‐stage decisions are to schedule the reserv[...]
: dc.contributor.author: Carvalho, Margarida; Dragotto, Gabriele; Lodi, Andrea; Sankaranarayanan, Sriram dc.date.issued: 2026-03 dc.description.abstract: When players in a game face messy decisions—like yes/no choices, rules layered within rules, or conflicting objectives—traditional algorithms often fail to find stable outcomes. Carvalho, Dragotto, Lodi, and Sankaranarayanan introduce a new algorithm, Cut-and-Play, that breaks this barrier. Unlike previous methods, Cut-and-Play handles nonconvex and unbounded decision spaces—the kind often found in real-world markets, public policy, and artificial intelligence systems. It works by iteratively solving simpler approximations of a complex game and then refining them with mathematical “cuts” until a solution is reached. Most strikingly, the algorithm finds equilibria up to 10× faster than existing techniques and is the first of its kind to offer a general-purpose solution method for this class of problems. The work is a leap forward for both the theory and applic[...]
: dc.contributor.author: Berger, David dc.date.issued: 2026-10 dc.description.abstract: L’essor des données volumineuses et de haute dimension, conjugué à l’augmentation des ressources computationnelles, a profondément transformé les pratiques en statistique. Si ces avancées ont favorisé le développement de méthodes performantes quant aux prédictions effectuées, les enjeux liés à l’interprétabilité des modèles et à la quantification de l’incertitude demeurent centraux, notamment en médecine ou en épidémiologie. En s'inscrivant dans ce contexte, cette thèse s’articule autour de deux axes principaux: la sélection de variables en grande dimension à l’aide de pénalisations $\ell_1$ et le développement de méthodes non paramétriques permettant de limiter les hypothèses de modélisation tout en assurant une quantification rigoureuse de l’incertitude. La première contribution porte sur l’estimation de graphes non dirigés. Nous étudions le comportement asymptotique du support estimé par le Lasso Graphique en fonction du[...]