Prix de thèse 2018



Geometric and Dual Approaches to Cumulative Scheduling

" The work of Nicolas Bonifas falls in the scope of constraint-based scheduling. In this framework, the most frequently encountered resource constraint is the cumulative, which enables the modeling of parallel processes. In his thesis, Nicolas studies the cumulative constraint with the help of tools rarely used in constraint programming (polyhedral analysis, linear programming duality, projective geometry duality) and propose two contributions for the domain. Cumulative strengthening is a means of generating tighter redundant cumulative constraints, analogous to the generation of cuts in integer linear programming. This is one of the first examples of a redundant global constraint. Energy Reasoning is an extremely powerful propagation for cumulative constraint, with hitherto a high complexity of O(n^3). Nicolas proposes an algorithm that computes this propagation with a O(n^2 log n) complexity, which is a significant improvement of this algorithm known for more than 25 years. Nicolas Bonifas, Geometric and Dual Approaches to Cumulative Scheduling . Université Paris-Saclay, 19/12/2017."


 Nicolas Flammarion

L'approximation stochastique et régression par moindres carrés avec applications en apprentissage automatique.

" Many problems in machine learning are naturally cast as the minimization of a smooth function defined on a Euclidean space. While small problems are efficiently solved by classical optimization algorithms, large-scale problems are typically solved with first-order techniques based on gradient descent. Nicolas Flammarion considers, in his thesis, the particular case of the quadratic loss. He addresses its minimization when gradients are only accessible through a stochastic oracle and proposes optimal algorithms in different cases. His work offers many perspectives of applications of the quadratic loss in machine learning. Clustering and estimation with shape constraints are the two first applications already considered. Nicolas Flammarion. Stochastic approximation and least-squares regression, with applications to machine learning. Paris Sciences et Lettres, 24/07/2017. "


Le jury 2018 est présidé par Mathilde Mougeot  :

Membres du nommés par le Conseil Scientifique du PGMO

Mathilde Mougeot, ENSIIE et Université Paris-Diderot, Présidente du Jury
Gabriel Peyré, ENS, CNRS
Nicolas Vieille, HEC

Membres nommés par la ROADEF

Céline Gicquel, Université Paris-Sud
Sandra Ulrich Ngueveu, LAAS, CNRS
Michael Poss, Université de Montpellier

Membres nommés par la SMAI

Jean Baptiste Caillau, Université de la Côte d’Azur
Thierry Champion, Université de Toulon.
Aude Rondepierre, INSA de Toulouse