Randomized Progressive Hedging methods for multi-stage stochastic programming. - Gilles Bareilles; Yassine Laguel; Dmitry Grishchenko; Franck Iutzeler; Jérôme Malick (Ann. Oper. Res., 2020)

On the interplay between acceleration and identification for the proximal gradient algorithm. - Gilles Bareilles; Franck Iutzeler (Comput. Optim. Appl., 2020)

Piecewise Polynomial Regression of Tame Functions via Integer Programming. - Gilles Bareilles; Johannes Aspman; Jiri Nemecek 002; Jakub Marecek (CoRR, 2023)

Newton acceleration on manifolds identified by proximal gradient methods. - Gilles Bareilles; Franck Iutzeler; Jérôme Malick (Math. Program., 2023)

Harnessing Structure in Composite Nonsmooth Minimization. - Gilles Bareilles; Franck Iutzeler; Jérôme Malick (SIAM J. Optim., 2023)

Generalizing while preserving monotonicity in comparison-based preference learning models. - Julien Fageot; Peva Blanchard; Gilles Bareilles; Lê-Nguyên Hoang (CoRR, 2025)

On Monotonicity in AI Alignment. - Gilles Bareilles; Julien Fageot; Lê-Nguyên Hoang; Peva Blanchard; Wassim Bouaziz; Sébastien Rouault; El-Mahdi El-Mhamdi (CoRR, 2025)

Benchmarking Stochastic Approximation Algorithms for Fairness-Constrained Training of Deep Neural Networks. - Andrii Kliachkin; Jana Lepsová; Gilles Bareilles; Jakub Marecek (CoRR, 2025)