First-Order Optimization for Superquantile-Based Supervised Learning. - Yassine Laguel; Jérôme Malick; Zaïd Harchaoui (MLSP, 2020)

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

Device Heterogeneity in Federated Learning: A Superquantile Approach. - Yassine Laguel; Krishna Pillutla; Jérôme Malick; Zaïd Harchaoui (CoRR, 2020)

A Superquantile Approach to Federated Learning with Heterogeneous Devices. - Yassine Laguel; Krishna Pillutla; Jérôme Malick; Zaïd Harchaoui (CISS, 2021)

Federated Learning with Heterogeneous Data: A Superquantile Optimization Approach. - Krishna Pillutla; Yassine Laguel; Jérôme Malick; Zaïd Harchaoui (CoRR, 2021)

Convex optimization for risk-sensitive learning. (Optimisation convexe pour l'apprentissage robuste au risque). - Yassine Laguel (2021)

Superquantile-Based Learning: A Direct Approach Using Gradient-Based Optimization. - Yassine Laguel; Jérôme Malick; Zaïd Harchaoui (J. Signal Process. Syst., 2022)

Push-Pull With Device Sampling. - Yu-Guan Hsieh; Yassine Laguel; Franck Iutzeler; Jérôme Malick (IEEE Trans. Autom. Control., 2023)

High-probability complexity bounds for stochastic non-convex minimax optimization. - Yassine Laguel; Yasa Syed; Necdet Serhat Aybat; Mert Gürbüzbalaban (NeurIPS, 2024)

Federated learning with superquantile aggregation for heterogeneous data. - Krishna Pillutla; Yassine Laguel; Jérôme Malick; Zaïd Harchaoui (Mach. Learn., 2024)

Chance-constrained programs with convex underlying functions: a bilevel convex optimization perspective. - Yassine Laguel; Jérôme Malick; Wim van Ackooij (Comput. Optim. Appl., 2024)