Yassine Laguel

According to our database, Yassine Laguel authored at least 10 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Chance-constrained programs with convex underlying functions: a bilevel convex optimization perspective.

Yassine Laguel, Jérôme Malick, Wim van Ackooij

Comput. Optim. Appl., July, 2024

Federated learning with superquantile aggregation for heterogeneous data.

Krishna Pillutla, Yassine Laguel, Jérôme Malick, Zaïd Harchaoui

Mach. Learn., May, 2024

2023

Push-Pull With Device Sampling.

Yu-Guan Hsieh, Yassine Laguel, Franck Iutzeler, Jérôme Malick

IEEE Trans. Autom. Control., December, 2023

2022

Superquantile-Based Learning: A Direct Approach Using Gradient-Based Optimization.

Yassine Laguel, Jérôme Malick, Zaïd Harchaoui

J. Signal Process. Syst., 2022

2021

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

Yassine Laguel

PhD thesis, 2021

Federated Learning with Heterogeneous Data: A Superquantile Optimization Approach.

Krishna Pillutla, Yassine Laguel, Jérôme Malick, Zaïd Harchaoui

CoRR, 2021

A Superquantile Approach to Federated Learning with Heterogeneous Devices.

Yassine Laguel, Krishna Pillutla, Jérôme Malick, Zaïd Harchaoui

Proceedings of the 55th Annual Conference on Information Sciences and Systems, 2021

2020

Device Heterogeneity in Federated Learning: A Superquantile Approach.

Yassine Laguel, Krishna Pillutla, Jérôme Malick, Zaïd Harchaoui

CoRR, 2020

Randomized Progressive Hedging methods for multi-stage stochastic programming.

Gilles Bareilles, Yassine Laguel, Dmitry Grishchenko, Franck Iutzeler, Jérôme Malick

Ann. Oper. Res., 2020

First-Order Optimization for Superquantile-Based Supervised Learning.

Yassine Laguel, Jérôme Malick, Zaïd Harchaoui

Proceedings of the 30th IEEE International Workshop on Machine Learning for Signal Processing, 2020