Yassine LAGUEL
Yassine LAGUEL
Université Côte d'Azur
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A superquantile approach to federated learning with heterogeneous devices
Y Laguel, K Pillutla, J Malick, Z Harchaoui
2021 55th Annual Conference on Information Sciences and Systems (CISS), 1-6, 2021
Device heterogeneity in federated learning: A superquantile approach
Y Laguel, K Pillutla, J Malick, Z Harchaoui
arXiv preprint arXiv:2002.11223, 2020
Randomized Progressive Hedging methods for multi-stage stochastic programming
G Bareilles, Y Laguel, D Grishchenko, F Iutzeler, J Malick
Annals of Operations Research 295 (2), 535-560, 2020
Superquantiles at work: Machine learning applications and efficient subgradient computation
Y Laguel, K Pillutla, J Malick, Z Harchaoui
Set-Valued and Variational Analysis 29 (4), 967-996, 2021
First-order optimization for superquantile-based supervised learning
Y Laguel, J Malick, Z Harchaoui
2020 IEEE 30th International Workshop on Machine Learning for Signal …, 2020
On the convexity of level-sets of probability functions
Y Laguel, W Van Ackooij, J Malick, G Ramalho
arXiv preprint arXiv:2102.04052, 2021
Superquantile-based learning: a direct approach using gradient-based optimization
Y Laguel, J Malick, Z Harchaoui
Journal of Signal Processing Systems 94 (2), 161-177, 2022
Federated learning with heterogeneous data: A superquantile optimization approach
K Pillutla, Y Laguel, J Malick, Z Harchaoui
arXiv preprint arXiv:2112.09429, 2021
Differentially Private Federated Quantiles with the Distributed Discrete Gaussian Mechanism
K Pillutla, Y Laguel, J Malick, Z Harchaoui
International Workshop on Federated Learning: Recent Advances and New Challenges, 2022
Chance constrained problems: a bilevel convex optimization perspective
Y Laguel, J Malick, W Ackooij
arXiv preprint arXiv:2103.10832, 2021
Push–Pull with Device Sampling
YG Hsieh, Y Laguel, F Iutzeler, J Malick
IEEE Transactions on Automatic Control, 2023
High probability and risk-averse guarantees for stochastic saddle point problems
Y Laguel, NS Aybat, M Gürbüzbalaban
arXiv preprint arXiv:2304.00444, 2023
Tackling Distribution Shifts in Federated Learning with Superquantile Aggregation
K Pillutla, Y Laguel, J Malick, Z Harchaoui
NeurIPS 2022 Workshop on Distribution Shifts (DistShift), 2022
convex optimization for risk-sensitive learning
Y Laguel
Université Grenoble Alpes [2020-....], 2021
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