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 | 35 | 2021 |
Device heterogeneity in federated learning: A superquantile approach Y Laguel, K Pillutla, J Malick, Z Harchaoui arXiv preprint arXiv:2002.11223, 2020 | 25 | 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 | 21 | 2021 |
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 | 21 | 2020 |
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 | 16 | 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 | 7 | 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 | 5 | 2022 |
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 | 5 | 2022 |
Federated learning with heterogeneous data: A superquantile optimization approach K Pillutla, Y Laguel, J Malick, Z Harchaoui arXiv preprint arXiv:2112.09429, 2021 | 3 | 2021 |
Chance constrained problems: a bilevel convex optimization perspective Y Laguel, J Malick, W Ackooij arXiv preprint arXiv:2103.10832, 2021 | 3 | 2021 |
Push–Pull with Device Sampling YG Hsieh, Y Laguel, F Iutzeler, J Malick IEEE Transactions on Automatic Control, 2023 | 2 | 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 | 1 | 2022 |
High probability and risk-averse guarantees for stochastic saddle point problems Y Laguel, NS Aybat, M Gürbüzbalaban arXiv preprint arXiv:2304.00444, 2023 | | 2023 |
convex optimization for risk-sensitive learning Y Laguel Université Grenoble Alpes [2020-....], 2021 | | 2021 |