| 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 | 43 | 2021 |
| Device heterogeneity in federated learning: A superquantile approach Y Laguel, K Pillutla, J Malick, Z Harchaoui arXiv preprint arXiv:2002.11223, 2020 | 28 | 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 | 22 | 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 | 22 | 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 | 18 | 2020 |
| Federated learning with superquantile aggregation for heterogeneous data K Pillutla, Y Laguel, J Malick, Z Harchaoui Machine Learning 113 (5), 2955-3022, 2024 | 15 | 2024 |
| 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 | 6 | 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 | 6 | 2022 |
| Federated learning with heterogeneous data: A superquantile optimization approach K Pillutla, Y Laguel, J Malick, Z Harchaoui | 4 | 2022 |
| Push–pull with device sampling YG Hsieh, Y Laguel, F Iutzeler, J Malick IEEE Transactions on Automatic Control 68 (12), 7179-7194, 2023 | 3 | 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 | 2 | 2022 |
| Chance constrained problems: a bilevel convex optimization perspective Y Laguel, J Malick, W Ackooij arXiv preprint arXiv:2103.10832, 2021 | 2 | 2021 |
| Chance-constrained programs with convex underlying functions: a bilevel convex optimization perspective Y Laguel, J Malick, W van Ackooij Computational Optimization and Applications, 1-29, 2024 | 1 | 2024 |
| High probability and risk-averse guarantees for stochastic saddle point problems Y Laguel, NS Aybat, M Gürbüzbalaban arXiv preprint arXiv:2304.00444, 2023 | 1 | 2023 |
| High-probability complexity guarantees for nonconvex minimax problems Y Laguel, Y Syed, NS Aybat, M Gürbüzbalaban arXiv preprint arXiv:2405.14130, 2024 | | 2024 |
| High Probability and Risk-Averse Guarantees for a Stochastic Accelerated Primal-Dual Method 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 |
| High-probability complexity bounds for stochastic non-convex minimax optimization Y Laguel, Y Syed, N Aybat, M Gurbuzbalaban The Thirty-eighth Annual Conference on Neural Information Processing Systems, 0 | | |
Jérôme MalickCNRSVerified email at univ-grenoble-alpes.fr
