| Unsupervised scalable representation learning for multivariate time series JY Franceschi, A Dieuleveut, M Jaggi 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), 2019 | 533 | 2019 |
| Nonparametric stochastic approximation with large step-sizes A Dieuleveut, F Bach The Annals of Statistics 44 (4), 1363-1399, 2016 | 216 | 2016 |
| Bridging the gap between constant step size stochastic gradient descent and markov chains A Dieuleveut, A Durmus, F Bach Annals of Statistics 48 (Number 3 (2020)), 1348-1382., 2020 | 198 | 2020 |
| Harder, better, faster, stronger convergence rates for least-squares regression A Dieuleveut, N Flammarion, F Bach Journal of Machine Learning Research 18 (101), 1-51, 2017 | 179 | 2017 |
| Adaptive conformal predictions for time series M Zaffran, O Féron, Y Goude, J Josse, A Dieuleveut International Conference on Machine Learning, 25834-25866, 2022 | 137 | 2022 |
| FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings J Ogier du Terrail, SS Ayed, E Cyffers, F Grimberg, C He, R Loeb, ... Advances in Neural Information Processing Systems 35, 5315-5334, 2022 | 116 | 2022 |
| Differentially Private Federated Learning on Heterogeneous Data M Noble, A Bellet, A Dieuleveut AISTATS 2022, 2022 | 99 | 2022 |
| Artemis: tight convergence guarantees for bidirectional compression in federated learning C Philippenko, A Dieuleveut arXiv preprint arXiv:2006.14591, 2020 | 88* | 2020 |
| Communication trade-offs for local-sgd with large step size A Dieuleveut, KK Patel Advances in Neural Information Processing Systems 32, 2019 | 61 | 2019 |
| PEPit: computer-assisted worst-case analyses of first-order optimization methods in Python B Goujaud, C Moucer, F Glineur, JM Hendrickx, AB Taylor, A Dieuleveut Mathematical Programming Computation 16 (3), 337-367, 2024 | 45 | 2024 |
| QLSD: Quantised Langevin stochastic dynamics for Bayesian federated learning M Vono, V Plassier, A Durmus, A Dieuleveut, E Moulines AISTATS 2022, 2022 | 39 | 2022 |
| Preserved central model for faster bidirectional compression in distributed settings C Philippenko, A Dieuleveut Advances in Neural Information Processing Systems 34, 2387-2399, 2021 | 39 | 2021 |
| Context mover’s distance & barycenters: Optimal transport of contexts for building representations SP Singh, A Hug, A Dieuleveut, M Jaggi International Conference on Artificial Intelligence and Statistics, 3437-3449, 2020 | 35 | 2020 |
| Super-Acceleration with Cyclical Step-sizes B Goujaud, D Scieur, A Dieuleveut, A Taylor, F Pedregosa AISTATS 2022, 2022 | 34 | 2022 |
| Communication trade-offs for synchronized distributed SGD with large step size KK Patel, A Dieuleveut arXiv preprint arXiv:1904.11325, 2019 | 26 | 2019 |
| On convergence-diagnostic based step sizes for stochastic gradient descent S Pesme, A Dieuleveut, N Flammarion International Conference on Machine Learning, 7641-7651, 2020 | 24 | 2020 |
| Conformal Prediction with Missing Values M Zaffran, A Dieuleveut, J Josse, Y Romano ICML 2023-40 th International Conference on Machine Learning, 40578, 2023 | 20 | 2023 |
| Federated-EM with heterogeneity mitigation and variance reduction A Dieuleveut, G Fort, E Moulines, G Robin Advances in Neural Information Processing Systems 34, 29553-29566, 2021 | 19 | 2021 |
| Debiasing averaged stochastic gradient descent to handle missing values A Sportisse, C Boyer, A Dieuleveut, J Josse Advances in Neural Information Processing Systems 33, 12957-12967, 2020 | 17* | 2020 |
| Optimal first-order methods for convex functions with a quadratic upper bound B Goujaud, A Taylor, A Dieuleveut arXiv preprint arXiv:2205.15033, 2022 | 15 | 2022 |
Martin JaggiEPFLVerified email at epfl.ch
