Aymeric Dieuleveut
Aymeric Dieuleveut
Assistant Professor, Ecole Polytechnique, France
Verified email at - Homepage
Cited by
Cited by
Unsupervised scalable representation learning for multivariate time series
JY Franceschi, A Dieuleveut, M Jaggi
33rd Conference on Neural Information Processing Systems (NeurIPS 2019), 2019
Nonparametric stochastic approximation with large step-sizes
A Dieuleveut, F Bach
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
Harder, better, faster, stronger convergence rates for least-squares regression
A Dieuleveut, N Flammarion, F Bach
The Journal of Machine Learning Research 18 (1), 3520-3570, 2017
Artemis: tight convergence guarantees for bidirectional compression in federated learning
C Philippenko, A Dieuleveut
arXiv preprint arXiv:2006.14591, 2020
Communication trade-offs for local-sgd with large step size
A Dieuleveut, KK Patel
Advances in Neural Information Processing Systems 32, 2019
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
Communication trade-offs for synchronized distributed SGD with large step size
KK Patel, A Dieuleveut
arXiv preprint arXiv:1904.11325, 2019
Preserved central model for faster bidirectional compression in distributed settings
C Philippenko, A Dieuleveut
Advances in Neural Information Processing Systems 34, 2387-2399, 2021
PEPit: computer-assisted worst-case analyses of first-order optimization methods in Python
B Goujaud, C Moucer, F Glineur, J Hendrickx, A Taylor, A Dieuleveut
arXiv preprint arXiv:2201.04040, 2022
Adaptive conformal predictions for time series
M Zaffran, O Féron, Y Goude, J Josse, A Dieuleveut
International Conference on Machine Learning, 25834-25866, 2022
Super-Acceleration with Cyclical Step-sizes
B Goujaud, D Scieur, A Dieuleveut, A Taylor, F Pedregosa
AISTATS 2022, 2022
QLSD: Quantised Langevin stochastic dynamics for Bayesian federated learning
M Vono, V Plassier, A Durmus, A Dieuleveut, E Moulines
AISTATS 2022, 2022
On convergence-diagnostic based step sizes for stochastic gradient descent
S Pesme, A Dieuleveut, N Flammarion
International Conference on Machine Learning, 7641-7651, 2020
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
Optimal first-order methods for convex functions with a quadratic upper bound
B Goujaud, A Taylor, A Dieuleveut
arXiv preprint arXiv:2205.15033, 2022
Differentially Private Federated Learning on Heterogeneous Data
M Noble, A Bellet, A Dieuleveut
AISTATS 2022, 2022
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
Communication trade-offs for Local-SGD with large step size
KK Patel, A Dieuleveut
Proceedings of the 33rd International Conference on Neural Information …, 2019
Stochastic approximation in Hilbert spaces
A Dieuleveut
Paris Sciences et Lettres (ComUE), 2017
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