Seuraa
Aymeric Dieuleveut
Aymeric Dieuleveut
Professor, Ecole Polytechnique, France
Vahvistettu sähköpostiosoite verkkotunnuksessa polytechnique.edu - Kotisivu
Nimike
Viittaukset
Viittaukset
Vuosi
Unsupervised scalable representation learning for multivariate time series
JY Franceschi, A Dieuleveut, M Jaggi
33rd Conference on Neural Information Processing Systems (NeurIPS 2019), 2019
5842019
Nonparametric stochastic approximation with large step-sizes
A Dieuleveut, F Bach
The Annals of Statistics 44 (4), 1363-1399, 2016
2312016
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
2062020
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
1862017
Adaptive conformal predictions for time series
M Zaffran, O Féron, Y Goude, J Josse, A Dieuleveut
International Conference on Machine Learning, 25834-25866, 2022
1582022
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
1412022
Differentially Private Federated Learning on Heterogeneous Data
M Noble, A Bellet, A Dieuleveut
AISTATS 2022, 2022
1232022
Artemis: tight convergence guarantees for bidirectional compression in federated learning
C Philippenko, A Dieuleveut
arXiv preprint arXiv:2006.14591, 2020
92*2020
Communication trade-offs for local-sgd with large step size
A Dieuleveut, KK Patel
Advances in Neural Information Processing Systems 32, 2019
632019
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
532024
QLSD: Quantised Langevin stochastic dynamics for Bayesian federated learning
M Vono, V Plassier, A Durmus, A Dieuleveut, E Moulines
AISTATS 2022, 2022
432022
Preserved central model for faster bidirectional compression in distributed settings
C Philippenko, A Dieuleveut
Advances in Neural Information Processing Systems 34, 2387-2399, 2021
432021
Super-Acceleration with Cyclical Step-sizes
B Goujaud, D Scieur, A Dieuleveut, A Taylor, F Pedregosa
AISTATS 2022, 2022
372022
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
302020
On convergence-diagnostic based step sizes for stochastic gradient descent
S Pesme, A Dieuleveut, N Flammarion
International conference on machine learning, 7641-7651, 2020
262020
Communication trade-offs for synchronized distributed SGD with large step size
KK Patel, A Dieuleveut
arXiv preprint arXiv:1904.11325, 2019
262019
Conformal Prediction with Missing Values
M Zaffran, A Dieuleveut, J Josse, Y Romano
ICML 2023-40 th International Conference on Machine Learning, 40578, 2023
252023
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
252021
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
19*2020
Provable non-accelerations of the heavy-ball method
B Goujaud, A Taylor, A Dieuleveut
arXiv preprint arXiv:2307.11291, 2023
182023
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Artikkelit 1–20