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Aymeric Dieuleveut
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- affiliation: École Polytechnique Institut Polytechnique de Paris, France
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2020 – today
- 2024
- [j4]Baptiste Goujaud, Céline Moucer, François Glineur, Julien M. Hendrickx, Adrien B. Taylor, Aymeric Dieuleveut:
PEPit: computer-assisted worst-case analyses of first-order optimization methods in Python. Math. Program. Comput. 16(3): 337-367 (2024) - [c21]Mahmoud Hegazy, Rémi Leluc, Cheuk Ting Li, Aymeric Dieuleveut:
Compression with Exact Error Distribution for Federated Learning. AISTATS 2024: 613-621 - [c20]Damien Ferbach, Baptiste Goujaud, Gauthier Gidel, Aymeric Dieuleveut:
Proving Linear Mode Connectivity of Neural Networks via Optimal Transport. AISTATS 2024: 3853-3861 - [c19]Alexis Ayme, Claire Boyer, Aymeric Dieuleveut, Erwan Scornet:
Random features models: a way to study the success of naive imputation. ICML 2024 - [c18]Rémi Leluc, Aymeric Dieuleveut, François Portier, Johan Segers, Aigerim Zhuman:
Sliced-Wasserstein Estimation with Spherical Harmonics as Control Variates. ICML 2024 - [i20]Rémi Leluc, Aymeric Dieuleveut, François Portier, Johan Segers, Aigerim Zhuman:
Sliced-Wasserstein Estimation with Spherical Harmonics as Control Variates. CoRR abs/2402.01493 (2024) - [i19]Renaud Gaucher, Hadrien Hendrikx, Aymeric Dieuleveut:
Byzantine-Robust Gossip: Insights from a Dual Approach. CoRR abs/2405.03449 (2024) - 2023
- [j3]Baptiste Goujaud
, Aymeric Dieuleveut, Adrien B. Taylor:
Counter-Examples in First-Order Optimization: A Constructive Approach. IEEE Control. Syst. Lett. 7: 2485-2490 (2023) - [j2]Aymeric Dieuleveut
Stochastic Approximation Beyond Gradient for Signal Processing and Machine Learning. IEEE Trans. Signal Process. 71: 3117-3148 (2023) - [c17]Baptiste Goujaud, Aymeric Dieuleveut, Adrien B. Taylor:
On Fundamental Proof Structures in First-Order Optimization. CDC 2023: 3023-3030 - [c16]Alexis Ayme, Claire Boyer, Aymeric Dieuleveut, Erwan Scornet:
Naive imputation implicitly regularizes high-dimensional linear models. ICML 2023: 1320-1340 - [c15]Margaux Zaffran, Aymeric Dieuleveut, Julie Josse, Yaniv Romano:
Conformal Prediction with Missing Values. ICML 2023: 40578-40604 - [i18]Margaux Zaffran, Aymeric Dieuleveut, Julie Josse, Yaniv Romano:
Conformal Prediction with Missing Values. CoRR abs/2306.02732 (2023) - [i17]Constantin Philippenko, Aymeric Dieuleveut:
Compressed and distributed least-squares regression: convergence rates with applications to Federated Learning. CoRR abs/2308.01358 (2023) - [i16]Damien Ferbach, Baptiste Goujaud, Gauthier Gidel, Aymeric Dieuleveut:
Proving Linear Mode Connectivity of Neural Networks via Optimal Transport. CoRR abs/2310.19103 (2023) - [i15]Mahmoud Hegazy, Rémi Leluc, Cheuk Ting Li, Aymeric Dieuleveut:
Compression with Exact Error Distribution for Federated Learning. CoRR abs/2310.20682 (2023) - 2022
- [c14]Baptiste Goujaud, Damien Scieur, Aymeric Dieuleveut, Adrien B. Taylor, Fabian Pedregosa:
Super-Acceleration with Cyclical Step-sizes. AISTATS 2022: 3028-3065 - [c13]Maxime Vono, Vincent Plassier, Alain Durmus, Aymeric Dieuleveut, Eric Moulines:
QLSD: Quantised Langevin Stochastic Dynamics for Bayesian Federated Learning. AISTATS 2022: 6459-6500 - [c12]Maxence Noble, Aurélien Bellet, Aymeric Dieuleveut:
Differentially Private Federated Learning on Heterogeneous Data. AISTATS 2022: 10110-10145 - [c11]Alexis Ayme, Claire Boyer, Aymeric Dieuleveut, Erwan Scornet:
Near-optimal rate of consistency for linear models with missing values. ICML 2022: 1211-1243 - [c10]Margaux Zaffran, Olivier Féron, Yannig Goude, Julie Josse, Aymeric Dieuleveut:
Adaptive Conformal Predictions for Time Series. ICML 2022: 25834-25866 - [c9]Jean Ogier du Terrail, Samy-Safwan Ayed, Edwige Cyffers, Felix Grimberg, Chaoyang He, Regis Loeb, Paul Mangold, Tanguy Marchand, Othmane Marfoq, Erum Mushtaq, Boris Muzellec, Constantin Philippenko, Santiago Silva, Maria Telenczuk, Shadi Albarqouni, Salman Avestimehr, Aurélien Bellet, Aymeric Dieuleveut, Martin Jaggi, Sai Praneeth Karimireddy, Marco Lorenzi, Giovanni Neglia, Marc Tommasi, Mathieu Andreux:
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings. NeurIPS 2022 - [i14]Baptiste Goujaud, Céline Moucer, François Glineur, Julien M. Hendrickx, Adrien B. Taylor, Aymeric Dieuleveut:
PEPit: computer-assisted worst-case analyses of first-order optimization methods in Python. CoRR abs/2201.04040 (2022) - [i13]Alexis Ayme, Claire Boyer, Aymeric Dieuleveut
Minimax rate of consistency for linear models with missing values. CoRR abs/2202.01463 (2022) - [i12]Margaux Zaffran, Aymeric Dieuleveut, Olivier Féron, Yannig Goude, Julie Josse:
Adaptive Conformal Predictions for Time Series. CoRR abs/2202.07282 (2022) - [i11]Jean Ogier du Terrail, Samy-Safwan Ayed, Edwige Cyffers, Felix Grimberg, Chaoyang He, Regis Loeb, Paul Mangold, Tanguy Marchand, Othmane Marfoq, Erum Mushtaq, Boris Muzellec, Constantin Philippenko, Santiago Silva, Maria Telenczuk, Shadi Albarqouni, Salman Avestimehr, Aurélien Bellet, Aymeric Dieuleveut, Martin Jaggi, Sai Praneeth Karimireddy, Marco Lorenzi, Giovanni Neglia, Marc Tommasi, Mathieu Andreux:
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings. CoRR abs/2210.04620 (2022) - 2021
- [c8]Constantin Philippenko, Aymeric Dieuleveut:
Preserved central model for faster bidirectional compression in distributed settings. NeurIPS 2021: 2387-2399 - [c7]Aymeric Dieuleveut, Gersende Fort, Eric Moulines, Geneviève Robin:
Federated-EM with heterogeneity mitigation and variance reduction. NeurIPS 2021: 29553-29566 - [i10]Constantin Philippenko, Aymeric Dieuleveut:
Preserved central model for faster bidirectional compression in distributed settings. CoRR abs/2102.12528 (2021) - [i9]Maxime Vono, Vincent Plassier, Alain Durmus, Aymeric Dieuleveut, Eric Moulines:
QLSD: Quantised Langevin stochastic dynamics for Bayesian federated learning. CoRR abs/2106.00797 (2021) - [i8]Aymeric Dieuleveut
Federated Expectation Maximization with heterogeneity mitigation and variance reduction. CoRR abs/2111.02083 (2021) - [i7]Maxence Noble, Aurélien Bellet, Aymeric Dieuleveut
Differentially Private Federated Learning on Heterogeneous Data. CoRR abs/2111.09278 (2021) - 2020
- [c6]Sidak Pal Singh, Andreas Hug, Aymeric Dieuleveut, Martin Jaggi:
Context Mover's Distance & Barycenters: Optimal Transport of Contexts for Building Representations. AISTATS 2020: 3437-3449 - [c5]Scott Pesme, Aymeric Dieuleveut, Nicolas Flammarion:
On Convergence-Diagnostic based Step Sizes for Stochastic Gradient Descent. ICML 2020: 7641-7651 - [c4]Aude Sportisse, Claire Boyer, Aymeric Dieuleveut, Julie Josse:
Debiasing Averaged Stochastic Gradient Descent to handle missing values. NeurIPS 2020 - [i6]Constantin Philippenko, Aymeric Dieuleveut:
Artemis: tight convergence guarantees for bidirectional compression in Federated Learning. CoRR abs/2006.14591 (2020) - [i5]Scott Pesme, Aymeric Dieuleveut, Nicolas Flammarion:
On Convergence-Diagnostic based Step Sizes for Stochastic Gradient Descent. CoRR abs/2007.00534 (2020)
2010 – 2019
- 2019
- [c3]Sidak Pal Singh, Andreas Hug, Aymeric Dieuleveut, Martin Jaggi:
Context Mover's Distance & Barycenters: Optimal transport of contexts for building representations. DGS@ICLR 2019 - [c2]Jean-Yves Franceschi, Aymeric Dieuleveut, Martin Jaggi:
Unsupervised Scalable Representation Learning for Multivariate Time Series. NeurIPS 2019: 4652-4663 - [c1]Aymeric Dieuleveut, Kumar Kshitij Patel:
Communication trade-offs for Local-SGD with large step size. NeurIPS 2019: 13579-13590 - [i4]Jean-Yves Franceschi, Aymeric Dieuleveut
Unsupervised Scalable Representation Learning for Multivariate Time Series. CoRR abs/1901.10738 (2019) - [i3]Kumar Kshitij Patel, Aymeric Dieuleveut:
Communication trade-offs for synchronized distributed SGD with large step size. CoRR abs/1904.11325 (2019) - 2018
- [i2]Sidak Pal Singh, Andreas Hug, Aymeric Dieuleveut, Martin Jaggi:
Wasserstein is all you need. CoRR abs/1808.09663 (2018) - 2017
- [j1]Aymeric Dieuleveut, Nicolas Flammarion, Francis R. Bach:
Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression. J. Mach. Learn. Res. 18: 101:1-101:51 (2017) - 2016
- [i1]Aymeric Dieuleveut
Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression. CoRR abs/1602.05419 (2016)
Coauthor Index
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