Aymeric Dieuleveut

According to our database, Aymeric Dieuleveut authored at least 33 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

PEPit: computer-assisted worst-case analyses of first-order optimization methods in Python.

Baptiste Goujaud, Céline Moucer, François Glineur, Julien M. Hendrickx, Adrien B. Taylor, Aymeric Dieuleveut

Math. Program. Comput., September, 2024

Short Paper - Quadratic minimization: from conjugate gradient to an adaptive Polyak's momentum method with Polyak step-sizes.

Baptiste Goujaud, Adrien B. Taylor, Aymeric Dieuleveut

Open J. Math. Optim., 2024

Refined Analysis of Federated Averaging's Bias and Federated Richardson-Romberg Extrapolation.

Paul Mangold, Alain Durmus, Aymeric Dieuleveut, Sergey Samsonov, Eric Moulines

CoRR, 2024

Byzantine-Robust Gossip: Insights from a Dual Approach.

Renaud Gaucher, Hadrien Hendrikx, Aymeric Dieuleveut

CoRR, 2024

Sliced-Wasserstein Estimation with Spherical Harmonics as Control Variates.

Rémi Leluc, Aymeric Dieuleveut, François Portier, Johan Segers, Aigerim Zhuman

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Random features models: a way to study the success of naive imputation.

Alexis Ayme, Claire Boyer, Aymeric Dieuleveut, Erwan Scornet

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Compression with Exact Error Distribution for Federated Learning.

Mahmoud Hegazy, Rémi Leluc, Cheuk Ting Li, Aymeric Dieuleveut

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

Proving Linear Mode Connectivity of Neural Networks via Optimal Transport.

Damien Ferbach, Baptiste Goujaud, Gauthier Gidel, Aymeric Dieuleveut

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Stochastic Approximation Beyond Gradient for Signal Processing and Machine Learning.

Aymeric Dieuleveut, Gersende Fort, Eric Moulines, Hoi-To Wai

IEEE Trans. Signal Process., 2023

Counter-Examples in First-Order Optimization: A Constructive Approach.

Baptiste Goujaud, Aymeric Dieuleveut, Adrien B. Taylor

IEEE Control. Syst. Lett., 2023

Compressed and distributed least-squares regression: convergence rates with applications to Federated Learning.

Constantin Philippenko, Aymeric Dieuleveut

CoRR, 2023

Conformal Prediction with Missing Values.

Margaux Zaffran, Aymeric Dieuleveut, Julie Josse, Yaniv Romano

Proceedings of the International Conference on Machine Learning, 2023

Naive imputation implicitly regularizes high-dimensional linear models.

Alexis Ayme, Claire Boyer, Aymeric Dieuleveut, Erwan Scornet

Proceedings of the International Conference on Machine Learning, 2023

On Fundamental Proof Structures in First-Order Optimization.

Baptiste Goujaud, Aymeric Dieuleveut, Adrien B. Taylor

Proceedings of the 62nd IEEE Conference on Decision and Control, 2023

2022

Minimax rate of consistency for linear models with missing values.

Alexis Ayme, Claire Boyer, Aymeric Dieuleveut, Erwan Scornet

CoRR, 2022

FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings.

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

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Adaptive Conformal Predictions for Time Series.

Margaux Zaffran, Olivier Féron, Yannig Goude, Julie Josse, Aymeric Dieuleveut

Proceedings of the International Conference on Machine Learning, 2022

Near-optimal rate of consistency for linear models with missing values.

Alexis Ayme, Claire Boyer, Aymeric Dieuleveut, Erwan Scornet

Proceedings of the International Conference on Machine Learning, 2022

QLSD: Quantised Langevin Stochastic Dynamics for Bayesian Federated Learning.

Maxime Vono, Vincent Plassier, Alain Durmus, Aymeric Dieuleveut, Eric Moulines

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Differentially Private Federated Learning on Heterogeneous Data.

Maxence Noble, Aurélien Bellet, Aymeric Dieuleveut

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Super-Acceleration with Cyclical Step-sizes.

Baptiste Goujaud, Damien Scieur, Aymeric Dieuleveut, Adrien B. Taylor, Fabian Pedregosa

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Federated Expectation Maximization with heterogeneity mitigation and variance reduction.

Aymeric Dieuleveut, Gersende Fort, Eric Moulines, Geneviève Robin

CoRR, 2021

Preserved central model for faster bidirectional compression in distributed settings.

Constantin Philippenko, Aymeric Dieuleveut

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Federated-EM with heterogeneity mitigation and variance reduction.

Aymeric Dieuleveut, Gersende Fort, Eric Moulines, Geneviève Robin

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Artemis: tight convergence guarantees for bidirectional compression in Federated Learning.

Constantin Philippenko, Aymeric Dieuleveut

CoRR, 2020

Debiasing Averaged Stochastic Gradient Descent to handle missing values.

Aude Sportisse, Claire Boyer, Aymeric Dieuleveut, Julie Josse

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

On Convergence-Diagnostic based Step Sizes for Stochastic Gradient Descent.

Scott Pesme, Aymeric Dieuleveut, Nicolas Flammarion

Proceedings of the 37th International Conference on Machine Learning, 2020

Context Mover's Distance & Barycenters: Optimal Transport of Contexts for Building Representations.

Sidak Pal Singh, Andreas Hug, Aymeric Dieuleveut, Martin Jaggi

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Communication trade-offs for synchronized distributed SGD with large step size.

Kumar Kshitij Patel, Aymeric Dieuleveut

CoRR, 2019

Unsupervised Scalable Representation Learning for Multivariate Time Series.

Jean-Yves Franceschi, Aymeric Dieuleveut, Martin Jaggi

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Communication trade-offs for Local-SGD with large step size.

Aymeric Dieuleveut, Kumar Kshitij Patel

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Wasserstein is all you need.

Sidak Pal Singh, Andreas Hug, Aymeric Dieuleveut, Martin Jaggi

CoRR, 2018

2017

Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression.

Aymeric Dieuleveut, Nicolas Flammarion, Francis R. Bach

J. Mach. Learn. Res., 2017