Aymeric Dieuleveut

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 42

Top Topics

Convergence Rate

Optimization Method

Top Venues

Publications

Damien Ferbach, Baptiste Goujaud, Gauthier Gidel, Aymeric Dieuleveut
Proving Linear Mode Connectivity of Neural Networks via Optimal Transport. AISTATS (2024)
Mahmoud Hegazy, Rémi Leluc, Cheuk Ting Li, Aymeric Dieuleveut
Compression with Exact Error Distribution for Federated Learning. AISTATS (2024)
Rémi Leluc, Aymeric Dieuleveut, François Portier, Johan Segers, Aigerim Zhuman
Sliced-Wasserstein Estimation with Spherical Harmonics as Control Variates. CoRR (2024)
Renaud Gaucher, Hadrien Hendrikx, Aymeric Dieuleveut
Byzantine-Robust Gossip: Insights from a Dual Approach. CoRR (2024)
Alexis Ayme, Claire Boyer, Aymeric Dieuleveut, Erwan Scornet
Naive imputation implicitly regularizes high-dimensional linear models. ICML (2023)
Mahmoud Hegazy, Rémi Leluc, Cheuk Ting Li, Aymeric Dieuleveut
Compression with Exact Error Distribution for Federated Learning. CoRR (2023)
Aymeric Dieuleveut, Gersende Fort, Eric Moulines, Hoi-To Wai
Stochastic Approximation Beyond Gradient for Signal Processing and Machine Learning. IEEE Trans. Signal Process. 71 (2023)
Margaux Zaffran, Aymeric Dieuleveut, Julie Josse, Yaniv Romano
Conformal Prediction with Missing Values. ICML (2023)
Baptiste Goujaud, Aymeric Dieuleveut, Adrien B. Taylor
Counter-Examples in First-Order Optimization: A Constructive Approach. IEEE Control. Syst. Lett. 7 (2023)
Baptiste Goujaud, Aymeric Dieuleveut, Adrien B. Taylor
On Fundamental Proof Structures in First-Order Optimization. CDC (2023)
Damien Ferbach, Baptiste Goujaud, Gauthier Gidel, Aymeric Dieuleveut
Proving Linear Mode Connectivity of Neural Networks via Optimal Transport. CoRR (2023)
Constantin Philippenko, Aymeric Dieuleveut
Compressed and distributed least-squares regression: convergence rates with applications to Federated Learning. CoRR (2023)
Margaux Zaffran, Aymeric Dieuleveut, Julie Josse, Yaniv Romano
Conformal Prediction with Missing Values. CoRR (2023)
Margaux Zaffran, Olivier Féron, Yannig Goude, Julie Josse, Aymeric Dieuleveut
Adaptive Conformal Predictions for Time Series. ICML (2022)
Baptiste Goujaud, Damien Scieur, Aymeric Dieuleveut, Adrien B. Taylor, Fabian Pedregosa
Super-Acceleration with Cyclical Step-sizes. AISTATS (2022)
Margaux Zaffran, Aymeric Dieuleveut, Olivier Féron, Yannig Goude, Julie Josse
Adaptive Conformal Predictions for Time Series. CoRR (2022)
Alexis Ayme, Claire Boyer, Aymeric Dieuleveut, Erwan Scornet
Minimax rate of consistency for linear models with missing values. CoRR (2022)
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 S. Silva R., 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 (2022)
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 (2022)
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)
Maxime Vono, Vincent Plassier, Alain Durmus, Aymeric Dieuleveut, Eric Moulines
QLSD: Quantised Langevin Stochastic Dynamics for Bayesian Federated Learning. AISTATS (2022)
Maxence Noble, Aurélien Bellet, Aymeric Dieuleveut
Differentially Private Federated Learning on Heterogeneous Data. AISTATS (2022)
Alexis Ayme, Claire Boyer, Aymeric Dieuleveut, Erwan Scornet
Near-optimal rate of consistency for linear models with missing values. ICML (2022)
Constantin Philippenko, Aymeric Dieuleveut
Preserved central model for faster bidirectional compression in distributed settings. NeurIPS (2021)
Aymeric Dieuleveut, Gersende Fort, Eric Moulines, Geneviève Robin
Federated Expectation Maximization with heterogeneity mitigation and variance reduction. CoRR (2021)
Maxence Noble, Aurélien Bellet, Aymeric Dieuleveut
Differentially Private Federated Learning on Heterogeneous Data. CoRR (2021)
Constantin Philippenko, Aymeric Dieuleveut
Preserved central model for faster bidirectional compression in distributed settings. CoRR (2021)
Maxime Vono, Vincent Plassier, Alain Durmus, Aymeric Dieuleveut, Eric Moulines
QLSD: Quantised Langevin stochastic dynamics for Bayesian federated learning. CoRR (2021)
Aymeric Dieuleveut, Gersende Fort, Eric Moulines, Geneviève Robin
Federated-EM with heterogeneity mitigation and variance reduction. NeurIPS (2021)
Sidak Pal Singh, Andreas Hug, Aymeric Dieuleveut, Martin Jaggi
Context Mover's Distance & Barycenters: Optimal Transport of Contexts for Building Representations. AISTATS (2020)
Constantin Philippenko, Aymeric Dieuleveut
Artemis: tight convergence guarantees for bidirectional compression in Federated Learning. CoRR (2020)
Aude Sportisse, Claire Boyer, Aymeric Dieuleveut, Julie Josse
Debiasing Averaged Stochastic Gradient Descent to handle missing values. NeurIPS (2020)
Scott Pesme, Aymeric Dieuleveut, Nicolas Flammarion
On Convergence-Diagnostic based Step Sizes for Stochastic Gradient Descent. ICML (2020)
Scott Pesme, Aymeric Dieuleveut, Nicolas Flammarion
On Convergence-Diagnostic based Step Sizes for Stochastic Gradient Descent. CoRR (2020)
Kumar Kshitij Patel, Aymeric Dieuleveut
Communication trade-offs for synchronized distributed SGD with large step size. CoRR (2019)
Jean-Yves Franceschi, Aymeric Dieuleveut, Martin Jaggi
Unsupervised Scalable Representation Learning for Multivariate Time Series. NeurIPS (2019)
Sidak Pal Singh, Andreas Hug, Aymeric Dieuleveut, Martin Jaggi
Context Mover's Distance & Barycenters: Optimal transport of contexts for building representations. DGS@ICLR (2019)
Jean-Yves Franceschi, Aymeric Dieuleveut, Martin Jaggi
Unsupervised Scalable Representation Learning for Multivariate Time Series. CoRR (2019)
Aymeric Dieuleveut, Kumar Kshitij Patel
Communication trade-offs for Local-SGD with large step size. NeurIPS (2019)
Sidak Pal Singh, Andreas Hug, Aymeric Dieuleveut, Martin Jaggi
Wasserstein is all you need. CoRR (2018)
Aymeric Dieuleveut, Nicolas Flammarion, Francis R. Bach
Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression. J. Mach. Learn. Res. 18 (2017)
Aymeric Dieuleveut, Nicolas Flammarion, Francis R. Bach
Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression. CoRR (2016)