Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression. - Aymeric Dieuleveut; Nicolas Flammarion; Francis R. Bach (J. Mach. Learn. Res., 2017)

Wasserstein is all you need. - Sidak Pal Singh; Andreas Hug; Aymeric Dieuleveut; Martin Jaggi (CoRR, 2018)

Communication trade-offs for Local-SGD with large step size. - Aymeric Dieuleveut; Kumar Kshitij Patel (NeurIPS, 2019)

Unsupervised Scalable Representation Learning for Multivariate Time Series. - Jean-Yves Franceschi; Aymeric Dieuleveut; Martin Jaggi (NeurIPS, 2019)

Communication trade-offs for synchronized distributed SGD with large step size. - Kumar Kshitij Patel; Aymeric Dieuleveut (CoRR, 2019)

Context Mover's Distance & Barycenters: Optimal Transport of Contexts for Building Representations. - Sidak Pal Singh; Andreas Hug; Aymeric Dieuleveut; Martin Jaggi (AISTATS, 2020)

On Convergence-Diagnostic based Step Sizes for Stochastic Gradient Descent. - Scott Pesme; Aymeric Dieuleveut; Nicolas Flammarion (ICML, 2020)

Debiasing Averaged Stochastic Gradient Descent to handle missing values. - Aude Sportisse; Claire Boyer; Aymeric Dieuleveut; Julie Josse (NeurIPS, 2020)

Artemis: tight convergence guarantees for bidirectional compression in Federated Learning. - Constantin Philippenko; Aymeric Dieuleveut (CoRR, 2020)

Federated-EM with heterogeneity mitigation and variance reduction. - Aymeric Dieuleveut; Gersende Fort; Eric Moulines; Geneviève Robin (NeurIPS, 2021)

Preserved central model for faster bidirectional compression in distributed settings. - Constantin Philippenko; Aymeric Dieuleveut (NeurIPS, 2021)

Federated Expectation Maximization with heterogeneity mitigation and variance reduction. - Aymeric Dieuleveut; Gersende Fort; Eric Moulines; Geneviève Robin (CoRR, 2021)

Super-Acceleration with Cyclical Step-sizes. - Baptiste Goujaud; Damien Scieur; Aymeric Dieuleveut; Adrien B. Taylor; Fabian Pedregosa (AISTATS, 2022)

Differentially Private Federated Learning on Heterogeneous Data. - Maxence Noble; Aurélien Bellet; Aymeric Dieuleveut (AISTATS, 2022)

QLSD: Quantised Langevin Stochastic Dynamics for Bayesian Federated Learning. - Maxime Vono; Vincent Plassier; Alain Durmus; Aymeric Dieuleveut; Eric Moulines (AISTATS, 2022)

Near-optimal rate of consistency for linear models with missing values. - Alexis Ayme; Claire Boyer; Aymeric Dieuleveut; Erwan Scornet (ICML, 2022)

Adaptive Conformal Predictions for Time Series. - Margaux Zaffran; Olivier Féron; Yannig Goude; Julie Josse; Aymeric Dieuleveut (ICML, 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 001; Regis Loeb; Paul Mangold; Tanguy Marchand; Othmane Marfoq; Erum Mushtaq; Boris Muzellec; Constantin Philippenko; et al. (NeurIPS, 2022)

Minimax rate of consistency for linear models with missing values. - Alexis Ayme; Claire Boyer; Aymeric Dieuleveut; Erwan Scornet (CoRR, 2022)

On Fundamental Proof Structures in First-Order Optimization. - Baptiste Goujaud; Aymeric Dieuleveut; Adrien B. Taylor (CDC, 2023)

Naive imputation implicitly regularizes high-dimensional linear models. - Alexis Ayme; Claire Boyer; Aymeric Dieuleveut; Erwan Scornet (ICML, 2023)

Conformal Prediction with Missing Values. - Margaux Zaffran; Aymeric Dieuleveut; Julie Josse; Yaniv Romano (ICML, 2023)

Compressed and distributed least-squares regression: convergence rates with applications to Federated Learning. - Constantin Philippenko; Aymeric Dieuleveut (CoRR, 2023)

Counter-Examples in First-Order Optimization: A Constructive Approach. - Baptiste Goujaud; Aymeric Dieuleveut; Adrien B. Taylor (IEEE Control. Syst. Lett., 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)

Proving Linear Mode Connectivity of Neural Networks via Optimal Transport. - Damien Ferbach; Baptiste Goujaud; Gauthier Gidel; Aymeric Dieuleveut (AISTATS, 2024)

Compression with Exact Error Distribution for Federated Learning. - Mahmoud Hegazy; Rémi Leluc; Cheuk Ting Li; Aymeric Dieuleveut (AISTATS, 2024)

Random features models: a way to study the success of naive imputation. - Alexis Ayme; Claire Boyer; Aymeric Dieuleveut; Erwan Scornet (ICML, 2024)

Sliced-Wasserstein Estimation with Spherical Harmonics as Control Variates. - Rémi Leluc; Aymeric Dieuleveut; François Portier; Johan Segers; Aigerim Zhuman (ICML, 2024)

Byzantine-Robust Gossip: Insights from a Dual Approach. - Renaud Gaucher; Hadrien Hendrikx; Aymeric Dieuleveut (CoRR, 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)

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)

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., 2024)

Refined Analysis of Constant Step Size Federated Averaging and Federated Richardson-Romberg Extrapolation. - Paul Mangold; Alain Oliviero Durmus; Aymeric Dieuleveut; Sergey Samsonov; Eric Moulines (AISTATS, 2025)

Scaffold with Stochastic Gradients: New Analysis with Linear Speed-Up. - Paul Mangold; Alain Durmus; Aymeric Dieuleveut; Eric Moulines (CoRR, 2025)

Tight analyses of first-order methods with error feedback. - Daniel Berg Thomsen; Adrien B. Taylor; Aymeric Dieuleveut (CoRR, 2025)

Valid Selection among Conformal Sets. - Mahmoud Hegazy; Liviu Aolaritei; Michael I. Jordan; Aymeric Dieuleveut (CoRR, 2025)

Federated Majorize-Minimization: Beyond Parameter Aggregation. - Aymeric Dieuleveut; Gersende Fort; Mahmoud Hegazy; Hoi-To Wai (CoRR, 2025)