Asynchrony and Acceleration in Gossip Algorithms. - Mathieu Even; Hadrien Hendrikx; Laurent Massoulié (CoRR, 2020)

Concentration of Non-Isotropic Random Tensors with Applications to Learning and Empirical Risk Minimization. - Mathieu Even; Laurent Massoulié (COLT, 2021)

Fast Stochastic Bregman Gradient Methods: Sharp Analysis and Variance Reduction. - Radu-Alexandru Dragomir; Mathieu Even; Hadrien Hendrikx (ICML, 2021)

Continuized Accelerations of Deterministic and Stochastic Gradient Descents, and of Gossip Algorithms. - Mathieu Even; Raphaël Berthier; Francis R. Bach; Nicolas Flammarion; Hadrien Hendrikx; Pierre Gaillard; Laurent Massoulié; Adrien B. Taylor (NeurIPS, 2021)

Decentralized Optimization with Heterogeneous Delays: a Continuous-Time Approach. - Mathieu Even; Hadrien Hendrikx; Laurent Massoulié (CoRR, 2021)

A Continuized View on Nesterov Acceleration for Stochastic Gradient Descent and Randomized Gossip. - Mathieu Even; Raphaël Berthier; Francis R. Bach; Nicolas Flammarion; Pierre Gaillard; Hadrien Hendrikx; Laurent Massoulié; Adrien B. Taylor (CoRR, 2021)

Muffliato: Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging. - Edwige Cyffers; Mathieu Even; Aurélien Bellet; Laurent Massoulié (NeurIPS, 2022)

On Sample Optimality in Personalized Collaborative and Federated Learning. - Mathieu Even; Laurent Massoulié; Kevin Scaman (NeurIPS, 2022)

Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays. - Konstantin Mishchenko; Francis R. Bach; Mathieu Even; Blake E. Woodworth (NeurIPS, 2022)

Stochastic Gradient Descent under Markovian Sampling Schemes. - Mathieu Even (ICML, 2023)

(S)GD over Diagonal Linear Networks: Implicit bias, Large Stepsizes and Edge of Stability. - Mathieu Even; Scott Pesme; Suriya Gunasekar; Nicolas Flammarion (NeurIPS, 2023)

(S)GD over Diagonal Linear Networks: Implicit Regularisation, Large Stepsizes and Edge of Stability. - Mathieu Even; Scott Pesme; Suriya Gunasekar; Nicolas Flammarion (CoRR, 2023)

Generalization Error of First-Order Methods for Statistical Learning with Generic Oracles. - Kevin Scaman; Mathieu Even; Laurent Massoulié (CoRR, 2023)

Asynchronous SGD on Graphs: a Unified Framework for Asynchronous Decentralized and Federated Optimization. - Mathieu Even; Anastasia Koloskova; Laurent Massoulié (AISTATS, 2024)

Minimax Excess Risk of First-Order Methods for Statistical Learning with Data-Dependent Oracles. - Kevin Scaman; Mathieu Even; Batiste Le Bars; Laurent Massoulié (AISTATS, 2024)

Aligning Embeddings and Geometric Random Graphs: Informational Results and Computational Approaches for the Procrustes-Wasserstein Problem. - Mathieu Even; Luca Ganassali; Jakob Maier; Laurent Massoulié (NeurIPS, 2024)

Beyond Noise: Privacy-Preserving Decentralized Learning with Virtual Nodes. - Sayan Biswas; Mathieu Even; Anne-Marie Kermarrec; Laurent Massoulié; Rafael Pires 001; Rishi Sharma 001; Martijn de Vos (CoRR, 2024)

Towards Decentralization, Asynchrony, Privacy and Personalization in Federated Learning. (Asynchronie, décentralisation, vie privée et personnalisation en apprentissage fédéré). - Mathieu Even (2024)

Long-Context Linear System Identification. - Oguz Kaan Yüksel; Mathieu Even; Nicolas Flammarion (ICLR, 2025)

Noiseless Privacy-Preserving Decentralized Learning. - Sayan Biswas; Mathieu Even; Anne-Marie Kermarrec; Laurent Massoulié; Rafael Pires 001; Rishi Sharma 001; Martijn de Vos (Proc. Priv. Enhancing Technol., 2025)

Meta-learning of shared linear representations beyond well-specified linear regression. - Mathieu Even; Laurent Massoulié (CoRR, 2025)

Asynchronous Speedup in Decentralized Optimization. - Mathieu Even; Hadrien Hendrikx; Laurent Massoulié (IEEE Trans. Autom. Control., 2025)

Model Agnostic Differentially Private Causal Inference. - Christian Janos Lebeda; Mathieu Even; Aurélien Bellet; Julie Josse (CoRR, 2025)