Mathieu Even

According to our database, Mathieu Even authored at least 17 papers between 2020 and 2024.

Collaborative distances:

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

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

CoRR, 2024

Beyond Noise: Privacy-Preserving Decentralized Learning with Virtual Nodes.

Sayan Biswas, Mathieu Even, Anne-Marie Kermarrec, Laurent Massoulié, Rafael Pires, Rishi Sharma, Martijn de Vos

CoRR, 2024

Minimax Excess Risk of First-Order Methods for Statistical Learning with Data-Dependent Oracles.

Kevin Scaman, Mathieu Even, Batiste Le Bars, Laurent Massoulié

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

Asynchronous SGD on Graphs: a Unified Framework for Asynchronous Decentralized and Federated Optimization.

Mathieu Even, Anastasia Koloskova, Laurent Massoulié

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

2023

Generalization Error of First-Order Methods for Statistical Learning with Generic Oracles.

Kevin Scaman, Mathieu Even, Laurent Massoulié

CoRR, 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

(S)GD over Diagonal Linear Networks: Implicit bias, Large Stepsizes and Edge of Stability.

Mathieu Even, Scott Pesme, Suriya Gunasekar, Nicolas Flammarion

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Stochastic Gradient Descent under Markovian Sampling Schemes.

Mathieu Even

Proceedings of the International Conference on Machine Learning, 2023

2022

Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays.

Konstantin Mishchenko, Francis R. Bach, Mathieu Even, Blake E. Woodworth

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

On Sample Optimality in Personalized Collaborative and Federated Learning.

Mathieu Even, Laurent Massoulié, Kevin Scaman

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

Muffliato: Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging.

Edwige Cyffers, Mathieu Even, Aurélien Bellet, Laurent Massoulié

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

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

Decentralized Optimization with Heterogeneous Delays: a Continuous-Time Approach.

Mathieu Even, Hadrien Hendrikx, Laurent Massoulié

CoRR, 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

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

Fast Stochastic Bregman Gradient Methods: Sharp Analysis and Variance Reduction.

Radu-Alexandru Dragomir, Mathieu Even, Hadrien Hendrikx

Proceedings of the 38th International Conference on Machine Learning, 2021

Concentration of Non-Isotropic Random Tensors with Applications to Learning and Empirical Risk Minimization.

Mathieu Even, Laurent Massoulié

Proceedings of the Conference on Learning Theory, 2021

2020

Asynchrony and Acceleration in Gossip Algorithms.

Mathieu Even, Hadrien Hendrikx, Laurent Massoulié

CoRR, 2020