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Mathieu Even
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2020 – today
- 2024
- [c10]Mathieu Even, Anastasia Koloskova, Laurent Massoulié:
Asynchronous SGD on Graphs: a Unified Framework for Asynchronous Decentralized and Federated Optimization. AISTATS 2024: 64-72 - [c9]Kevin Scaman, Mathieu Even, Batiste Le Bars, Laurent Massoulié:
Minimax Excess Risk of First-Order Methods for Statistical Learning with Data-Dependent Oracles. AISTATS 2024: 3709-3717 - [i12]Sayan Biswas, Mathieu Even, Anne-Marie Kermarrec, Laurent Massoulié, Rafael Pires, Rishi Sharma, Martijn de Vos:
Beyond Noise: Privacy-Preserving Decentralized Learning with Virtual Nodes. CoRR abs/2404.09536 (2024) - [i11]Mathieu Even, Luca Ganassali, Jakob Maier, Laurent Massoulié:
Aligning Embeddings and Geometric Random Graphs: Informational Results and Computational Approaches for the Procrustes-Wasserstein Problem. CoRR abs/2405.14532 (2024) - 2023
- [c8]Mathieu Even:
Stochastic Gradient Descent under Markovian Sampling Schemes. ICML 2023: 9412-9439 - [c7]Mathieu Even, Scott Pesme, Suriya Gunasekar, Nicolas Flammarion:
(S)GD over Diagonal Linear Networks: Implicit bias, Large Stepsizes and Edge of Stability. NeurIPS 2023 - [i10]Mathieu Even, Scott Pesme, Suriya Gunasekar, Nicolas Flammarion:
(S)GD over Diagonal Linear Networks: Implicit Regularisation, Large Stepsizes and Edge of Stability. CoRR abs/2302.08982 (2023) - [i9]Mathieu Even:
Stochastic Gradient Descent under Markovian Sampling Schemes. CoRR abs/2302.14428 (2023) - [i8]Kevin Scaman, Mathieu Even, Laurent Massoulié:
Generalization Error of First-Order Methods for Statistical Learning with Generic Oracles. CoRR abs/2307.04679 (2023) - [i7]Mathieu Even, Anastasia Koloskova, Laurent Massoulié:
Asynchronous SGD on Graphs: a Unified Framework for Asynchronous Decentralized and Federated Optimization. CoRR abs/2311.00465 (2023) - 2022
- [c6]Edwige Cyffers, Mathieu Even, Aurélien Bellet, Laurent Massoulié:
Muffliato: Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging. NeurIPS 2022 - [c5]Mathieu Even, Laurent Massoulié, Kevin Scaman:
On Sample Optimality in Personalized Collaborative and Federated Learning. NeurIPS 2022 - [c4]Konstantin Mishchenko, Francis R. Bach, Mathieu Even, Blake E. Woodworth:
Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays. NeurIPS 2022 - [i6]Edwige Cyffers, Mathieu Even, Aurélien Bellet, Laurent Massoulié:
Muffliato: Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging. CoRR abs/2206.05091 (2022) - [i5]Konstantin Mishchenko, Francis R. Bach, Mathieu Even, Blake E. Woodworth:
Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays. CoRR abs/2206.07638 (2022) - 2021
- [c3]Mathieu Even, Laurent Massoulié:
Concentration of Non-Isotropic Random Tensors with Applications to Learning and Empirical Risk Minimization. COLT 2021: 1847-1886 - [c2]Radu-Alexandru Dragomir, Mathieu Even, Hadrien Hendrikx:
Fast Stochastic Bregman Gradient Methods: Sharp Analysis and Variance Reduction. ICML 2021: 2815-2825 - [c1]Mathieu Even, Raphaël Berthier, Francis R. Bach, Nicolas Flammarion, Hadrien Hendrikx, Pierre Gaillard, Laurent Massoulié, Adrien B. Taylor:
Continuized Accelerations of Deterministic and Stochastic Gradient Descents, and of Gossip Algorithms. NeurIPS 2021: 28054-28066 - [i4]Mathieu Even, Laurent Massoulié:
Concentration of Non-Isotropic Random Tensors with Applications to Learning and Empirical Risk Minimization. CoRR abs/2102.04259 (2021) - [i3]Mathieu Even, Hadrien Hendrikx, Laurent Massoulié:
Decentralized Optimization with Heterogeneous Delays: a Continuous-Time Approach. CoRR abs/2106.03585 (2021) - [i2]Mathieu Even, Raphaël Berthier, Francis R. Bach, Nicolas Flammarion, Pierre Gaillard, Hadrien Hendrikx, Laurent Massoulié, Adrien B. Taylor:
A Continuized View on Nesterov Acceleration for Stochastic Gradient Descent and Randomized Gossip. CoRR abs/2106.07644 (2021) - 2020
- [i1]Mathieu Even, Hadrien Hendrikx, Laurent Massoulié:
Asynchrony and Acceleration in Gossip Algorithms. CoRR abs/2011.02379 (2020)
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