S. Labbi, P. Mangold, D. Tiapkin and E. Moulines,
On Global Convergence Rates for Federated Policy Gradient under Heterogeneous Environment,
2025.
[pdf][arxiv][code]
P. Mangold, A. Durmus, A. Dieuleveut and E. Moulines,
Scaffold with Stochastic Gradients: New Analysis with Linear Speed-Up,
ICML,
2025.
[pdf][arxiv][code]
P. Mangold, A. Durmus, A. Dieuleveut, S. Samsonov and E. Moulines,
Refined Analysis of Constant Step Size Federated Averaging and Federated Richardson-Romberg Extrapolation,
AISTATS,
2025.
[pdf][arxiv][hal][code]
S. Labbi, D. Tiapkin, L. Mancini, P. Mangold and E. Moulines,
Federated UCBVI: Communication-Efficient Federated Regret Minimization with Heterogeneous Agents,
AISTATS,
2025.
[pdf][arxiv][code]
2024
L. Mancini, S. Labbi, K. Abed Meraim, F. Boukhalfa, A. Durmus, P. Mangold and E. Moulines,
Joint Channel Selection using FedDRL in V2X,
arXiv preprint,
2024.
[pdf][arxiv]
P. Mangold, S. Samsonov, S. Labbi, I. Levin, R. Alami, A. Naumov and E. Moulines,
SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning,
NeurIPS,
2024.
[pdf][arxiv][hal][poster][code]
H. Hendrikx, P. Mangold and A. Bellet,
The Relative Gaussian Mechanism and its Application to Private Gradient Descent,
AISTATS,
2024.
[pdf][arxiv][hal][code]
2023
P. Mangold,
Exploiting Problem Structure in Privacy-Preserving Optimization and Machine Learning,
PhD Thesis,
2023.
[pdf][hal][slides]
P. Mangold, M. Perrot, A. Bellet and M. Tommasi,
Differential Privacy has Bounded Impact on Fairness in Classification,
ICML,
2023.
[pdf][arxiv][hal][poster][code]
P. Mangold, A. Bellet, J. Salmon and M. Tommasi,
High-Dimensional Private Empirical Risk Minimization by Greedy Coordinate Descent,
AISTATS,
2023.
[pdf][arxiv][hal][poster]
2022
JO. du Terrail, S. Ayed, E. Cyffers, F. Grimberg, C. He, R. Loeb, P. Mangold, T. Marchand, O. Marfoq, E. Mushtaq, B. Muzellec, C. Philippenko, S. Silva, M. Teleńczuk, S. Albarqouni, S. Avestimehr, A. Bellet, A. Dieuleveut, M. Jaggi, SP. Karimireddy, M. Lorenzi, G. Neglia, M. Tommasi and M. Andreux,
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings,
NeurIPS,
2022.
[pdf][arxiv][hal][code]
P. Mangold, A. Bellet, J. Salmon and M. Tommasi,
Differentially Private Coordinate Descent for Composite Empirical Risk Minimization,
ICML,
2022.
[pdf][arxiv][hal][poster][code]
2021
A. Lamer, A. Filiot, Y. Bouillard, P. Mangold, P. Andrey and J. Schiro,
Specifications for the routine implementation of federated learning in hospitals networks,
Studies in health technology and informatics,
2021.
[pdf][hal]
