P. Mangold, S. Samsonov, S. Labbi, I. Levin, R. Alami, A. Naumov and E. Moulines,
SCAFFLSA: Quantifying and Eliminating Heterogeneity Bias in Federated
Linear Stochastic Approximation and Temporal Difference Learning,
Preprint,
2024.
[pdf][arxiv]
H. Hendrikx, P. Mangold and A. Bellet,
The Relative Gaussian Mechanism and its Application to Private Gradient Descent,
AISTATS,
2024.
[pdf][arxiv][hal]
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]
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]
P. Mangold, A. Bellet, J. Salmon and M. Tommasi,
Differentially Private Coordinate Descent for Composite Empirical Risk Minimization,
ICML,
2022.
[pdf][arxiv][hal][poster]
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]
