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Books and Theses
- 2023
[b1]Paul Mangold:
Exploiting Problem Structure in Privacy-Preserving Optimization and Machine Learning. (Exploitation de la Structure des Problèmes en Optimisation et en Apprentissage Automatique Respectueux de la Vie Privée). University of Lille, France, 2023
Conference and Workshop Papers
- 2024
- [c7]Hadrien Hendrikx, Paul Mangold, Aurélien Bellet:
The Relative Gaussian Mechanism and its Application to Private Gradient Descent. AISTATS 2024: 3079-3087 - 2023
- [c6]Paul Mangold, Aurélien Bellet, Joseph Salmon, Marc Tommasi:
High-Dimensional Private Empirical Risk Minimization by Greedy Coordinate Descent. AISTATS 2023: 4894-4916 - [c5]Paul Mangold, Michaël Perrot, Aurélien Bellet, Marc Tommasi:
Differential Privacy has Bounded Impact on Fairness in Classification. ICML 2023: 23681-23705 - 2022
- [c4]Paul Mangold, Aurélien Bellet, Joseph Salmon, Marc Tommasi:
Differentially Private Coordinate Descent for Composite Empirical Risk Minimization. ICML 2022: 14948-14978 - [c3]Jean Ogier du Terrail, Samy-Safwan Ayed, Edwige Cyffers, Felix Grimberg, Chaoyang He, Regis Loeb, Paul Mangold, Tanguy Marchand, Othmane Marfoq, Erum Mushtaq, Boris Muzellec, Constantin Philippenko, Santiago Silva, Maria Telenczuk, Shadi Albarqouni, Salman Avestimehr, Aurélien Bellet, Aymeric Dieuleveut, Martin Jaggi, Sai Praneeth Karimireddy, Marco Lorenzi, Giovanni Neglia, Marc Tommasi, Mathieu Andreux:
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings. NeurIPS 2022 - 2021
- [c2]Antoine Lamer, Alexandre Filiot, Yannick Bouillard, Paul Mangold, Paul Andrey
, Jessica Schiro:
Specifications for the Routine Implementation of Federated Learning in Hospitals Networks. MIE 2021: 128-132 - 2020
- [c1]Paul Mangold, Alexandre Filiot, Mouhamed Moussa, Vincent Sobanski, Grégoire Ficheur, Paul Andrey, Antoine Lamer:
A Decentralized Framework for Biostatistics and Privacy Concerns. EFMI-STC 2020: 137-141
Informal and Other Publications
- 2024
- [i6]Paul Mangold, Sergey Samsonov, Safwan Labbi, Ilya Levin, Réda Alami, Alexey Naumov, Eric Moulines:
SCAFFLSA: Quantifying and Eliminating Heterogeneity Bias in Federated Linear Stochastic Approximation and Temporal Difference Learning. CoRR abs/2402.04114 (2024) - 2023
- [i5]Hadrien Hendrikx, Paul Mangold, Aurélien Bellet:
The Relative Gaussian Mechanism and its Application to Private Gradient Descent. CoRR abs/2308.15250 (2023) - 2022
- [i4]Paul Mangold, Aurélien Bellet, Joseph Salmon, Marc Tommasi:
High-Dimensional Private Empirical Risk Minimization by Greedy Coordinate Descent. CoRR abs/2207.01560 (2022) - [i3]Jean Ogier du Terrail, Samy-Safwan Ayed, Edwige Cyffers, Felix Grimberg, Chaoyang He, Regis Loeb, Paul Mangold, Tanguy Marchand, Othmane Marfoq, Erum Mushtaq, Boris Muzellec, Constantin Philippenko, Santiago Silva, Maria Telenczuk, Shadi Albarqouni, Salman Avestimehr, Aurélien Bellet, Aymeric Dieuleveut, Martin Jaggi, Sai Praneeth Karimireddy, Marco Lorenzi, Giovanni Neglia, Marc Tommasi, Mathieu Andreux:
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings. CoRR abs/2210.04620 (2022) - [i2]Paul Mangold, Michaël Perrot, Aurélien Bellet, Marc Tommasi:
Fairness Certificates for Differentially Private Classification. CoRR abs/2210.16242 (2022) - 2021
- [i1]Paul Mangold, Aurélien Bellet, Joseph Salmon, Marc Tommasi:
Differentially Private Coordinate Descent for Composite Empirical Risk Minimization. CoRR abs/2110.11688 (2021)
Coauthor Index
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