Paul Mangold

According to our database1, Paul Mangold authored at least 12 papers between 2020 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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PhD thesis 
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Links

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Bibliography

2024
Federated UCBVI: Communication-Efficient Federated Regret Minimization with Heterogeneous Agents.
CoRR, 2024

Joint Channel Selection using FedDRL in V2X.
CoRR, 2024

SCAFFLSA: Quantifying and Eliminating Heterogeneity Bias in Federated Linear Stochastic Approximation and Temporal Difference Learning.
CoRR, 2024

The Relative Gaussian Mechanism and its Application to Private Gradient Descent.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023
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).
PhD thesis, 2023

Differential Privacy has Bounded Impact on Fairness in Classification.
Proceedings of the International Conference on Machine Learning, 2023

High-Dimensional Private Empirical Risk Minimization by Greedy Coordinate Descent.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022
Fairness Certificates for Differentially Private Classification.
CoRR, 2022

FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Differentially Private Coordinate Descent for Composite Empirical Risk Minimization.
Proceedings of the International Conference on Machine Learning, 2022

2021
Specifications for the Routine Implementation of Federated Learning in Hospitals Networks.
Proceedings of the Public Health and Informatics, 2021

2020
A Decentralized Framework for Biostatistics and Privacy Concerns.
Proceedings of the Integrated Citizen Centered Digital Health and Social Care - Citizens as Data Producers and Service co-Creators, 2020


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