1 code implementation • 28 Nov 2023 • Jean Ogier du Terrail, Quentin Klopfenstein, Honghao Li, Imke Mayer, Nicolas Loiseau, Mohammad Hallal, Félix Balazard, Mathieu Andreux
External control arms (ECA) can inform the early clinical development of experimental drugs and provide efficacy evidence for regulatory approval in non-randomized settings.
1 code implementation • 10 Oct 2022 • 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 Teleńczuk, Shadi Albarqouni, Salman Avestimehr, Aurélien Bellet, Aymeric Dieuleveut, Martin Jaggi, Sai Praneeth Karimireddy, Marco Lorenzi, Giovanni Neglia, Marc Tommasi, Mathieu Andreux
In this work, we propose a novel cross-silo dataset suite focused on healthcare, FLamby (Federated Learning AMple Benchmark of Your cross-silo strategies), to bridge the gap between theory and practice of cross-silo FL.
no code implementations • 4 Oct 2022 • Tanguy Marchand, Boris Muzellec, Constance Beguier, Jean Ogier du Terrail, Mathieu Andreux
The Yeo-Johnson (YJ) transformation is a standard parametrized per-feature unidimensional transformation often used to Gaussianize features in machine learning.
1 code implementation • 8 Jan 2021 • Constance Beguier, Jean Ogier du Terrail, Iqraa Meah, Mathieu Andreux, Eric W. Tramel
Since 2014, the NIH funded iDASH (integrating Data for Analysis, Anonymization, SHaring) National Center for Biomedical Computing has hosted yearly competitions on the topic of private computing for genomic data.
no code implementations • 17 Aug 2020 • Mathieu Andreux, Jean Ogier du Terrail, Constance Beguier, Eric W. Tramel
While federated learning is a promising approach for training deep learning models over distributed sensitive datasets, it presents new challenges for machine learning, especially when applied in the medical domain where multi-centric data heterogeneity is common.
no code implementations • 29 Jul 2020 • Constance Beguier, Mathieu Andreux, Eric W. Tramel
Federated Learning enables one to jointly train a machine learning model across distributed clients holding sensitive datasets.
no code implementations • 16 Jun 2020 • Mathieu Andreux, Andre Manoel, Romuald Menuet, Charlie Saillard, Chloé Simpson
Building machine learning models from decentralized datasets located in different centers with federated learning (FL) is a promising approach to circumvent local data scarcity while preserving privacy.
2 code implementations • 28 Dec 2018 • Mathieu Andreux, Tomás Angles, Georgios Exarchakis, Roberto Leonarduzzi, Gaspar Rochette, Louis Thiry, John Zarka, Stéphane Mallat, Joakim andén, Eugene Belilovsky, Joan Bruna, Vincent Lostanlen, Muawiz Chaudhary, Matthew J. Hirn, Edouard Oyallon, Sixin Zhang, Carmine Cella, Michael Eickenberg
The wavelet scattering transform is an invariant signal representation suitable for many signal processing and machine learning applications.