Théo Ryffel

Publication Activity (10 Years)

Years Active: 2018-2023
Publications (10 Years): 13

Top Topics

Privacy Preserving

Weakly Supervised

Stochastic Gradient

Top Venues

Nat. Mach. Intell.

Publications

Simon Meoni, Théo Ryffel, Éric Villemonte de la Clergerie
Annotation d'entités cliniques en utilisant les Larges Modèles de Langue. CORIA-TALN (1) (2023)
Simon Meoni, Théo Ryffel, Éric de la Clergerie
Annotate French Clinical Data Using Large Language Model Predictions. ICHI (2023)
Simon Meoni, Éric de la Clergerie, Théo Ryffel
Large Language Models as Instructors: A Study on Multilingual Clinical Entity Extraction. BioNLP@ACL (2023)
Théo Ryffel, Francis R. Bach, David Pointcheval
Differential Privacy Guarantees for Stochastic Gradient Langevin Dynamics. CoRR (2022)
Théo Ryffel, Pierre Tholoniat, David Pointcheval, Francis R. Bach
AriaNN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing. Proc. Priv. Enhancing Technol. 2022 (1) (2022)
Adam James Hall, Madhava Jay, Tudor Cebere, Bogdan Cebere, Koen Lennart van der Veen, George Muraru, Tongye Xu, Patrick Cason, William Abramson, Ayoub Benaissa, Chinmay Shah, Alan Aboudib, Théo Ryffel, Kritika Prakash, Tom Titcombe, Varun Kumar Khare, Maddie Shang, Ionesio Junior, Animesh Gupta, Jason Paumier, Nahua Kang, Vova Manannikov, Andrew Trask
Syft 0.5: A Platform for Universally Deployable Structured Transparency. CoRR (2021)
Georgios Kaissis, Alexander Ziller, Jonathan Passerat-Palmbach, Théo Ryffel, Dmitrii Usynin, Andrew Trask, Ionésio Lima, Jason Mancuso, Friederike Jungmann, Marc-Matthias Steinborn, Andreas Saleh, Marcus R. Makowski, Daniel Rueckert, Rickmer Braren
End-to-end privacy preserving deep learning on multi-institutional medical imaging. Nat. Mach. Intell. 3 (6) (2021)
Alexander Ziller, Jonathan Passerat-Palmbach, Théo Ryffel, Dmitrii Usynin, Andrew Trask, Ionésio Da Lima Costa Junior, Jason Mancuso, Marcus R. Makowski, Daniel Rueckert, Rickmer Braren, Georgios Kaissis
Privacy-preserving medical image analysis. CoRR (2020)
Miles Brundage, Shahar Avin, Jasmine Wang, Haydn Belfield, Gretchen Krueger, Gillian K. Hadfield, Heidy Khlaaf, Jingying Yang, Helen Toner, Ruth Fong, Tegan Maharaj, Pang Wei Koh, Sara Hooker, Jade Leung, Andrew Trask, Emma Bluemke, Jonathan Lebensold, Cullen O'Keefe, Mark Koren, Théo Ryffel, J. B. Rubinovitz, Tamay Besiroglu, Federica Carugati, Jack Clark, Peter Eckersley, Sarah de Haas, Maritza Johnson, Ben Laurie, Alex Ingerman, Igor Krawczuk, Amanda Askell, Rosario Cammarota, Andrew Lohn, David Krueger, Charlotte Stix, Peter Henderson, Logan Graham, Carina Prunkl, Bianca Martin, Elizabeth Seger, Noa Zilberman, Seán Ó hÉigeartaigh, Frens Kroeger, Girish Sastry, Rebecca Kagan, Adrian Weller, Brian Tse, Elizabeth Barnes, Allan Dafoe, Paul Scharre, Ariel Herbert-Voss, Martijn Rasser, Shagun Sodhani, Carrick Flynn, Thomas Krendl Gilbert, Lisa Dyer, Saif Khan, Yoshua Bengio, Markus Anderljung
Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims. CoRR (2020)
Théo Ryffel, David Pointcheval, Francis R. Bach
ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing. CoRR (2020)
Théo Ryffel, Edouard Dufour Sans, Romain Gay, Francis R. Bach, David Pointcheval
Partially Encrypted Machine Learning using Functional Encryption. CoRR (2019)
Théo Ryffel, David Pointcheval, Francis R. Bach, Edouard Dufour-Sans, Romain Gay
Partially Encrypted Deep Learning using Functional Encryption. NeurIPS (2019)
Théo Ryffel, Andrew Trask, Morten Dahl, Bobby Wagner, Jason Mancuso, Daniel Rueckert, Jonathan Passerat-Palmbach
A generic framework for privacy preserving deep learning. CoRR (2018)