Graph-based Clustering under Differential Privacy. - Rafael Pinot; Anne Morvan; Florian Yger; Cédric Gouy-Pailler; Jamal Atif (UAI, 2018)

Minimum spanning tree release under differential privacy constraints. - Rafael Pinot (CoRR, 2018)

Theoretical evidence for adversarial robustness through randomization. - Rafael Pinot; Laurent Meunier; Alexandre Araujo; Hisashi Kashima; Florian Yger; Cédric Gouy-Pailler; Jamal Atif (NeurIPS, 2019)

Theoretical evidence for adversarial robustness through randomization: the case of the Exponential family. - Rafael Pinot; Laurent Meunier; Alexandre Araujo; Hisashi Kashima; Florian Yger; Cédric Gouy-Pailler; Jamal Atif (CoRR, 2019)

Robust Neural Networks using Randomized Adversarial Training. - Alexandre Araujo; Rafael Pinot; Benjamin Négrevergne; Laurent Meunier; Yann Chevaleyre; Florian Yger; Jamal Atif (CoRR, 2019)

A unified view on differential privacy and robustness to adversarial examples. - Rafael Pinot; Florian Yger; Cédric Gouy-Pailler; Jamal Atif (CoRR, 2019)

Randomization matters How to defend against strong adversarial attacks. - Rafael Pinot; Raphael Ettedgui; Geovani Rizk; Yann Chevaleyre; Jamal Atif (ICML, 2020)

Advocating for Multiple Defense Strategies Against Adversarial Examples. - Alexandre Araujo; Laurent Meunier; Rafael Pinot; Benjamin Négrevergne (PKDD/ECML Workshops, 2020)

On the impact of randomization on robustness in machine learning. (Impact de la randomisation sur la robustesse des modèles d'apprentissage supervisé). - Rafael Pinot (2020)

Mixed Nash Equilibria in the Adversarial Examples Game. - Laurent Meunier; Meyer Scetbon; Rafael Pinot; Jamal Atif; Yann Chevaleyre (ICML, 2021)

Differential Privacy and Byzantine Resilience in SGD: Do They Add Up? - Rachid Guerraoui; Nirupam Gupta; Rafaël Pinot; Sébastien Rouault; John Stephan (PODC, 2021)

Combining Differential Privacy and Byzantine Resilience in Distributed SGD. - Rachid Guerraoui; Nirupam Gupta; Rafael Pinot; Sébastien Rouault; John Stephan (CoRR, 2021)

SPEED: secure, PrivatE, and efficient deep learning. - Arnaud Grivet Sébert; Rafael Pinot; Martin Zuber; Cédric Gouy-Pailler; Renaud Sirdey (Mach. Learn., 2021)

Byzantine Machine Learning Made Easy By Resilient Averaging of Momentums. - Sadegh Farhadkhani; Rachid Guerraoui; Nirupam Gupta; Rafael Pinot; John Stephan (ICML, 2022)

The Universal Gossip Fighter. - Anastasiia Gorbunova; Rachid Guerraoui; Anne-Marie Kermarrec; Anastasiia Kucherenko; Rafaël Pinot (IPDPS, 2022)

Towards Consistency in Adversarial Classification. - Laurent Meunier; Raphael Ettedgui; Rafael Pinot; Yann Chevaleyre; Jamal Atif (NeurIPS, 2022)

Democratizing Machine Learning: Resilient Distributed Learning with Heterogeneous Participants. - Karim Boubouh; Amine Boussetta; Nirupam Gupta; Alexandre Maurer; Rafaël Pinot (SRDS, 2022)

Towards Evading the Limits of Randomized Smoothing: A Theoretical Analysis. - Raphael Ettedgui; Alexandre Araujo; Rafael Pinot; Yann Chevaleyre; Jamal Atif (CoRR, 2022)

Making Byzantine Decentralized Learning Efficient. - Sadegh Farhadkhani; Rachid Guerraoui; Nirupam Gupta; Lê Nguyên Hoang; Rafael Pinot; John Stephan (CoRR, 2022)

On the Impossible Safety of Large AI Models. - El-Mahdi El-Mhamdi; Sadegh Farhadkhani; Rachid Guerraoui; Nirupam Gupta; Lê-Nguyên Hoang; Rafael Pinot; John Stephan (CoRR, 2022)

On the robustness of randomized classifiers to adversarial examples. - Rafael Pinot; Laurent Meunier; Florian Yger; Cédric Gouy-Pailler; Yann Chevaleyre; Jamal Atif (Mach. Learn., 2022)

Fixing by Mixing: A Recipe for Optimal Byzantine ML under Heterogeneity. - Youssef Allouah; Sadegh Farhadkhani; Rachid Guerraoui; Nirupam Gupta; Rafael Pinot; John Stephan (AISTATS, 2023)

On the Privacy-Robustness-Utility Trilemma in Distributed Learning. - Youssef Allouah; Rachid Guerraoui; Nirupam Gupta; Rafael Pinot; John Stephan (ICML, 2023)

Robust Collaborative Learning with Linear Gradient Overhead. - Sadegh Farhadkhani; Rachid Guerraoui; Nirupam Gupta; Lê-Nguyên Hoang; Rafael Pinot; John Stephan (ICML, 2023)

Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneity. - Youssef Allouah; Rachid Guerraoui; Nirupam Gupta; Rafael Pinot; Geovani Rizk (NeurIPS, 2023)

On the Inherent Anonymity of Gossiping. - Rachid Guerraoui; Anne-Marie Kermarrec; Anastasiia Kucherenko; Rafaël Pinot; Sasha Voitovych (DISC, 2023)

Distributed Learning with Curious and Adversarial Machines. - Youssef Allouah; Rachid Guerraoui; Nirupam Gupta; Rafael Pinot; John Stephan (CoRR, 2023)

Practical Homomorphic Aggregation for Byzantine ML. - Antoine Choffrut; Rachid Guerraoui; Rafael Pinot; Renaud Sirdey; John Stephan; Martin Zuber (CoRR, 2023)

Robust Machine Learning - Distributed Methods for Safe AI - Rachid Guerraoui; Nirupam Gupta; Rafael Pinot (Springer, 2024)

Byzantine-Robust Federated Learning: Impact of Client Subsampling and Local Updates. - Youssef Allouah; Sadegh Farhadkhani; Rachid Guerraoui; Nirupam Gupta; Rafael Pinot; Geovani Rizk; Sasha Voitovych (ICML, 2024)

Towards Practical Homomorphic Aggregation in Byzantine-Resilient Distributed Learning. - Antoine Choffrut; Rachid Guerraoui; Rafael Pinot; Renaud Sirdey; John Stephan; Martin Zuber (Middleware, 2024)

Revisiting Ensembling in One-Shot Federated Learning. - Youssef Allouah; Akash Balasaheb Dhasade; Rachid Guerraoui; Nirupam Gupta; Anne-Marie Kermarrec; Rafael Pinot; Rafael Pires 001; Rishi Sharma 001 (NeurIPS, 2024)

Fine-Tuning Personalization in Federated Learning to Mitigate Adversarial Clients. - Youssef Allouah; Abdellah El Mrini; Rachid Guerraoui; Nirupam Gupta; Rafael Pinot (NeurIPS, 2024)

Brief Announcement: A Case for Byzantine Machine Learning. - Sadegh Farhadkhani; Rachid Guerraoui; Nirupam Gupta; Rafael Pinot (PODC, 2024)

Peerswap: A Peer-Sampler with Randomness Guarantees. - Rachid Guerraoui; Anne-Marie Kermarrec; Anastasiia Kucherenko; Rafael Pinot; Martijn de Vos (SRDS, 2024)

Tackling Byzantine Clients in Federated Learning. - Youssef Allouah; Sadegh Farhadkhani; Rachid Guerraoui; Nirupam Gupta; Rafael Pinot; Geovani Rizk; Sasha Voitovych (CoRR, 2024)

On the Relevance of Byzantine Robust Optimization Against Data Poisoning. - Sadegh Farhadkhani; Rachid Guerraoui; Nirupam Gupta; Rafael Pinot (CoRR, 2024)

Overcoming the Challenges of Batch Normalization in Federated Learning. - Rachid Guerraoui; Rafael Pinot; Geovani Rizk; John Stephan; François Taïani (CoRR, 2024)

PeerSwap: A Peer-Sampler with Randomness Guarantees. - Rachid Guerraoui; Anne-Marie Kermarrec; Anastasiia Kucherenko; Rafael Pinot; Marijn de Vos (CoRR, 2024)

Byzantine Machine Learning: A Primer. - Rachid Guerraoui; Nirupam Gupta; Rafael Pinot (ACM Comput. Surv., 2024)

Unveiling the Role of Randomization in Multiclass Adversarial Classification: Insights from Graph Theory. - Lucas Gnecco Heredia; Matteo Sammut; Muni Sreenivas Pydi; Rafael Pinot; Benjamin Négrevergne; Yann Chevaleyre (AISTATS, 2025)

ByzFL: Research Framework for Robust Federated Learning. - Marc González 001; Rachid Guerraoui; Rafael Pinot; Geovani Rizk; John Stephan; François Taïani (CoRR, 2025)