Rafael Pinot

According to our database, Rafael Pinot authored at least 38 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of four.

2024

Byzantine Machine Learning: A Primer.

Rachid Guerraoui, Nirupam Gupta, Rafael Pinot

ACM Comput. Surv., July, 2024

Fine-Tuning Personalization in Federated Learning to Mitigate Adversarial Clients.

Youssef Allouah, Abdellah El Mrini, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot

CoRR, 2024

PeerSwap: A Peer-Sampler with Randomness Guarantees.

Rachid Guerraoui, Anne-Marie Kermarrec, Anastasiia Kucherenko, Rafael Pinot, Marijn de Vos

CoRR, 2024

Overcoming the Challenges of Batch Normalization in Federated Learning.

Rachid Guerraoui, Rafael Pinot, Geovani Rizk, John Stephan, François Taïani

CoRR, 2024

On the Relevance of Byzantine Robust Optimization Against Data Poisoning.

Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot

CoRR, 2024

Tackling Byzantine Clients in Federated Learning.

Youssef Allouah, Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, Geovani Rizk, Sasha Voitovych

CoRR, 2024

Brief Announcement: A Case for Byzantine Machine Learning.

Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot

Proceedings of the 43rd ACM Symposium on Principles of Distributed Computing, 2024

Towards Practical Homomorphic Aggregation in Byzantine-Resilient Distributed Learning.

Antoine Choffrut, Rachid Guerraoui, Rafael Pinot, Renaud Sirdey, John Stephan, Martin Zuber

Proceedings of the 25th International Middleware Conference, 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

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Robust Machine Learning - Distributed Methods for Safe AI

Rachid Guerraoui, Nirupam Gupta, Rafael Pinot

Springer, ISBN: 978-981-97-0687-7, 2024

2023

Practical Homomorphic Aggregation for Byzantine ML.

Antoine Choffrut, Rachid Guerraoui, Rafael Pinot, Renaud Sirdey, John Stephan, Martin Zuber

CoRR, 2023

Distributed Learning with Curious and Adversarial Machines.

Youssef Allouah, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, John Stephan

CoRR, 2023

On the Inherent Anonymity of Gossiping.

Rachid Guerraoui, Anne-Marie Kermarrec, Anastasiia Kucherenko, Rafaël Pinot, Sasha Voitovych

Proceedings of the 37th International Symposium on Distributed Computing, 2023

Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneity.

Youssef Allouah, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, Geovani Rizk

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Robust Collaborative Learning with Linear Gradient Overhead.

Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Lê-Nguyên Hoang, Rafael Pinot, John Stephan

Proceedings of the International Conference on Machine Learning, 2023

On the Privacy-Robustness-Utility Trilemma in Distributed Learning.

Youssef Allouah, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, John Stephan

Proceedings of the International Conference on Machine Learning, 2023

Fixing by Mixing: A Recipe for Optimal Byzantine ML under Heterogeneity.

Youssef Allouah, Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, John Stephan

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

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

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

Making Byzantine Decentralized Learning Efficient.

Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Lê Nguyên Hoang, Rafael Pinot, John Stephan

CoRR, 2022

Towards Evading the Limits of Randomized Smoothing: A Theoretical Analysis.

Raphael Ettedgui, Alexandre Araujo, Rafael Pinot, Yann Chevaleyre, Jamal Atif

CoRR, 2022

Democratizing Machine Learning: Resilient Distributed Learning with Heterogeneous Participants.

Karim Boubouh, Amine Boussetta, Nirupam Gupta, Alexandre Maurer, Rafaël Pinot

Proceedings of the 41st International Symposium on Reliable Distributed Systems, 2022

Towards Consistency in Adversarial Classification.

Laurent Meunier, Raphael Ettedgui, Rafael Pinot, Yann Chevaleyre, Jamal Atif

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

The Universal Gossip Fighter.

Anastasiia Gorbunova, Rachid Guerraoui, Anne-Marie Kermarrec, Anastasiia Kucherenko, Rafaël Pinot

Proceedings of the 2022 IEEE International Parallel and Distributed Processing Symposium, 2022

Byzantine Machine Learning Made Easy By Resilient Averaging of Momentums.

Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, John Stephan

Proceedings of the International Conference on Machine Learning, 2022

2021

SPEED: secure, PrivatE, and efficient deep learning.

Arnaud Grivet Sébert, Rafael Pinot, Martin Zuber, Cédric Gouy-Pailler, Renaud Sirdey

Mach. Learn., 2021

Combining Differential Privacy and Byzantine Resilience in Distributed SGD.

Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, Sébastien Rouault, John Stephan

CoRR, 2021

Differential Privacy and Byzantine Resilience in SGD: Do They Add Up?

Rachid Guerraoui, Nirupam Gupta, Rafaël Pinot, Sébastien Rouault, John Stephan

Proceedings of the PODC '21: ACM Symposium on Principles of Distributed Computing, 2021

Mixed Nash Equilibria in the Adversarial Examples Game.

Laurent Meunier, Meyer Scetbon, Rafael Pinot, Jamal Atif, Yann Chevaleyre

Proceedings of the 38th International Conference on Machine Learning, 2021

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

PhD thesis, 2020

Advocating for Multiple Defense Strategies Against Adversarial Examples.

Alexandre Araujo, Laurent Meunier, Rafael Pinot, Benjamin Négrevergne

Proceedings of the ECML PKDD 2020 Workshops, 2020

Randomization matters How to defend against strong adversarial attacks.

Rafael Pinot, Raphael Ettedgui, Geovani Rizk, Yann Chevaleyre, Jamal Atif

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

A unified view on differential privacy and robustness to adversarial examples.

Rafael Pinot, 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

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

Theoretical evidence for adversarial robustness through randomization.

Rafael Pinot, Laurent Meunier, Alexandre Araujo, Hisashi Kashima, Florian Yger, Cédric Gouy-Pailler, Jamal Atif

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Minimum spanning tree release under differential privacy constraints.

Rafael Pinot

CoRR, 2018

Graph-based Clustering under Differential Privacy.

Rafael Pinot, Anne Morvan, Florian Yger, Cédric Gouy-Pailler, Jamal Atif

Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018