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ORCIDRafael Pinot
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2020 – today
- 2024
[b2]Rachid Guerraoui, Nirupam Gupta, Rafael Pinot:
Robust Machine Learning - Distributed Methods for Safe AI. Springer 2024, ISBN 978-981-97-0687-7, pp. 1-154- [j3]Rachid Guerraoui
, Nirupam Gupta, Rafael Pinot:
Byzantine Machine Learning: A Primer. ACM Comput. Surv. 56(7): 169:1-169:39 (2024) - [c17]Youssef Allouah, Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, Geovani Rizk, Sasha Voitovych:
Byzantine-Robust Federated Learning: Impact of Client Subsampling and Local Updates. ICML 2024 - [c16]Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot:
Brief Announcement: A Case for Byzantine Machine Learning. PODC 2024: 131-134 - [i27]Youssef Allouah, Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, Geovani Rizk, Sasha Voitovych:
Tackling Byzantine Clients in Federated Learning. CoRR abs/2402.12780 (2024) - [i26]Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot:
On the Relevance of Byzantine Robust Optimization Against Data Poisoning. CoRR abs/2405.00491 (2024) - [i25]Rachid Guerraoui, Rafael Pinot, Geovani Rizk, John Stephan, François Taïani:
Overcoming the Challenges of Batch Normalization in Federated Learning. CoRR abs/2405.14670 (2024) - [i24]Rachid Guerraoui, Anne-Marie Kermarrec, Anastasiia Kucherenko, Rafael Pinot, Marijn de Vos:
PeerSwap: A Peer-Sampler with Randomness Guarantees. CoRR abs/2408.03829 (2024) - [i23]Youssef Allouah, Abdellah El Mrini, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot:
Fine-Tuning Personalization in Federated Learning to Mitigate Adversarial Clients. CoRR abs/2409.20329 (2024) - 2023
- [c15]Youssef Allouah, Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, John Stephan:
Fixing by Mixing: A Recipe for Optimal Byzantine ML under Heterogeneity. AISTATS 2023: 1232-1300 - [c14]Youssef Allouah, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, John Stephan:
On the Privacy-Robustness-Utility Trilemma in Distributed Learning. ICML 2023: 569-626 - [c13]Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Lê-Nguyên Hoang, Rafael Pinot, John Stephan:
Robust Collaborative Learning with Linear Gradient Overhead. ICML 2023: 9761-9813 - [c12]Youssef Allouah, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, Geovani Rizk:
Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneity. NeurIPS 2023 - [c11]Rachid Guerraoui, Anne-Marie Kermarrec, Anastasiia Kucherenko, Rafaël Pinot, Sasha Voitovych:
On the Inherent Anonymity of Gossiping. DISC 2023: 24:1-24:19 - [i22]Youssef Allouah, Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, John Stephan:
Fixing by Mixing: A Recipe for Optimal Byzantine ML under Heterogeneity. CoRR abs/2302.01772 (2023) - [i21]Youssef Allouah, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, John Stephan:
Distributed Learning with Curious and Adversarial Machines. CoRR abs/2302.04787 (2023) - [i20]Rachid Guerraoui, Anne-Marie Kermarrec, Anastasiia Kucherenko, Rafael Pinot, Sasha Voitovych:
On the Inherent Anonymity of Gossiping. CoRR abs/2308.02477 (2023) - [i19]Antoine Choffrut, Rachid Guerraoui, Rafael Pinot, Renaud Sirdey, John Stephan, Martin Zuber:
Practical Homomorphic Aggregation for Byzantine ML. CoRR abs/2309.05395 (2023) - [i18]Youssef Allouah, Rachid Guerraoui, Nirupam Gupta, Rafaël Pinot, Geovani Rizk:
Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneity. CoRR abs/2309.13591 (2023) - 2022
- [j2]Rafael Pinot, Laurent Meunier, Florian Yger, Cédric Gouy-Pailler, Yann Chevaleyre, Jamal Atif:
On the robustness of randomized classifiers to adversarial examples. Mach. Learn. 111(9): 3425-3457 (2022) - [c10]Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, John Stephan:
Byzantine Machine Learning Made Easy By Resilient Averaging of Momentums. ICML 2022: 6246-6283 - [c9]Anastasiia Gorbunova, Rachid Guerraoui, Anne-Marie Kermarrec, Anastasiia Kucherenko, Rafaël Pinot:
The Universal Gossip Fighter. IPDPS 2022: 1162-1172 - [c8]Laurent Meunier, Raphael Ettedgui, Rafael Pinot, Yann Chevaleyre, Jamal Atif:
Towards Consistency in Adversarial Classification. NeurIPS 2022 - [c7]Karim Boubouh, Amine Boussetta, Nirupam Gupta, Alexandre Maurer, Rafaël Pinot:
Democratizing Machine Learning: Resilient Distributed Learning with Heterogeneous Participants. SRDS 2022: 94-120 - [i17]Laurent Meunier, Raphaël Ettedgui, Rafael Pinot, Yann Chevaleyre, Jamal Atif:
Towards Consistency in Adversarial Classification. CoRR abs/2205.10022 (2022) - [i16]Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, John Stephan:
Byzantine Machine Learning Made Easy by Resilient Averaging of Momentums. CoRR abs/2205.12173 (2022) - [i15]Raphael Ettedgui, Alexandre Araujo, Rafael Pinot, Yann Chevaleyre, Jamal Atif:
Towards Evading the Limits of Randomized Smoothing: A Theoretical Analysis. CoRR abs/2206.01715 (2022) - [i14]Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Lê Nguyên Hoang, Rafael Pinot, John Stephan:
Making Byzantine Decentralized Learning Efficient. CoRR abs/2209.10931 (2022) - [i13]El-Mahdi El-Mhamdi, Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Lê-Nguyên Hoang, Rafael Pinot, John Stephan:
On the Impossible Safety of Large AI Models. CoRR abs/2209.15259 (2022) - 2021
- [j1]Arnaud Grivet Sébert, Rafael Pinot, Martin Zuber, Cédric Gouy-Pailler, Renaud Sirdey:
SPEED: secure, PrivatE, and efficient deep learning. Mach. Learn. 110(4): 675-694 (2021) - [c6]Laurent Meunier, Meyer Scetbon, Rafael Pinot, Jamal Atif, Yann Chevaleyre:
Mixed Nash Equilibria in the Adversarial Examples Game. ICML 2021: 7677-7687 - [c5]Rachid Guerraoui, Nirupam Gupta, Rafaël Pinot, Sébastien Rouault, John Stephan:
Differential Privacy and Byzantine Resilience in SGD: Do They Add Up? PODC 2021: 391-401 - [i12]Laurent Meunier, Meyer Scetbon, Rafael Pinot, Jamal Atif, Yann Chevaleyre:
Mixed Nash Equilibria in the Adversarial Examples Game. CoRR abs/2102.06905 (2021) - [i11]Rachid Guerraoui, Nirupam Gupta, Rafaël Pinot, Sébastien Rouault, John Stephan:
Differential Privacy and Byzantine Resilience in SGD: Do They Add Up? CoRR abs/2102.08166 (2021) - [i10]Rafael Pinot, Laurent Meunier, Florian Yger, Cédric Gouy-Pailler, Yann Chevaleyre, Jamal Atif:
On the robustness of randomized classifiers to adversarial examples. CoRR abs/2102.10875 (2021) - [i9]Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, Sébastien Rouault, John Stephan:
Combining Differential Privacy and Byzantine Resilience in Distributed SGD. CoRR abs/2110.03991 (2021) - 2020
- [b1]Rafael Pinot:
On the impact of randomization on robustness in machine learning. (Impact de la randomisation sur la robustesse des modèles d'apprentissage supervisé). PSL Research University, Paris, France, 2020 - [c4]Rafael Pinot, Raphael Ettedgui, Geovani Rizk, Yann Chevaleyre, Jamal Atif:
Randomization matters How to defend against strong adversarial attacks. ICML 2020: 7717-7727 - [c3]Alexandre Araujo, Laurent Meunier, Rafael Pinot, Benjamin Négrevergne:
Advocating for Multiple Defense Strategies Against Adversarial Examples. PKDD/ECML Workshops 2020: 165-177 - [i8]Rafael Pinot, Raphael Ettedgui, Geovani Rizk, Yann Chevaleyre, Jamal Atif:
Randomization matters. How to defend against strong adversarial attacks. CoRR abs/2002.11565 (2020) - [i7]Arnaud Grivet Sébert, Rafael Pinot, Martin Zuber, Cédric Gouy-Pailler, Renaud Sirdey:
SPEED: Secure, PrivatE, and Efficient Deep learning. CoRR abs/2006.09475 (2020) - [i6]Alexandre Araujo, Laurent Meunier, Rafael Pinot, Benjamin Négrevergne:
Advocating for Multiple Defense Strategies against Adversarial Examples. CoRR abs/2012.02632 (2020)
2010 – 2019
- 2019
- [c2]Rafael Pinot, Laurent Meunier, Alexandre Araujo, Hisashi Kashima, Florian Yger, Cédric Gouy-Pailler, Jamal Atif:
Theoretical evidence for adversarial robustness through randomization. NeurIPS 2019: 11838-11848 - [i5]Rafael Pinot, Laurent Meunier, Alexandre Araujo, Hisashi Kashima, Florian Yger, Cédric Gouy-Pailler, Jamal Atif:
Theoretical evidence for adversarial robustness through randomization: the case of the Exponential family. CoRR abs/1902.01148 (2019) - [i4]Alexandre Araujo, Rafael Pinot, Benjamin Négrevergne, Laurent Meunier, Yann Chevaleyre, Florian Yger, Jamal Atif:
Robust Neural Networks using Randomized Adversarial Training. CoRR abs/1903.10219 (2019) - [i3]Rafael Pinot, Florian Yger, Cédric Gouy-Pailler, Jamal Atif:
A unified view on differential privacy and robustness to adversarial examples. CoRR abs/1906.07982 (2019) - 2018
- [c1]Rafael Pinot, Anne Morvan, Florian Yger, Cédric Gouy-Pailler, Jamal Atif:
Graph-based Clustering under Differential Privacy. UAI 2018: 329-338 - [i2]Rafael Pinot:
Minimum spanning tree release under differential privacy constraints. CoRR abs/1801.06423 (2018) - [i1]Rafael Pinot, Anne Morvan, Florian Yger, Cédric Gouy-Pailler, Jamal Atif:
Graph-based Clustering under Differential Privacy. CoRR abs/1803.03831 (2018)
Coauthor Index
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