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Pinot Rafael
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Cited by
Year
Theoretical evidence for adversarial robustness through randomization
R Pinot, L Meunier, A Araujo, H Kashima, F Yger, C Gouy-Pailler, J Atif
Advances in Neural Information Processing Systems 32, 2019
942019
Randomization matters. how to defend against strong adversarial attacks
R Pinot, R Ettedgui, G Rizk, Y Chevaleyre, J Atif
International Conference on Machine Learning (ICML), 2020
562020
Advocating for multiple defense strategies against adversarial examples
A Araujo, L Meunier, R Pinot, B Negrevergne
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2020
42*2020
Byzantine machine learning made easy by resilient averaging of momentums
S Farhadkhani, R Guerraoui, N Gupta, R Pinot, J Stephan
International Conference on Machine Learning, 6246-6283, 2022
332022
Mixed Nash Equilibria in the Adversarial Examples Game
L Meunier, M Scetbon, R Pinot, J Atif, Y Chevaleyre
International Conference on Machine Learning (ICML), 2021
302021
Differential Privacy and Byzantine Resilience in SGD: Do They Add Up?
R Guerraoui, N Gupta, R Pinot, S Rouault, J Stephan
ACM Symposium on Principles of Distributed Computing (PODC), 2021
242021
Fixing by mixing: A recipe for optimal byzantine ml under heterogeneity
Y Allouah, S Farhadkhani, R Guerraoui, N Gupta, R Pinot, J Stephan
International Conference on Artificial Intelligence and Statistics, 1232-1300, 2023
222023
SPEED: secure, PrivatE, and efficient deep learning
A Grivet Sébert, R Pinot, M Zuber, C Gouy-Pailler, R Sirdey
Machine Learning 110 (4), 675-694, 2021
222021
A unified view on differential privacy and robustness to adversarial examples
R Pinot, F Yger, C Gouy-Pailler, J Atif
Workshop on Machine Learning for CyberSecurity (MLCS@ECML-PKDD), 2019
202019
Graph-based Clustering under Differential Privacy
R Pinot, A Morvan, F Yger, C Gouy-Pailler, J Atif
Conference on Uncertainty in Artificial Intelligence (UAI), 2018
192018
On the robustness of randomized classifiers to adversarial examples
R Pinot, L Meunier, F Yger, C Gouy-Pailler, Y Chevaleyre, J Atif
Machine Learning 111 (9), 3425-3457, 2022
162022
On the Impossible Safety of Large AI Models
EM El-Mhamdi, S Farhadkhani, R Guerraoui, N Gupta, LN Hoang, R Pinot, ...
arXiv preprint arXiv:2209.15259, 2022
12*2022
On the privacy-robustness-utility trilemma in distributed learning
Y Allouah, R Guerraoui, N Gupta, R Pinot, J Stephan
International Conference on Machine Learning, 569-626, 2023
112023
Minimum spanning tree release under differential privacy constraints
R Pinot
Sorbonne University, 2018
112018
Byzantine machine learning: A primer
R Guerraoui, N Gupta, R Pinot
ACM Computing Surveys, 2023
52023
Robust collaborative learning with linear gradient overhead
S Farhadkhani, R Guerraoui, N Gupta, LN Hoang, R Pinot, J Stephan
International Conference on Machine Learning, 9761-9813, 2023
5*2023
Towards consistency in adversarial classification
L Meunier, R Ettedgui, R Pinot, Y Chevaleyre, J Atif
Advances in Neural Information Processing Systems 35, 8538-8549, 2022
32022
Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneity
Y Allouah, R Guerraoui, N Gupta, R Pinot, G Rizk
Advances in Neural Information Processing Systems 36, 2024
12024
Tackling Byzantine Clients in Federated Learning
Y Allouah, S Farhadkhani, R GuerraouI, N Gupta, R Pinot, G Rizk, ...
arXiv preprint arXiv:2402.12780, 2024
2024
On the Effectiveness of One-Shot Federated Ensembles in Heterogeneous Cross-Silo Settings
Y Allouah, A Dhasade, R Guerraoui, N Gupta, A Kermarrec, R Pinot, ...
2023
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Articles 1–20