Youssef Allouah

Orcid: 0000-0003-1048-7548

According to our database1, Youssef Allouah authored at least 18 papers between 2021 and 2025.

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Bibliography

2025
Distributional Unlearning: Forgetting Distributions, Not Just Samples.
CoRR, July, 2025

Certified Unlearning for Neural Networks.
CoRR, June, 2025

Towards Trustworthy Federated Learning with Untrusted Participants.
CoRR, May, 2025

The Utility and Complexity of In- and Out-of-Distribution Machine Unlearning.
Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Adaptive Gradient Clipping for Robust Federated Learning.
Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024
Boosting Robustness by Clipping Gradients in Distributed Learning.
CoRR, 2024

Tackling Byzantine Clients in Federated Learning.
CoRR, 2024

Fine-Tuning Personalization in Federated Learning to Mitigate Adversarial Clients.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Revisiting Ensembling in One-Shot Federated Learning.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

The Privacy Power of Correlated Noise in Decentralized Learning.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Byzantine-Robust Federated Learning: Impact of Client Subsampling and Local Updates.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Robust Sparse Voting.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023
Can Machines Learn Robustly, Privately, and Efficiently?
CoRR, 2023

Distributed Learning with Curious and Adversarial Machines.
CoRR, 2023

Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneity.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

On the Privacy-Robustness-Utility Trilemma in Distributed Learning.
Proceedings of the International Conference on Machine Learning, 2023

Fixing by Mixing: A Recipe for Optimal Byzantine ML under Heterogeneity.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2021
Further results on latent discourse models and word embeddings.
J. Mach. Learn. Res., 2021


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