Hossein Zakerinia
3 Publications
2024 | Published | Conference Paper | IST-REx-ID: 17093 | 
Zakerinia, Hossein, Shayan Talaei, Giorgi Nadiradze, and Dan-Adrian Alistarh. “Communication-Efficient Federated Learning with Data and Client Heterogeneity.” In Proceedings of the 27th International Conference on Artificial Intelligence and Statistics, 238:3448–56. ML Research Press, 2024.
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2024 | Published | Conference Paper | IST-REx-ID: 17411 |
Scott, Jonathan A, Hossein Zakerinia, and Christoph Lampert. “PEFLL: Personalized Federated Learning by Learning to Learn.” In 12th International Conference on Learning Representations. OpenReview, 2024.
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2024 | Published | Conference Paper | IST-REx-ID: 18118 |
Zakerinia, Hossein, Amin Behjati, and Christoph Lampert. “More Flexible PAC-Bayesian Meta-Learning by Learning Learning Algorithms.” In Proceedings of the 41st International Conference on Machine Learning, 235:58122–39. ML Research Press, 2024.
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3 Publications
2024 | Published | Conference Paper | IST-REx-ID: 17093 |
Zakerinia, Hossein, Shayan Talaei, Giorgi Nadiradze, and Dan-Adrian Alistarh. “Communication-Efficient Federated Learning with Data and Client Heterogeneity.” In Proceedings of the 27th International Conference on Artificial Intelligence and Statistics, 238:3448–56. ML Research Press, 2024.
[Preprint]
View
| Download Preprint (ext.)
| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 17411 |
Scott, Jonathan A, Hossein Zakerinia, and Christoph Lampert. “PEFLL: Personalized Federated Learning by Learning to Learn.” In 12th International Conference on Learning Representations. OpenReview, 2024.
[Published Version]
View
| Files available
| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18118 |
Zakerinia, Hossein, Amin Behjati, and Christoph Lampert. “More Flexible PAC-Bayesian Meta-Learning by Learning Learning Algorithms.” In Proceedings of the 41st International Conference on Machine Learning, 235:58122–39. ML Research Press, 2024.
[Published Version]
View
| Download Published Version (ext.)
| arXiv