Jonathan A Scott

6 Publications

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[6]
2026 | Published | Thesis | PhD | IST-REx-ID: 21198 | OA
Data heterogeneity and personalization in federated learning
J.A. Scott, Data Heterogeneity and Personalization in Federated Learning, Institute of Science and Technology Austria, 2026.
[Published Version] View | Files available | DOI
 
[5]
2025 | Published | Conference Paper | IST-REx-ID: 20819 | OA
Differentially private federated k-means clustering with server-side data
J.A. Scott, C. Lampert, D. Saulpic, in:, 42nd International Conference on Machine Learning, ML Research Press, 2025, pp. 53757–53790.
[Published Version] View | Files available | arXiv
 
[4]
2025 | Draft | Preprint | IST-REx-ID: 21207 | OA
Federated learning with unlabeled clients: Personalization can happen in low dimensions
H. Zakerinia, J.A. Scott, C. Lampert, ArXiv (n.d.).
[Preprint] View | Files available | DOI | Download Preprint (ext.)
 
[3]
2024 | Published | Conference Paper | IST-REx-ID: 18120 | OA
Improved modelling of federated datasets using mixtures-of-Dirichlet-multinomials
J.A. Scott, Á. Cahill, in:, Proceedings of the 41st International Conference on Machine Learning, ML Research Press, 2024, pp. 44012–44037.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[2]
2024 | Published | Conference Paper | IST-REx-ID: 17411 | OA
PEFLL: Personalized federated learning by learning to learn
J.A. Scott, H. Zakerinia, C. Lampert, in:, 12th International Conference on Learning Representations, OpenReview, 2024.
[Published Version] View | Files available | arXiv
 
[1]
2023 | Published | Conference Paper | IST-REx-ID: 12660 | OA
Cross-client label propagation for transductive and semi-supervised federated learning
J.A. Scott, M.X. Yeo, C. Lampert, in:, Transactions in Machine Learning, Curran Associates, 2023.
[Preprint] View | Files available | arXiv
 
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6 Publications

Mark all

[6]
2026 | Published | Thesis | PhD | IST-REx-ID: 21198 | OA
Data heterogeneity and personalization in federated learning
J.A. Scott, Data Heterogeneity and Personalization in Federated Learning, Institute of Science and Technology Austria, 2026.
[Published Version] View | Files available | DOI
 
[5]
2025 | Published | Conference Paper | IST-REx-ID: 20819 | OA
Differentially private federated k-means clustering with server-side data
J.A. Scott, C. Lampert, D. Saulpic, in:, 42nd International Conference on Machine Learning, ML Research Press, 2025, pp. 53757–53790.
[Published Version] View | Files available | arXiv
 
[4]
2025 | Draft | Preprint | IST-REx-ID: 21207 | OA
Federated learning with unlabeled clients: Personalization can happen in low dimensions
H. Zakerinia, J.A. Scott, C. Lampert, ArXiv (n.d.).
[Preprint] View | Files available | DOI | Download Preprint (ext.)
 
[3]
2024 | Published | Conference Paper | IST-REx-ID: 18120 | OA
Improved modelling of federated datasets using mixtures-of-Dirichlet-multinomials
J.A. Scott, Á. Cahill, in:, Proceedings of the 41st International Conference on Machine Learning, ML Research Press, 2024, pp. 44012–44037.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[2]
2024 | Published | Conference Paper | IST-REx-ID: 17411 | OA
PEFLL: Personalized federated learning by learning to learn
J.A. Scott, H. Zakerinia, C. Lampert, in:, 12th International Conference on Learning Representations, OpenReview, 2024.
[Published Version] View | Files available | arXiv
 
[1]
2023 | Published | Conference Paper | IST-REx-ID: 12660 | OA
Cross-client label propagation for transductive and semi-supervised federated learning
J.A. Scott, M.X. Yeo, C. Lampert, in:, Transactions in Machine Learning, Curran Associates, 2023.
[Preprint] View | Files available | arXiv
 
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