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Alexandra Volkova

Christoph Lampert

Nikita Kalinin

Peter Sukenik

Hossein Zakerinia

Egor Zverev

Fabian Kresse

Max Cairney-Leeming

Bernd Prach

Alexandra Krumes

Filip Korc

Mary Phuong

Edwige Cyffers

Amélie Royer

Paul Henderson

Nikola Konstantinov

Alexander Zimin

Alexander Kolesnikov

Volhejn, Vaclav

Johannes Dostal

Jonathan Scott

Csaba Domokos

Georg Martius

Anastasia Pentina

Viktoriia Sharmanska

Emilie Morvant

Daniel Pucher



135 Publications

2026 | Published | Thesis | PhD | IST-REx-ID: 21198 | OA
Scott, J. A. (2026). Data heterogeneity and personalization in federated learning. Institute of Science and Technology Austria. https://doi.org/10.15479/AT-ISTA-21198
[Published Version] View | Files available | DOI
 
2025 | Published | Journal Article | IST-REx-ID: 12662 | OA | PlanS
Súkeník, P., & Lampert, C. (2025). Generalization in multi-objective machine learning. Neural Computing and Applications. Springer Nature. https://doi.org/10.1007/s00521-024-10616-1
[Published Version] View | Files available | DOI | arXiv
 
2025 | Published | Conference Paper | IST-REx-ID: 20256 | OA
Henzinger, T. A., Kresse, F., Mallik, K., Yu, E., & Zikelic, D. (2025). Predictive monitoring of black-box dynamical systems. In 7th Annual Learning for Dynamics & Control Conference (Vol. 283, pp. 804–816). Ann Arbor, MI, United States: ML Research Press.
[Published Version] View | Files available | arXiv
 
2025 | Published | Conference Paper | IST-REx-ID: 20296 | OA
Kresse, F., Yu, E., Lampert, C., & Henzinger, T. A. (2025). Logic gate neural networks are good for verification. In 2nd International Conferenceon Neuro-Symbolic Systems (Vol. 288). Philadephia, PA, United States: ML Research Press.
[Published Version] View | Files available | arXiv
 
2025 | Published | Conference Paper | IST-REx-ID: 20298 | OA
Kalinin, N., & Steinberger, L. (2025). Efficient estimation of a Gaussian mean with local differential privacy. In Proceedings of the 28th International Conference on Artificial Intelligence and Statistics (Vol. 258, pp. 118–126). Mai Khao, Thailand: ML Research Press.
[Published Version] View | Files available | arXiv
 
2025 | Published | Conference Paper | IST-REx-ID: 20455 | OA
Prach, B., & Lampert, C. (2025). Intriguing properties of robust classification. In 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (pp. 660–669). Nashville, TN, United States: IEEE. https://doi.org/10.1109/CVPRW67362.2025.00071
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
2025 | Published | Conference Paper | IST-REx-ID: 20819 | OA
Scott, J. A., Lampert, C., & Saulpic, D. (2025). Differentially private federated k-means clustering with server-side data. In 42nd International Conference on Machine Learning (Vol. 267, pp. 53757–53790). Vancouver, Canada: ML Research Press.
[Published Version] View | Files available | arXiv
 
2025 | Published | Thesis | PhD | IST-REx-ID: 19759 | OA
Prach, B. (2025). Robust image classification with 1-Lipschitz networks. Institute of Science and Technology Austria. https://doi.org/10.15479/10.15479/at-ista-19759
[Published Version] View | Files available | DOI
 
2025 | Draft | Preprint | IST-REx-ID: 21207 | OA
Zakerinia, H., Scott, J. A., & Lampert, C. (n.d.). Federated learning with unlabeled clients: Personalization can happen in low dimensions. arXiv. https://doi.org/10.48550/ARXIV.2505.15579
[Preprint] View | Files available | DOI | Download Preprint (ext.)
 
2024 | Published | Conference Paper | IST-REx-ID: 18118 | OA
Zakerinia, H., Behjati, A., & Lampert, C. (2024). More flexible PAC-Bayesian meta-learning by learning learning algorithms. In Proceedings of the 41st International Conference on Machine Learning (Vol. 235, pp. 58122–58139). Vienna, Austria: ML Research Press.
[Published Version] View | Download Published Version (ext.) | arXiv
 
2024 | Published | Conference Paper | IST-REx-ID: 18120 | OA
Scott, J. A., & Cahill, Á. (2024). Improved modelling of federated datasets using mixtures-of-Dirichlet-multinomials. In Proceedings of the 41st International Conference on Machine Learning (Vol. 235, pp. 44012–44037). Vienna, Austria: ML Research Press.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
2024 | Published | Journal Article | IST-REx-ID: 18856 | OA
Lutsai, K., & Lampert, C. (2024). Predicting the geolocation of tweets using transformer models on customized data. Journal of Spatial Information Science. University of Maine. https://doi.org/10.5311/JOSIS.2024.29.295
[Published Version] View | Files available | DOI
 
2024 | Draft | Preprint | IST-REx-ID: 18874 | OA
Prach, B., & Lampert, C. (n.d.). Intriguing properties of robust classification. arXiv. https://doi.org/10.48550/arXiv.2412.04245
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
2024 | Published | Conference Paper | IST-REx-ID: 18875 | OA
Kalinin, N., & Lampert, C. (2024). Banded square root matrix factorization for differentially private model training. In 38th Annual Conference on Neural Information Processing Systems (Vol. 37). Vancouver, Canada: Neural Information Processing Systems Foundation.
[Published Version] View | Files available | arXiv
 
2024 | Published | Conference Paper | IST-REx-ID: 18891 | OA
Súkeník, P., Lampert, C., & Mondelli, M. (2024). Neural collapse versus low-rank bias: Is deep neural collapse really optimal? In 38th Annual Conference on Neural Information Processing Systems (Vol. 37). Vancouver, Canada: Neural Information Processing Systems Foundation.
[Published Version] View | Files available | arXiv
 
2024 | Published | Preprint | IST-REx-ID: 19063 | OA
Zverev, E., Abdelnabi, S., Tabesh, S., Fritz, M., & Lampert, C. (2024). Can LLMs separate instructions from data? And what do we even mean by that? arXiv. https://doi.org/10.48550/arXiv.2403.06833
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
2024 | Published | Journal Article | IST-REx-ID: 19408 | OA
Verwimp, E., Aljundi, R., Ben-David, S., Bethge, M., Cossu, A., Gepperth, A., … Van De Ven, G. M. (2024). Continual learning: Applications and the road forward. Transactions on Machine Learning Research. Transactions on Machine Learning Research.
[Published Version] View | Files available | arXiv
 
2024 | Published | Conference Paper | IST-REx-ID: 17093 | OA
Zakerinia, H., Talaei, S., Nadiradze, G., & Alistarh, D.-A. (2024). Communication-efficient federated learning with data and client heterogeneity. In Proceedings of the 27th International Conference on Artificial Intelligence and Statistics (Vol. 238, pp. 3448–3456). Valencia, Spain: ML Research Press.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2024 | Published | Conference Paper | IST-REx-ID: 17411 | OA
Scott, J. A., Zakerinia, H., & Lampert, C. (2024). PEFLL: Personalized federated learning by learning to learn. In 12th International Conference on Learning Representations. Vienna, Austria: OpenReview.
[Published Version] View | Files available | arXiv
 
2024 | Published | Conference Paper | IST-REx-ID: 17426 | OA
Prach, B., Brau, F., Buttazzo, G., & Lampert, C. (2024). 1-Lipschitz layers compared: Memory, speed, and certifiable robustness. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 24574–24583). Seattle, WA, United States: Computer Vision Foundation. https://doi.org/10.1109/CVPR52733.2024.02320
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 

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