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Christoph Lampert

Nikita Kalinin

Bernd Prach

Egor Zverev

Jonathan Scott

Max Cairney-Leeming

Peter Sukenik

Edwige Cyffers

Hossein Zakerinia

Paul Henderson

Filip Korc

Nikola Konstantinov

Daniel Pucher

Georg Martius

Johannes Dostal

Alexander Kolesnikov

Volhejn, Vaclav

Viktoriia Sharmanska

Anastasia Pentina

Emilie Morvant

Alexandra Krumes

Alexander Zimin

Mary Phuong

Amélie Royer

Csaba Domokos



124 Publications

2024 | Published | Conference Paper | IST-REx-ID: 17093 | OA
Communication-efficient federated learning with data and client heterogeneity
H. Zakerinia, S. Talaei, G. Nadiradze, D.-A. Alistarh, in:, Proceedings of the 27th International Conference on Artificial Intelligence and Statistics, ML Research Press, 2024, pp. 3448–3456.
[Preprint] View | Download Preprint (ext.) | arXiv
 
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
 
2024 | Published | Conference Paper | IST-REx-ID: 18118 | OA
More flexible PAC-Bayesian meta-learning by learning learning algorithms
H. Zakerinia, A. Behjati, C. Lampert, in:, Proceedings of the 41st International Conference on Machine Learning, ML Research Press, 2024, pp. 58122–58139.
[Published Version] View | Download Published Version (ext.) | arXiv
 
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 | Download Preprint (ext.) | arXiv
 
2024 | Epub ahead of print | Journal Article | IST-REx-ID: 12662 | OA
Generalization in multi-objective machine learning
P. Súkeník, C. Lampert, Neural Computing and Applications (2024).
[Published Version] View | DOI | Download Published Version (ext.) | arXiv
 
2024 | Published | Conference Paper | IST-REx-ID: 17426 | OA
1-Lipschitz layers compared: Memory, speed, and certifiable robustness
B. Prach, F. Brau, G. Buttazzo, C. Lampert, in:, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Computer Vision Foundation, 2024, pp. 24574–24583.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
2024 | Published | Preprint | IST-REx-ID: 19063 | OA
Can LLMs separate instructions from data? And what do we even mean by that?
E. Zverev, S. Abdelnabi, S. Tabesh, M. Fritz, C. Lampert, ArXiv (2024).
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
2024 | Published | Journal Article | IST-REx-ID: 19408 | OA
Continual learning: Applications and the road forward
E. Verwimp, R. Aljundi, S. Ben-David, M. Bethge, A. Cossu, A. Gepperth, T.L. Hayes, E. Hüllermeier, C. Kanan, D. Kudithipudi, C. Lampert, M. Mundt, R. Pascanu, A. Popescu, A.S. Tolias, J. Van De Weijer, B. Liu, V. Lomonaco, T. Tuytelaars, G.M. Van De Ven, Transactions on Machine Learning Research 2024 (2024).
[Published Version] View | Files available | arXiv
 
2024 | Published | Conference Paper | IST-REx-ID: 18875 | OA
Banded square root matrix factorization for differentially private model training
Kalinin, Nikita, Banded square root matrix factorization for differentially private model training. 38th Annual Conference on Neural Information Processing Systems 37. 2024
[Published Version] View | Files available | arXiv
 
2024 | Published | Conference Paper | IST-REx-ID: 18891 | OA
Neural collapse versus low-rank bias: Is deep neural collapse really optimal?
Súkeník, Peter, Neural collapse versus low-rank bias: Is deep neural collapse really optimal?. 38th Annual Conference on Neural Information Processing Systems 37. 2024
[Published Version] View | Files available
 
2024 | Submitted | Preprint | IST-REx-ID: 18874 | OA
Intriguing properties of robust classification
Prach, Bernd, Intriguing properties of robust classification. arXiv. 2024
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
2023 | Published | Journal Article | IST-REx-ID: 14320 | OA
Deep learning extraction of band structure parameters from density of states: A case study on trilayer graphene
P.M. Henderson, A. Ghazaryan, A.A. Zibrov, A.F. Young, M. Serbyn, Physical Review B 108 (2023).
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
2023 | Published | Conference Paper | IST-REx-ID: 14410
On the implementation of baselines and lightweight conditional model extrapolation (LIMES) under class-prior shift
P. Tomaszewska, C. Lampert, in:, International Workshop on Reproducible Research in Pattern Recognition, Springer Nature, 2023, pp. 67–73.
View | DOI
 
2023 | Published | Journal Article | IST-REx-ID: 14446 | OA
Against the flow of time with multi-output models
J. Jakubík, M. Phuong, M. Chvosteková, A. Krakovská, Measurement Science Review 23 (2023) 175–183.
[Published Version] View | Files available | DOI
 
2023 | Submitted | Preprint | IST-REx-ID: 15039 | OA
1-Lipschitz neural networks are more expressive with N-activations
B. Prach, C. Lampert, ArXiv (n.d.).
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
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
 
2023 | Published | Conference Paper | IST-REx-ID: 14771 | OA
Bias in pruned vision models: In-depth analysis and countermeasures
E.B. Iofinova, A. Krumes, D.-A. Alistarh, in:, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2023, pp. 24364–24373.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
2023 | Published | Conference Paper | IST-REx-ID: 13053 | OA
CrAM: A Compression-Aware Minimizer
A. Krumes, A. Vladu, E. Kurtic, C. Lampert, D.-A. Alistarh, in:, 11th International Conference on Learning Representations , OpenReview, 2023.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
2023 | Published | Conference Paper | IST-REx-ID: 14921 | OA
Deep neural collapse is provably optimal for the deep unconstrained features model
P. Súkeník, M. Mondelli, C. Lampert, in:, 37th Annual Conference on Neural Information Processing Systems, 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2023 | Published | Thesis | IST-REx-ID: 13074 | OA
Efficiency and generalization of sparse neural networks
A. Krumes, Efficiency and Generalization of Sparse Neural Networks, Institute of Science and Technology Austria, 2023.
[Published Version] View | Files available | DOI
 

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