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124 Publications


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: 18875 | OA
Banded square root matrix factorization for differentially private model training
N. Kalinin, C. Lampert, in:, 38th Annual Conference on Neural Information Processing Systems, Curran Associates, 2024.
[Published Version] View | Files available | arXiv
 

2024 | Submitted | Preprint | IST-REx-ID: 18874
Intriguing properties of robust classification
B. Prach, C. Lampert, ArXiv (n.d.).
[Preprint] View | DOI | 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 | Conference Paper | IST-REx-ID: 18891 | OA
Neural collapse vs. low-rank bias: Is deep neural collapse really optimal?
P. Súkeník, C. Lampert, M. Mondelli, in:, 38th Annual Conference on Neural Information Processing Systems, Curran Associates, 2024.
[Published Version] View | Files available
 

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 | 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 | 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
 

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 | 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 | 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 | 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
 

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: 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 | 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
 

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