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124 Publications


2024 | Epub ahead of print | Journal Article | IST-REx-ID: 12662 | OA
Súkeník, Peter, and Christoph Lampert. “Generalization in Multi-Objective Machine Learning.” Neural Computing and Applications, Springer Nature, 2024, doi:10.1007/s00521-024-10616-1.
[Published Version] View | DOI | Download Published Version (ext.) | arXiv
 

2024 | Published | Conference Paper | IST-REx-ID: 18875 | OA
Kalinin, Nikita, and Christoph Lampert. “Banded Square Root Matrix Factorization for Differentially Private Model Training.” 38th Annual Conference on Neural Information Processing Systems, vol. 38, Curran Associates, 2024.
[Published Version] View | Files available | arXiv
 

2024 | Submitted | Preprint | IST-REx-ID: 18874
Prach, Bernd, and Christoph Lampert. “Intriguing Properties of Robust Classification.” ArXiv, 2412.04245, doi:10.48550/arXiv.2412.04245.
[Preprint] View | DOI | arXiv
 

2024 | Published | Conference Paper | IST-REx-ID: 17426 | OA
Prach, Bernd, et al. “1-Lipschitz Layers Compared: Memory, Speed, and Certifiable Robustness.” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Computer Vision Foundation, 2024, pp. 24574–83, doi:10.1109/CVPR52733.2024.02320.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 

2024 | Published | Conference Paper | IST-REx-ID: 18891 | OA
Súkeník, Peter, et al. “Neural Collapse vs. Low-Rank Bias: Is Deep Neural Collapse Really Optimal?” 38th Annual Conference on Neural Information Processing Systems, vol. 38, Curran Associates, 2024.
[Published Version] View | Files available
 

2024 | Published | Preprint | IST-REx-ID: 19063 | OA
Zverev, Egor, et al. “Can LLMs Separate Instructions from Data? And What Do We Even Mean by That?” ArXiv, 2403.06833, 2024, doi:10.48550/arXiv.2403.06833.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 

2024 | Published | Conference Paper | IST-REx-ID: 17093 | OA
Zakerinia, Hossein, et al. “Communication-Efficient Federated Learning with Data and Client Heterogeneity.” Proceedings of the 27th International Conference on Artificial Intelligence and Statistics, vol. 238, ML Research Press, 2024, pp. 3448–56.
[Preprint] View | Download Preprint (ext.) | arXiv
 

2024 | Published | Conference Paper | IST-REx-ID: 17411 | OA
Scott, Jonathan A., et al. “PEFLL: Personalized Federated Learning by Learning to Learn.” 12th International Conference on Learning Representations, OpenReview, 2024.
[Published Version] View | Files available | arXiv
 

2024 | Published | Conference Paper | IST-REx-ID: 18118 | OA
Zakerinia, Hossein, et al. “More Flexible PAC-Bayesian Meta-Learning by Learning Learning Algorithms.” Proceedings of the 41st International Conference on Machine Learning, vol. 235, ML Research Press, 2024, pp. 58122–39.
[Published Version] View | Download Published Version (ext.) | arXiv
 

2024 | Published | Conference Paper | IST-REx-ID: 18120 | OA
Scott, Jonathan A., and Áine Cahill. “Improved Modelling of Federated Datasets Using Mixtures-of-Dirichlet-Multinomials.” Proceedings of the 41st International Conference on Machine Learning, vol. 235, ML Research Press, 2024, pp. 44012–37.
[Preprint] View | Download Preprint (ext.) | arXiv
 

2024 | Published | Journal Article | IST-REx-ID: 19408 | OA
Verwimp, Eli, et al. “Continual Learning: Applications and the Road Forward.” Transactions on Machine Learning Research, vol. 2024, Transactions on Machine Learning Research, 2024.
[Published Version] View | Files available | arXiv
 

2023 | Published | Conference Paper | IST-REx-ID: 12660 | OA
Scott, Jonathan A., et al. “Cross-Client Label Propagation for Transductive and Semi-Supervised Federated Learning.” Transactions in Machine Learning, Curran Associates, 2023.
[Preprint] View | Files available | arXiv
 

2023 | Published | Journal Article | IST-REx-ID: 14320 | OA
Henderson, Paul M., et al. “Deep Learning Extraction of Band Structure Parameters from Density of States: A Case Study on Trilayer Graphene.” Physical Review B, vol. 108, no. 12, 125411, American Physical Society, 2023, doi:10.1103/physrevb.108.125411.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 

2023 | Published | Conference Paper | IST-REx-ID: 14410
Tomaszewska, Paulina, and Christoph Lampert. “On the Implementation of Baselines and Lightweight Conditional Model Extrapolation (LIMES) under Class-Prior Shift.” International Workshop on Reproducible Research in Pattern Recognition, vol. 14068, Springer Nature, 2023, pp. 67–73, doi:10.1007/978-3-031-40773-4_6.
View | DOI
 

2023 | Published | Journal Article | IST-REx-ID: 14446 | OA
Jakubík, Jozef, et al. “Against the Flow of Time with Multi-Output Models.” Measurement Science Review, vol. 23, no. 4, Sciendo, 2023, pp. 175–83, doi:10.2478/msr-2023-0023.
[Published Version] View | Files available | DOI
 

2023 | Published | Conference Paper | IST-REx-ID: 13053 | OA
Krumes, Alexandra, et al. “CrAM: A Compression-Aware Minimizer.” 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
Krumes, Alexandra. Efficiency and Generalization of Sparse Neural Networks. Institute of Science and Technology Austria, 2023, doi:10.15479/at:ista:13074.
[Published Version] View | Files available | DOI
 

2023 | Published | Conference Paper | IST-REx-ID: 14771 | OA
Iofinova, Eugenia B., et al. “Bias in Pruned Vision Models: In-Depth Analysis and Countermeasures.” 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2023, pp. 24364–73, doi:10.1109/cvpr52729.2023.02334.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 

2023 | Published | Conference Paper | IST-REx-ID: 14921 | OA
Súkeník, Peter, et al. “Deep Neural Collapse Is Provably Optimal for the Deep Unconstrained Features Model.” 37th Annual Conference on Neural Information Processing Systems, 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 

2023 | Submitted | Preprint | IST-REx-ID: 15039 | OA
Prach, Bernd, and Christoph Lampert. “1-Lipschitz Neural Networks Are More Expressive with N-Activations.” ArXiv, 2311.06103, doi:10.48550/ARXIV.2311.06103.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 

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