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117 Publications


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
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). Computer Vision Foundation.
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2023 |Published| Journal Article | IST-REx-ID: 14320 | OA
Henderson, P. M., Ghazaryan, A., Zibrov, A. A., Young, A. F., & Serbyn, M. (2023). Deep learning extraction of band structure parameters from density of states: A case study on trilayer graphene. Physical Review B. American Physical Society. https://doi.org/10.1103/physrevb.108.125411
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 

2023 |Published| Conference Paper | IST-REx-ID: 14410
Tomaszewska, P., & Lampert, C. (2023). On the implementation of baselines and lightweight conditional model extrapolation (LIMES) under class-prior shift. In International Workshop on Reproducible Research in Pattern Recognition (Vol. 14068, pp. 67–73). Montreal, Canada: Springer Nature. https://doi.org/10.1007/978-3-031-40773-4_6
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2023 |Published| Journal Article | IST-REx-ID: 14446 | OA
Jakubík, J., Phuong, M., Chvosteková, M., & Krakovská, A. (2023). Against the flow of time with multi-output models. Measurement Science Review. Sciendo. https://doi.org/10.2478/msr-2023-0023
[Published Version] View | Files available | DOI
 

2023 |Published| Conference Paper | IST-REx-ID: 14771 | OA
Iofinova, E. B., Peste, E.-A., & Alistarh, D.-A. (2023). Bias in pruned vision models: In-depth analysis and countermeasures. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 24364–24373). Vancouver, BC, Canada: IEEE. https://doi.org/10.1109/cvpr52729.2023.02334
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 

2023 |Submitted| Preprint | IST-REx-ID: 15039 | OA
Prach, B., & Lampert, C. (n.d.). 1-Lipschitz neural networks are more expressive with N-activations. arXiv. https://doi.org/10.48550/ARXIV.2311.06103
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 

2023 |Published| Thesis | IST-REx-ID: 13074 | OA
Peste, E.-A. (2023). Efficiency and generalization of sparse neural networks. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:13074
[Published Version] View | Files available | DOI
 

2023 |Published| Conference Paper | IST-REx-ID: 13053 | OA
Krumes, A., Vladu, A., Kurtic, E., Lampert, C., & Alistarh, D.-A. (2023). CrAM: A Compression-Aware Minimizer. In 11th International Conference on Learning Representations . Kigali, Rwanda : OpenReview.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 

2023 |Published| Conference Paper | IST-REx-ID: 14921 | OA
Súkeník, P., Mondelli, M., & Lampert, C. (2023). Deep neural collapse is provably optimal for the deep unconstrained features model. In 37th Annual Conference on Neural Information Processing Systems. New Orleans, LA, United States.
[Preprint] View | Download Preprint (ext.) | arXiv
 

2022 |Submitted| Preprint | IST-REx-ID: 12660 | OA
Scott, J. A., Yeo, M. X., & Lampert, C. (n.d.). Cross-client Label Propagation for transductive federated learning. arXiv. https://doi.org/10.48550/arXiv.2210.06434
[Preprint] View | Files available | DOI | arXiv
 

2022 |Submitted| Preprint | IST-REx-ID: 12662 | OA
Súkeník, P., & Lampert, C. (n.d.). Generalization in Multi-objective machine learning. arXiv. https://doi.org/10.48550/arXiv.2208.13499
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 

2022 |Published| Journal Article | IST-REx-ID: 12495 | OA
Iofinova, E. B., Konstantinov, N. H., & Lampert, C. (2022). FLEA: Provably robust fair multisource learning from unreliable training data. Transactions on Machine Learning Research. ML Research Press.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 

2022 |Published| Conference Paper | IST-REx-ID: 11839 | OA
Prach, B., & Lampert, C. (2022). Almost-orthogonal layers for efficient general-purpose Lipschitz networks. In Computer Vision – ECCV 2022 (Vol. 13681, pp. 350–365). Tel Aviv, Israel: Springer Nature. https://doi.org/10.1007/978-3-031-19803-8_21
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 

2022 |Published| Conference Paper | IST-REx-ID: 12161 | OA
Tomaszewska, P., & Lampert, C. (2022). Lightweight conditional model extrapolation for streaming data under class-prior shift. In 26th International Conference on Pattern Recognition (Vol. 2022, pp. 2128–2134). Montreal, Canada: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/icpr56361.2022.9956195
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 

2022 |Published| Conference Paper | IST-REx-ID: 12299 | OA
Iofinova, E. B., Peste, E.-A., Kurtz, M., & Alistarh, D.-A. (2022). How well do sparse ImageNet models transfer? In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 12256–12266). New Orleans, LA, United States: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/cvpr52688.2022.01195
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 

2022 |Published| Journal Article | IST-REx-ID: 10802 | OA
Konstantinov, N. H., & Lampert, C. (2022). Fairness-aware PAC learning from corrupted data. Journal of Machine Learning Research. ML Research Press.
[Published Version] View | Files available | arXiv
 

2022 |Published| Conference Paper | IST-REx-ID: 13241 | OA
Konstantinov, N. H., & Lampert, C. (2022). On the impossibility of fairness-aware learning from corrupted data. In Proceedings of Machine Learning Research (Vol. 171, pp. 59–83). ML Research Press.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 

2022 |Published| Thesis | IST-REx-ID: 10799 | OA
Konstantinov, N. H. (2022). Robustness and fairness in machine learning. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:10799
[Published Version] View | Files available | DOI
 

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