Peste, E.-A., Vladu, A., Kurtic, E., Lampert, C., &; Alistarh, D.-A. (n.d.). CrAM: A Compression-Aware Minimizer. In 11th International Conference on Learning Representations . Kigali, Rwanda .
LibreCat: https://research-explorer.ista.ac.at/record/13053
arXiv: 2207.14200

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 
LibreCat: https://research-explorer.ista.ac.at/record/13074
DOI: 10.15479/at:ista:13074

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 
LibreCat: https://research-explorer.ista.ac.at/record/14320
DOI: 10.1103/physrevb.108.125411
arXiv: 2210.06310

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 
LibreCat: https://research-explorer.ista.ac.at/record/14410
DOI: 10.1007/978-3-031-40773-4_6

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 
LibreCat: https://research-explorer.ista.ac.at/record/14446
DOI: 10.2478/msr-2023-0023

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 
LibreCat: https://research-explorer.ista.ac.at/record/14771
DOI: 10.1109/cvpr52729.2023.02334
WoS: 001062531308068
arXiv: 2304.12622

Súkeník, P., Mondelli, M., &; Lampert, C. (n.d.). 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.
LibreCat: https://research-explorer.ista.ac.at/record/14921
arXiv: 2305.13165

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 
LibreCat: https://research-explorer.ista.ac.at/record/15039
DOI: 10.48550/ARXIV.2311.06103
arXiv: 2311.06103

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 
LibreCat: https://research-explorer.ista.ac.at/record/12660
DOI: 10.48550/arXiv.2210.06434
arXiv: 2210.06434

Súkeník, P., &; Lampert, C. (n.d.). Generalization in Multi-objective machine learning. arXiv.https://doi.org/10.48550/arXiv.2208.13499 
LibreCat: https://research-explorer.ista.ac.at/record/12662
DOI: 10.48550/arXiv.2208.13499
arXiv: 2208.13499

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.
LibreCat: https://research-explorer.ista.ac.at/record/12495
arXiv: 2106.11732

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 
LibreCat: https://research-explorer.ista.ac.at/record/11839
DOI: 10.1007/978-3-031-19803-8_21
arXiv: 2208.03160

Lampert, J., &; Lampert, C. (2022). Overcoming rare-language discrimination in multi-lingual sentiment analysis. In 2021 IEEE International Conference on Big Data (pp. 5185–5192). Orlando, FL, United States: IEEE.https://doi.org/10.1109/bigdata52589.2021.9672003 
LibreCat: https://research-explorer.ista.ac.at/record/10752
DOI: 10.1109/bigdata52589.2021.9672003
WoS: 000800559505036

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 
LibreCat: https://research-explorer.ista.ac.at/record/12161
DOI: 10.1109/icpr56361.2022.9956195
WoS: 000897707602018
arXiv: 2206.05181

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 
LibreCat: https://research-explorer.ista.ac.at/record/12299
DOI: 10.1109/cvpr52688.2022.01195
WoS: 000870759105034
arXiv: 2111.13445

Konstantinov, N. H., &; Lampert, C. (2022). Fairness-aware PAC learning from corrupted data. Journal of Machine Learning Research. ML Research Press.
LibreCat: https://research-explorer.ista.ac.at/record/10802
arXiv: 2102.06004

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.
LibreCat: https://research-explorer.ista.ac.at/record/13241
arXiv: 2102.06004

Konstantinov, N. H. (2022). Robustness and fairness in machine learning. Institute of Science and Technology Austria.https://doi.org/10.15479/at:ista:10799 
LibreCat: https://research-explorer.ista.ac.at/record/10799
DOI: 10.15479/at:ista:10799

Volhejn, V., &; Lampert, C. (2021). Does SGD implicitly optimize for smoothness? In 42nd German Conference on Pattern Recognition (Vol. 12544, pp. 246–259). Tübingen, Germany: Springer.https://doi.org/10.1007/978-3-030-71278-5_18 
LibreCat: https://research-explorer.ista.ac.at/record/9210
DOI: 10.1007/978-3-030-71278-5_18

Phuong, M., &; Lampert, C. (2021). The inductive bias of ReLU networks on orthogonally separable data. In 9th International Conference on Learning Representations. Virtual.
LibreCat: https://research-explorer.ista.ac.at/record/9416