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

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

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

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

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

Konstantinov, N. H., &; Lampert, C. (n.d.). Fairness through regularization for learning to rank. arXiv.https://doi.org/10.48550/arXiv.2102.05996 
LibreCat: https://research-explorer.ista.ac.at/record/10803
DOI: 10.48550/arXiv.2102.05996
arXiv: 2102.05996

Lampert, C. (2021). Zero-Shot Learning. In K. Ikeuchi (Ed.), Computer Vision (2nd ed., pp. 1395–1397). Cham: Springer.https://doi.org/10.1007/978-3-030-63416-2_874 
LibreCat: https://research-explorer.ista.ac.at/record/14987
DOI: 10.1007/978-3-030-63416-2_874

Anciukevicius, T., Lampert, C., &; Henderson, P. M. (n.d.). Object-centric image generation with factored depths, locations, and appearances. arXiv.
LibreCat: https://research-explorer.ista.ac.at/record/8063
arXiv: 2004.00642

Henderson, P. M., &; Lampert, C. (2020). Unsupervised object-centric video generation and decomposition in 3D. In 34th Conference on Neural Information Processing Systems (Vol. 33, pp. 3106–3117). Vancouver, Canada: Curran Associates.
LibreCat: https://research-explorer.ista.ac.at/record/8188
arXiv: 2007.06705

Royer, A., &; Lampert, C. (2020). Localizing grouped instances for efficient detection in low-resource scenarios. In IEEE Winter Conference on Applications of Computer Vision.https://doi.org/10.1109/WACV45572.2020.9093288__Snowmass Village, CO, United States: IEEE.https://doi.org/10.1109/WACV45572.2020.9093288 
LibreCat: https://research-explorer.ista.ac.at/record/7936
DOI: 10.1109/WACV45572.2020.9093288
arXiv: 2004.12623

Royer, A., &; Lampert, C. (2020). A flexible selection scheme for minimum-effort transfer learning. In 2020 IEEE Winter Conference on Applications of Computer Vision. Snowmass Village, CO, United States: IEEE.https://doi.org/10.1109/WACV45572.2020.9093635 
LibreCat: https://research-explorer.ista.ac.at/record/7937
DOI: 10.1109/WACV45572.2020.9093635
arXiv: 2008.11995