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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 

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 

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 

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 

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 

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.

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 

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 

Súkeník, P., &; Lampert, C. (n.d.). Generalization in Multi-objective machine learning. arXiv.https://doi.org/10.48550/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.

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 

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 

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 

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 

Konstantinov, N. H., &; Lampert, C. (2022). Fairness-aware PAC learning from corrupted data. Journal of Machine Learning Research. ML Research Press.

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.

Konstantinov, N. H. (2022). Robustness and fairness in machine learning. Institute of Science and Technology Austria.https://doi.org/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 

Phuong, M., &; Lampert, C. (2021). The inductive bias of ReLU networks on orthogonally separable data. In 9th International Conference on Learning Representations. Virtual.