Peste E-A, Vladu A, Kurtic E, Lampert C, Alistarh D-A. CrAM: A Compression-Aware Minimizer. In: 11th International Conference on Learning Representations .

Tomaszewska P, Lampert C. 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. Springer Nature; 2023:67-73. doi:10.1007/978-3-031-40773-4_6

Súkeník P, Mondelli M, Lampert C. Deep neural collapse is provably optimal for the deep unconstrained features model. In: 37th Annual Conference on Neural Information Processing Systems.

Prach B, Lampert C. 1-Lipschitz neural networks are more expressive with N-activations. arXiv. doi:10.48550/ARXIV.2311.06103

Scott JA, Yeo MX, Lampert C. Cross-client Label Propagation for transductive federated learning. arXiv. doi:10.48550/arXiv.2210.06434

Súkeník P, Lampert C. Generalization in Multi-objective machine learning. arXiv. doi:10.48550/arXiv.2208.13499

Iofinova EB, Konstantinov NH, Lampert C. FLEA: Provably robust fair multisource learning from unreliable training data. Transactions on Machine Learning Research. 2022.

Prach B, Lampert C. Almost-orthogonal layers for efficient general-purpose Lipschitz networks. In: Computer Vision – ECCV 2022. Vol 13681. Springer Nature; 2022:350-365. doi:10.1007/978-3-031-19803-8_21

Lampert J, Lampert C. Overcoming rare-language discrimination in multi-lingual sentiment analysis. In: 2021 IEEE International Conference on Big Data. IEEE; 2022:5185-5192. doi:10.1109/bigdata52589.2021.9672003

Tomaszewska P, Lampert C. Lightweight conditional model extrapolation for streaming data under class-prior shift. In: 26th International Conference on Pattern Recognition. Vol 2022. Institute of Electrical and Electronics Engineers; 2022:2128-2134. doi:10.1109/icpr56361.2022.9956195

Konstantinov NH, Lampert C. Fairness-aware PAC learning from corrupted data. Journal of Machine Learning Research. 2022;23:1-60.

Konstantinov NH, Lampert C. On the impossibility of fairness-aware learning from corrupted data. In: Proceedings of Machine Learning Research. Vol 171. ML Research Press; 2022:59-83.

Volhejn V, Lampert C. Does SGD implicitly optimize for smoothness? In: 42nd German Conference on Pattern Recognition. Vol 12544. LNCS. Springer; 2021:246-259. doi:10.1007/978-3-030-71278-5_18

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

Konstantinov NH, Lampert C. Fairness through regularization for learning to rank. arXiv. doi:10.48550/arXiv.2102.05996

Lampert C. Zero-Shot Learning. In: Ikeuchi K, ed. Computer Vision. 2nd ed. Cham: Springer; 2021:1395-1397. doi:10.1007/978-3-030-63416-2_874

Anciukevicius T, Lampert C, Henderson PM. Object-centric image generation with factored depths, locations, and appearances. arXiv.

Henderson PM, Lampert C. Unsupervised object-centric video generation and decomposition in 3D. In: 34th Conference on Neural Information Processing Systems. Vol 33. Curran Associates; 2020:3106–3117.

Royer A, Lampert C. Localizing grouped instances for efficient detection in low-resource scenarios. In: IEEE Winter Conference on Applications of Computer Vision. IEEE; 2020. doi:10.1109/WACV45572.2020.9093288

Royer A, Lampert C. A flexible selection scheme for minimum-effort transfer learning. In: 2020 IEEE Winter Conference on Applications of Computer Vision. IEEE; 2020. doi:10.1109/WACV45572.2020.9093635