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133 Publications
2024 | Published | Conference Paper | IST-REx-ID: 18120 | 
Scott, J. A., & Cahill, Á. (2024). Improved modelling of federated datasets using mixtures-of-Dirichlet-multinomials. In Proceedings of the 41st International Conference on Machine Learning (Vol. 235, pp. 44012–44037). Vienna, Austria: ML Research Press.
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| arXiv
2024 | Published | Preprint | IST-REx-ID: 19063 |
Zverev, E., Abdelnabi, S., Tabesh, S., Fritz, M., & Lampert, C. (2024). Can LLMs separate instructions from data? And what do we even mean by that? arXiv. https://doi.org/10.48550/arXiv.2403.06833
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| arXiv
2024 | Published | Journal Article | IST-REx-ID: 19408 |
Verwimp, E., Aljundi, R., Ben-David, S., Bethge, M., Cossu, A., Gepperth, A., … Van De Ven, G. M. (2024). Continual learning: Applications and the road forward. Transactions on Machine Learning Research. Transactions on Machine Learning Research.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18875 |
Kalinin, N., & Lampert, C. (2024). Banded square root matrix factorization for differentially private model training. In 38th Annual Conference on Neural Information Processing Systems (Vol. 37). Vancouver, Canada: Neural Information Processing Systems Foundation.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18891 |
Súkeník, P., Lampert, C., & Mondelli, M. (2024). Neural collapse versus low-rank bias: Is deep neural collapse really optimal? In 38th Annual Conference on Neural Information Processing Systems (Vol. 37). Vancouver, Canada: Neural Information Processing Systems Foundation.
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| arXiv
2024 | Published | Journal Article | IST-REx-ID: 18856 |
Lutsai, K., & Lampert, C. (2024). Predicting the geolocation of tweets using transformer models on customized data. Journal of Spatial Information Science. University of Maine. https://doi.org/10.5311/JOSIS.2024.29.295
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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). Seattle, WA, United States: Computer Vision Foundation. https://doi.org/10.1109/CVPR52733.2024.02320
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| arXiv
2024 | Draft | Preprint | IST-REx-ID: 18874 |
Prach, B., & Lampert, C. (n.d.). Intriguing properties of robust classification. arXiv. https://doi.org/10.48550/arXiv.2412.04245
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| arXiv
2023 | Published | Journal Article | IST-REx-ID: 14320 |
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
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| 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 | Conference Paper | IST-REx-ID: 12660 |
Scott, J. A., Yeo, M. X., & Lampert, C. (2023). Cross-client label propagation for transductive and semi-supervised federated learning. In Transactions in Machine Learning. Curran Associates.
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| arXiv
2023 | Published | Conference Paper | IST-REx-ID: 13053 |
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.
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| arXiv
2023 | Published | Conference Paper | IST-REx-ID: 14921 |
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.
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| arXiv
2023 | Draft | Preprint | IST-REx-ID: 15039 |
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
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| arXiv
2023 | Published | Journal Article | IST-REx-ID: 14446 |
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
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2023 | Published | Conference Paper | IST-REx-ID: 14771 |
Iofinova, E. B., Krumes, 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
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| arXiv
2023 | Published | Thesis | IST-REx-ID: 13074 |
Krumes, A. (2023). Efficiency and generalization of sparse neural networks. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:13074
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2022 | Published | Conference Paper | IST-REx-ID: 13241 |
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.
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| arXiv
2022 | Published | Conference Paper | IST-REx-ID: 12161 |
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
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| arXiv
2022 | Published | Conference Paper | IST-REx-ID: 10752
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
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