Zakerinia, Hossein, et al. “Communication-Efficient Federated Learning with Data and Client Heterogeneity.” Proceedings of the 27th International Conference on Artificial Intelligence and Statistics, vol. 238, ML Research Press, 2024, pp. 3448–56.
LibreCat: https://research-explorer.ista.ac.at/record/17093
arXiv: 2206.10032

Peste, Elena-Alexandra, et al. “CrAM: A Compression-Aware Minimizer.” 11th International Conference on Learning Representations .
LibreCat: https://research-explorer.ista.ac.at/record/13053
arXiv: 2207.14200

Peste, Elena-Alexandra. Efficiency and Generalization of Sparse Neural Networks. Institute of Science and Technology Austria, 2023, doi:10.15479/at:ista:13074.
LibreCat: https://research-explorer.ista.ac.at/record/13074
DOI: 10.15479/at:ista:13074

Henderson, Paul M., et al. “Deep Learning Extraction of Band Structure Parameters from Density of States: A Case Study on Trilayer Graphene.”10.1103/physrevb.108.125411__Physical Review B, vol. 108, no. 12, 125411, American Physical Society, 2023, doi:10.1103/physrevb.108.125411.
LibreCat: https://research-explorer.ista.ac.at/record/14320
DOI: 10.1103/physrevb.108.125411
arXiv: 2210.06310

Tomaszewska, Paulina, and Christoph Lampert. “On the Implementation of Baselines and Lightweight Conditional Model Extrapolation (LIMES) under Class-Prior Shift.”10.1007/978-3-031-40773-4_6__International Workshop on Reproducible Research in Pattern Recognition, vol. 14068, Springer Nature, 2023, pp. 67–73, doi: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, Jozef, et al. “Against the Flow of Time with Multi-Output Models.”10.2478/msr-2023-0023__Measurement Science Review, vol. 23, no. 4, Sciendo, 2023, pp. 175–83, doi:10.2478/msr-2023-0023.
LibreCat: https://research-explorer.ista.ac.at/record/14446
DOI: 10.2478/msr-2023-0023

Iofinova, Eugenia B., et al. “Bias in Pruned Vision Models: In-Depth Analysis and Countermeasures.”10.1109/cvpr52729.2023.02334__2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2023, pp. 24364–73, doi: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, Peter, et al. “Deep Neural Collapse Is Provably Optimal for the Deep Unconstrained Features Model.” 37th Annual Conference on Neural Information Processing Systems.
LibreCat: https://research-explorer.ista.ac.at/record/14921
arXiv: 2305.13165

Prach, Bernd, and Christoph Lampert. “-Lipschitz Neural Networks Are More Expressive with N-Activations.”10.48550/ARXIV.2311.06103__ArXiv, 2311.06103, doi:10.48550/ARXIV.2311.06103.
LibreCat: https://research-explorer.ista.ac.at/record/15039
DOI: 10.48550/ARXIV.2311.06103
arXiv: 2311.06103

Scott, Jonathan A., et al. “Cross-Client Label Propagation for Transductive Federated Learning.”10.48550/arXiv.2210.06434__ArXiv, 2210.06434, doi:10.48550/arXiv.2210.06434.
LibreCat: https://research-explorer.ista.ac.at/record/12660
DOI: 10.48550/arXiv.2210.06434
arXiv: 2210.06434

Súkeník, Peter, and Christoph Lampert. “Generalization in Multi-Objective Machine Learning.”10.48550/arXiv.2208.13499__ArXiv, 2208.13499, doi:10.48550/arXiv.2208.13499.
LibreCat: https://research-explorer.ista.ac.at/record/12662
DOI: 10.48550/arXiv.2208.13499
arXiv: 2208.13499

Iofinova, Eugenia B., et al. “FLEA: Provably Robust Fair Multisource Learning from Unreliable Training Data.” Transactions on Machine Learning Research, ML Research Press, 2022.
LibreCat: https://research-explorer.ista.ac.at/record/12495
arXiv: 2106.11732

Prach, Bernd, and Christoph Lampert. “Almost-Orthogonal Layers for Efficient General-Purpose Lipschitz Networks.”10.1007/978-3-031-19803-8_21__Computer Vision – ECCV 2022, vol. 13681, Springer Nature, 2022, pp. 350–65, doi: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, Jasmin, and Christoph Lampert. “Overcoming Rare-Language Discrimination in Multi-Lingual Sentiment Analysis.”10.1109/bigdata52589.2021.9672003__2021 IEEE International Conference on Big Data, IEEE, 2022, pp. 5185–92, doi:10.1109/bigdata52589.2021.9672003.
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DOI: 10.1109/bigdata52589.2021.9672003
WoS: 000800559505036

Tomaszewska, Paulina, and Christoph Lampert. “Lightweight Conditional Model Extrapolation for Streaming Data under Class-Prior Shift.”10.1109/icpr56361.2022.9956195__26th International Conference on Pattern Recognition, vol. 2022, Institute of Electrical and Electronics Engineers, 2022, pp. 2128–34, doi: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, Eugenia B., et al. “How Well Do Sparse ImageNet Models Transfer?”10.1109/cvpr52688.2022.01195__2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Institute of Electrical and Electronics Engineers, 2022, pp. 12256–66, doi: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, Nikola H., and Christoph Lampert. “Fairness-Aware PAC Learning from Corrupted Data.” Journal of Machine Learning Research, vol. 23, ML Research Press, 2022, pp. 1–60.
LibreCat: https://research-explorer.ista.ac.at/record/10802
arXiv: 2102.06004

Konstantinov, Nikola H., and Christoph Lampert. “On the Impossibility of Fairness-Aware Learning from Corrupted Data.” Proceedings of Machine Learning Research, vol. 171, ML Research Press, 2022, pp. 59–83.
LibreCat: https://research-explorer.ista.ac.at/record/13241
arXiv: 2102.06004

Konstantinov, Nikola H. Robustness and Fairness in Machine Learning. Institute of Science and Technology Austria, 2022, doi:10.15479/at:ista:10799.
LibreCat: https://research-explorer.ista.ac.at/record/10799
DOI: 10.15479/at:ista:10799

Volhejn, Vaclav, and Christoph Lampert. “Does SGD Implicitly Optimize for Smoothness?”10.1007/978-3-030-71278-5_18__42nd German Conference on Pattern Recognition, vol. 12544, Springer, 2021, pp. 246–59, doi: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