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

Peste, Elena-Alexandra, Adrian Vladu, Eldar Kurtic, Christoph Lampert, and Dan-Adrian Alistarh. “CrAM: A Compression-Aware Minimizer.” In 11th International Conference on Learning Representations , n.d.
LibreCat: https://research-explorer.ista.ac.at/record/13053
arXiv: 2207.14200

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

Henderson, Paul M, Areg Ghazaryan, Alexander A. Zibrov, Andrea F. Young, and Maksym Serbyn. “Deep Learning Extraction of Band Structure Parameters from Density of States: A Case Study on Trilayer Graphene.”https://doi.org/10.1103/physrevb.108.125411__Physical Review B. American Physical Society, 2023.https://doi.org/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.”https://doi.org/10.1007/978-3-031-40773-4_6__In International Workshop on Reproducible Research in Pattern Recognition, 14068:67–73. Springer Nature, 2023.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

Jakubík, Jozef, Mary Phuong, Martina Chvosteková, and Anna Krakovská. “Against the Flow of Time with Multi-Output Models.”https://doi.org/10.2478/msr-2023-0023__Measurement Science Review. Sciendo, 2023.https://doi.org/10.2478/msr-2023-0023 .
LibreCat: https://research-explorer.ista.ac.at/record/14446
DOI: 10.2478/msr-2023-0023

Iofinova, Eugenia B, Elena-Alexandra Peste, and Dan-Adrian Alistarh. “Bias in Pruned Vision Models: In-Depth Analysis and Countermeasures.”https://doi.org/10.1109/cvpr52729.2023.02334__In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 24364–73. IEEE, 2023.https://doi.org/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, Marco Mondelli, and Christoph Lampert. “Deep Neural Collapse Is Provably Optimal for the Deep Unconstrained Features Model.” In 37th Annual Conference on Neural Information Processing Systems, n.d.
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.”https://doi.org/10.48550/ARXIV.2311.06103__ArXiv, n.d.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, Jonathan A, Michelle X Yeo, and Christoph Lampert. “Cross-Client Label Propagation for Transductive Federated Learning.”https://doi.org/10.48550/arXiv.2210.06434__ArXiv, n.d.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, Peter, and Christoph Lampert. “Generalization in Multi-Objective Machine Learning.”https://doi.org/10.48550/arXiv.2208.13499__ArXiv, n.d.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, Eugenia B, Nikola H Konstantinov, and Christoph Lampert. “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.”https://doi.org/10.1007/978-3-031-19803-8_21__In Computer Vision – ECCV 2022, 13681:350–65. Springer Nature, 2022.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, Jasmin, and Christoph Lampert. “Overcoming Rare-Language Discrimination in Multi-Lingual Sentiment Analysis.”https://doi.org/10.1109/bigdata52589.2021.9672003__In 2021 IEEE International Conference on Big Data, 5185–92. IEEE, 2022.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, Paulina, and Christoph Lampert. “Lightweight Conditional Model Extrapolation for Streaming Data under Class-Prior Shift.”https://doi.org/10.1109/icpr56361.2022.9956195__In 26th International Conference on Pattern Recognition, 2022:2128–34. Institute of Electrical and Electronics Engineers, 2022.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

Iofinova, Eugenia B, Elena-Alexandra Peste, Mark Kurtz, and Dan-Adrian Alistarh. “How Well Do Sparse ImageNet Models Transfer?”https://doi.org/10.1109/cvpr52688.2022.01195__In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 12256–66. Institute of Electrical and Electronics Engineers, 2022.https://doi.org/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. ML Research Press, 2022.
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.” In Proceedings of Machine Learning Research, 171:59–83. ML Research Press, 2022.
LibreCat: https://research-explorer.ista.ac.at/record/13241
arXiv: 2102.06004

Konstantinov, Nikola H. “Robustness and Fairness in Machine Learning.”https://doi.org/10.15479/at:ista:10799__Institute of Science and Technology Austria, 2022.https://doi.org/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?”https://doi.org/10.1007/978-3-030-71278-5_18__In 42nd German Conference on Pattern Recognition, 12544:246–59. LNCS. Springer, 2021.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