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119 Publications

2024 | Published | Conference Paper | IST-REx-ID: 17093 | OA
Zakerinia H, Talaei S, Nadiradze G, Alistarh D-A. Communication-efficient federated learning with data and client heterogeneity. In: Proceedings of the 27th International Conference on Artificial Intelligence and Statistics. Vol 238. ML Research Press; 2024:3448-3456.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2024 | Published | Conference Paper | IST-REx-ID: 17411 | OA
Scott JA, Zakerinia H, Lampert C. PEFLL: Personalized federated learning by learning to learn. In: 12th International Conference on Learning Representations. OpenReview; 2024.
[Published Version] View | Files available | arXiv
 
2024 | Published | Conference Paper | IST-REx-ID: 17426
Prach B, Brau F, Buttazzo G, Lampert C. 1-Lipschitz layers compared: Memory, speed, and certifiable robustness. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Computer Vision Foundation; 2024:24574-24583.
[Published Version] View | Files available
 
2024 | Published | Conference Paper | IST-REx-ID: 18118 | OA
Zakerinia H, Behjati A, Lampert C. More flexible PAC-Bayesian meta-learning by learning learning algorithms. In: Proceedings of the 41st International Conference on Machine Learning. Vol 235. ML Research Press; 2024:58122-58139.
[Published Version] View | Download Published Version (ext.) | arXiv
 
2024 | Published | Conference Paper | IST-REx-ID: 18120 | OA
Scott JA, Cahill Á. Improved modelling of federated datasets using mixtures-of-Dirichlet-multinomials. In: Proceedings of the 41st International Conference on Machine Learning. Vol 235. ML Research Press; 2024:44012-44037.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2023 | Published | Conference Paper | IST-REx-ID: 13053 | OA
Krumes A, Vladu A, Kurtic E, Lampert C, Alistarh D-A. CrAM: A Compression-Aware Minimizer. In: 11th International Conference on Learning Representations . OpenReview; 2023.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
2023 | Published | Thesis | IST-REx-ID: 13074 | OA
Krumes A. Efficiency and generalization of sparse neural networks. 2023. doi:10.15479/at:ista:13074
[Published Version] View | Files available | DOI
 
2023 | Published | Conference Paper | IST-REx-ID: 14771 | OA
Iofinova EB, Krumes A, Alistarh D-A. Bias in pruned vision models: In-depth analysis and countermeasures. In: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE; 2023:24364-24373. doi:10.1109/cvpr52729.2023.02334
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
2023 | Published | Conference Paper | IST-REx-ID: 14921 | OA
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. ; 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2023 | Submitted | Preprint | IST-REx-ID: 15039 | OA
Prach B, Lampert C. 1-Lipschitz neural networks are more expressive with N-activations. arXiv. doi:10.48550/ARXIV.2311.06103
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
2023 | Published | Journal Article | IST-REx-ID: 14320 | OA
Henderson PM, Ghazaryan A, Zibrov AA, Young AF, Serbyn M. Deep learning extraction of band structure parameters from density of states: A case study on trilayer graphene. Physical Review B. 2023;108(12). doi:10.1103/physrevb.108.125411
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
2023 | Published | Conference Paper | IST-REx-ID: 14410
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
View | DOI
 
2023 | Published | Journal Article | IST-REx-ID: 14446 | OA
Jakubík J, Phuong M, Chvosteková M, Krakovská A. Against the flow of time with multi-output models. Measurement Science Review. 2023;23(4):175-183. doi:10.2478/msr-2023-0023
[Published Version] View | Files available | DOI
 
2022 | Published | Conference Paper | IST-REx-ID: 10752
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
View | DOI | WoS
 
2022 | Published | Thesis | IST-REx-ID: 10799 | OA
Konstantinov NH. Robustness and fairness in machine learning. 2022. doi:10.15479/at:ista:10799
[Published Version] View | Files available | DOI
 
2022 | Published | Journal Article | IST-REx-ID: 10802 | OA
Konstantinov NH, Lampert C. Fairness-aware PAC learning from corrupted data. Journal of Machine Learning Research. 2022;23:1-60.
[Published Version] View | Files available | arXiv
 
2022 | Published | Conference Paper | IST-REx-ID: 12161 | OA
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
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
2022 | Published | Conference Paper | IST-REx-ID: 12299 | OA
Iofinova EB, Krumes A, Kurtz M, Alistarh D-A. How well do sparse ImageNet models transfer? In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Institute of Electrical and Electronics Engineers; 2022:12256-12266. doi:10.1109/cvpr52688.2022.01195
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
2022 | Published | Journal Article | IST-REx-ID: 12495 | OA
Iofinova EB, Konstantinov NH, Lampert C. FLEA: Provably robust fair multisource learning from unreliable training data. Transactions on Machine Learning Research. 2022.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
2022 | Submitted | Preprint | IST-REx-ID: 12660 | OA
Scott JA, Yeo MX, Lampert C. Cross-client Label Propagation for transductive federated learning. arXiv. doi:10.48550/arXiv.2210.06434
[Preprint] View | Files available | DOI | arXiv
 

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