Communication-efficient federated learning with data and client heterogeneity
H. Zakerinia, S. Talaei, G. Nadiradze, D.-A. Alistarh, in:, Proceedings of the 27th International Conference on Artificial Intelligence and Statistics, ML Research Press, 2024, pp. 3448–3456.
LibreCat: https://research-explorer.ista.ac.at/record/17093
arXiv: 2206.10032

CrAM: A Compression-Aware Minimizer
E.-A. Peste, A. Vladu, E. Kurtic, C. Lampert, D.-A. Alistarh, in:, 11th International Conference on Learning Representations , n.d.
LibreCat: https://research-explorer.ista.ac.at/record/13053
arXiv: 2207.14200

Efficiency and generalization of sparse neural networks
E.-A. Peste, Efficiency and Generalization of Sparse Neural Networks, Institute of Science and Technology Austria, 2023.
LibreCat: https://research-explorer.ista.ac.at/record/13074
DOI: 10.15479/at:ista:13074

Deep learning extraction of band structure parameters from density of states: A case study on trilayer graphene
P.M. Henderson, A. Ghazaryan, A.A. Zibrov, A.F. Young, M. Serbyn, Physical Review B 108 (2023).
LibreCat: https://research-explorer.ista.ac.at/record/14320
DOI: 10.1103/physrevb.108.125411
arXiv: 2210.06310

On the implementation of baselines and lightweight conditional model extrapolation (LIMES) under class-prior shift
P. Tomaszewska, C. Lampert, in:, International Workshop on Reproducible Research in Pattern Recognition, Springer Nature, 2023, pp. 67–73.
LibreCat: https://research-explorer.ista.ac.at/record/14410
DOI: 10.1007/978-3-031-40773-4_6

Against the flow of time with multi-output models
J. Jakubík, M. Phuong, M. Chvosteková, A. Krakovská, Measurement Science Review 23 (2023) 175–183.
LibreCat: https://research-explorer.ista.ac.at/record/14446
DOI: 10.2478/msr-2023-0023

Bias in pruned vision models: In-depth analysis and countermeasures
E.B. Iofinova, E.-A. Peste, D.-A. Alistarh, in:, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2023, pp. 24364–24373.
LibreCat: https://research-explorer.ista.ac.at/record/14771
DOI: 10.1109/cvpr52729.2023.02334
WoS: 001062531308068
arXiv: 2304.12622

Deep neural collapse is provably optimal for the deep unconstrained features model
P. Súkeník, M. Mondelli, C. Lampert, in:, 37th Annual Conference on Neural Information Processing Systems, n.d.
LibreCat: https://research-explorer.ista.ac.at/record/14921
arXiv: 2305.13165

1-Lipschitz neural networks are more expressive with N-activations
B. Prach, C. Lampert, ArXiv (n.d.).
LibreCat: https://research-explorer.ista.ac.at/record/15039
DOI: 10.48550/ARXIV.2311.06103
arXiv: 2311.06103

Cross-client Label Propagation for transductive federated learning
J.A. Scott, M.X. Yeo, C. Lampert, ArXiv (n.d.).
LibreCat: https://research-explorer.ista.ac.at/record/12660
DOI: 10.48550/arXiv.2210.06434
arXiv: 2210.06434

Generalization in Multi-objective machine learning
P. Súkeník, C. Lampert, ArXiv (n.d.).
LibreCat: https://research-explorer.ista.ac.at/record/12662
DOI: 10.48550/arXiv.2208.13499
arXiv: 2208.13499

FLEA: Provably robust fair multisource learning from unreliable training data
E.B. Iofinova, N.H. Konstantinov, C. Lampert, Transactions on Machine Learning Research (2022).
LibreCat: https://research-explorer.ista.ac.at/record/12495
arXiv: 2106.11732

Almost-orthogonal layers for efficient general-purpose Lipschitz networks
B. Prach, C. Lampert, in:, Computer Vision – ECCV 2022, Springer Nature, 2022, pp. 350–365.
LibreCat: https://research-explorer.ista.ac.at/record/11839
DOI: 10.1007/978-3-031-19803-8_21
arXiv: 2208.03160

Overcoming rare-language discrimination in multi-lingual sentiment analysis
J. Lampert, C. Lampert, in:, 2021 IEEE International Conference on Big Data, IEEE, 2022, pp. 5185–5192.
LibreCat: https://research-explorer.ista.ac.at/record/10752
DOI: 10.1109/bigdata52589.2021.9672003
WoS: 000800559505036

Lightweight conditional model extrapolation for streaming data under class-prior shift
P. Tomaszewska, C. Lampert, in:, 26th International Conference on Pattern Recognition, Institute of Electrical and Electronics Engineers, 2022, pp. 2128–2134.
LibreCat: https://research-explorer.ista.ac.at/record/12161
DOI: 10.1109/icpr56361.2022.9956195
WoS: 000897707602018
arXiv: 2206.05181

How well do sparse ImageNet models transfer?
E.B. Iofinova, E.-A. Peste, M. Kurtz, D.-A. Alistarh, in:, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Institute of Electrical and Electronics Engineers, 2022, pp. 12256–12266.
LibreCat: https://research-explorer.ista.ac.at/record/12299
DOI: 10.1109/cvpr52688.2022.01195
WoS: 000870759105034
arXiv: 2111.13445

Fairness-aware PAC learning from corrupted data
N.H. Konstantinov, C. Lampert, Journal of Machine Learning Research 23 (2022) 1–60.
LibreCat: https://research-explorer.ista.ac.at/record/10802
arXiv: 2102.06004

On the impossibility of fairness-aware learning from corrupted data
N.H. Konstantinov, C. Lampert, in:, Proceedings of Machine Learning Research, ML Research Press, 2022, pp. 59–83.
LibreCat: https://research-explorer.ista.ac.at/record/13241
arXiv: 2102.06004

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

Does SGD implicitly optimize for smoothness?
V. Volhejn, C. Lampert, in:, 42nd German Conference on Pattern Recognition, Springer, 2021, pp. 246–259.
LibreCat: https://research-explorer.ista.ac.at/record/9210
DOI: 10.1007/978-3-030-71278-5_18