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114 Publications


2023 | Accepted | Conference Paper | IST-REx-ID: 13053 | OA
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.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 

2023 | Published | Thesis | IST-REx-ID: 13074 | OA
Efficiency and generalization of sparse neural networks
E.-A. Peste, Efficiency and Generalization of Sparse Neural Networks, Institute of Science and Technology Austria, 2023.
[Published Version] View | Files available | DOI
 

2023 | Published | Journal Article | IST-REx-ID: 14320 | OA
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).
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 

2023 | Published | Conference Paper | IST-REx-ID: 14410
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.
View | DOI
 

2023 | Published | Journal Article | IST-REx-ID: 14446 | OA
Against the flow of time with multi-output models
J. Jakubík, M. Phuong, M. Chvosteková, A. Krakovská, Measurement Science Review 23 (2023) 175–183.
[Published Version] View | Files available | DOI
 

2023 | Published | Conference Paper | IST-REx-ID: 14771 | OA
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.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 

2023 | In Press | Conference Paper | IST-REx-ID: 14921 | OA
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.
[Preprint] View | Download Preprint (ext.) | arXiv
 

2023 | Submitted | Preprint | IST-REx-ID: 15039 | OA [Preprint] View | DOI | Download Preprint (ext.) | arXiv
 

2022 | Published | Conference Paper | IST-REx-ID: 12299 | OA
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.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 

2022 | Published | Journal Article | IST-REx-ID: 12495 | OA
FLEA: Provably robust fair multisource learning from unreliable training data
E.B. Iofinova, N.H. Konstantinov, C. Lampert, Transactions on Machine Learning Research (2022).
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 

2022 | Submitted | Preprint | IST-REx-ID: 12660 | OA
Cross-client Label Propagation for transductive federated learning
J.A. Scott, M.X. Yeo, C. Lampert, ArXiv (n.d.).
[Preprint] View | Files available | DOI | arXiv
 

2022 | Submitted | Preprint | IST-REx-ID: 12662 | OA
Generalization in Multi-objective machine learning
P. Súkeník, C. Lampert, ArXiv (n.d.).
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 

2022 | Published | Conference Paper | IST-REx-ID: 13241 | OA
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.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 

2022 | Published | Conference Paper | IST-REx-ID: 11839 | OA
Almost-orthogonal layers for efficient general-purpose Lipschitz networks
B. Prach, C. Lampert, in:, Computer Vision – ECCV 2022, Springer Nature, 2022, pp. 350–365.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 

2022 | Published | Conference Paper | IST-REx-ID: 12161 | OA
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.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 

2022 | Published | Conference Paper | IST-REx-ID: 10752
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.
View | DOI | WoS
 

2022 | Published | Thesis | IST-REx-ID: 10799 | OA
Robustness and fairness in machine learning
N.H. Konstantinov, Robustness and Fairness in Machine Learning, Institute of Science and Technology Austria, 2022.
[Published Version] View | Files available | DOI
 

2022 | Published | Journal Article | IST-REx-ID: 10802 | OA
Fairness-aware PAC learning from corrupted data
N.H. Konstantinov, C. Lampert, Journal of Machine Learning Research 23 (2022) 1–60.
[Published Version] View | Files available | arXiv
 

2021 | Published | Book Chapter | IST-REx-ID: 14987
Zero-Shot Learning
C. Lampert, in:, K. Ikeuchi (Ed.), Computer Vision, 2nd ed., Springer, Cham, 2021, pp. 1395–1397.
View | DOI
 

2021 | Published | Conference Paper | IST-REx-ID: 9210 | OA
Does SGD implicitly optimize for smoothness?
V. Volhejn, C. Lampert, in:, 42nd German Conference on Pattern Recognition, Springer, 2021, pp. 246–259.
[Submitted Version] View | Files available | DOI
 

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