Eugenia Iofinova

11 Publications

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[11]
2026 | Published | Journal Article | IST-REx-ID: 21839 | OA
Sin, C., Watzenboeck, M. L., Iofinova, E. B., Balcar, L., Semmler, G., Scheiner, B., … Scharitzer, M. (2026). Radiomics‐based assessment of portal hypertension severity and risk stratification of cirrhotic patients using routine CT scans. Liver International. Wiley. https://doi.org/10.1111/liv.70633
[Published Version] View | Files available | DOI | PubMed | Europe PMC
 
[10]
2026 | Published | Thesis | PhD | IST-REx-ID: 21854 | OA
Iofinova, E. B. (2026). On the utility and effects of efficiency in artificial neural networks. Institute of Science and Technology Austria. https://doi.org/10.15479/AT-ISTA-21854
[Published Version] View | Files available | DOI
 
[9]
2026 | Published | Conference Poster | IST-REx-ID: 21857 | OA
Nicolicioiu, A., Iofinova, E. B., Jovanovic, A., Kurtic, E., Nikdan, M., Panferov, A., … Alistarh, D.-A. (2026). Panza: Investigating the feasibility of fully-local personalized text generation. Third Conference on Parsimony and Learning (Proceedings Track). Tübíngen, Germany: OpenReview.
[Accepted Version] View | Files available | Download Accepted Version (ext.)
 
[8]
2026 | Draft | Preprint | IST-REx-ID: 21859 | OA
Iofinova, E. B., & Alistarh, D.-A. (n.d.). Behemoth: Benchmarking unlearning in LLMs using fully synthetic data. arXiv. https://doi.org/10.48550/arXiv.2601.23153
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
[7]
2025 | Draft | Preprint | IST-REx-ID: 21858 | OA
Iofinova, E. B., Jovanovic, A., & Alistarh, D.-A. (n.d.). Position: It’s time to act on the risk of efficient personalized text generation. arXiv. https://doi.org/10.48550/arXiv.2502.06560
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
[6]
2024 | Published | Conference Paper | IST-REx-ID: 18121 | OA
Moakhar, A. S., Iofinova, E. B., Frantar, E., & Alistarh, D.-A. (2024). SPADE: Sparsity-guided debugging for deep neural networks. In Proceedings of the 41st International Conference on Machine Learning (Vol. 235, pp. 45955–45987). Vienna, Austria: ML Research Press.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[5]
2023 | Published | Conference Paper | IST-REx-ID: 14460 | OA
Nikdan, M., Pegolotti, T., Iofinova, E. B., Kurtic, E., & Alistarh, D.-A. (2023). SparseProp: Efficient sparse backpropagation for faster training of neural networks at the edge. In Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 26215–26227). Honolulu, Hawaii, HI, United States: ML Research Press.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[4]
2023 | Published | Conference Paper | IST-REx-ID: 14771 | OA
Iofinova, E. B., Krumes, A., & Alistarh, D.-A. (2023). Bias in pruned vision models: In-depth analysis and countermeasures. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 24364–24373). Vancouver, BC, Canada: IEEE. https://doi.org/10.1109/cvpr52729.2023.02334
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[3]
2022 | Published | Conference Paper | IST-REx-ID: 12299 | OA
Iofinova, E. B., Krumes, A., Kurtz, M., & Alistarh, D.-A. (2022). How well do sparse ImageNet models transfer? In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 12256–12266). New Orleans, LA, United States: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/cvpr52688.2022.01195
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[2]
2022 | Published | Journal Article | IST-REx-ID: 12495 | OA
Iofinova, E. B., Konstantinov, N. H., & Lampert, C. (2022). FLEA: Provably robust fair multisource learning from unreliable training data. Transactions on Machine Learning Research. ML Research Press.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[1]
2021 | Published | Conference Paper | IST-REx-ID: 11458 | OA
Krumes, A., Iofinova, E. B., Vladu, A., & Alistarh, D.-A. (2021). AC/DC: Alternating Compressed/DeCompressed training of deep neural networks. In 35th Conference on Neural Information Processing Systems (Vol. 34, pp. 8557–8570). Virtual, Online: Neural Information Processing Systems Foundation.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
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11 Publications

Mark all

[11]
2026 | Published | Journal Article | IST-REx-ID: 21839 | OA
Sin, C., Watzenboeck, M. L., Iofinova, E. B., Balcar, L., Semmler, G., Scheiner, B., … Scharitzer, M. (2026). Radiomics‐based assessment of portal hypertension severity and risk stratification of cirrhotic patients using routine CT scans. Liver International. Wiley. https://doi.org/10.1111/liv.70633
[Published Version] View | Files available | DOI | PubMed | Europe PMC
 
[10]
2026 | Published | Thesis | PhD | IST-REx-ID: 21854 | OA
Iofinova, E. B. (2026). On the utility and effects of efficiency in artificial neural networks. Institute of Science and Technology Austria. https://doi.org/10.15479/AT-ISTA-21854
[Published Version] View | Files available | DOI
 
[9]
2026 | Published | Conference Poster | IST-REx-ID: 21857 | OA
Nicolicioiu, A., Iofinova, E. B., Jovanovic, A., Kurtic, E., Nikdan, M., Panferov, A., … Alistarh, D.-A. (2026). Panza: Investigating the feasibility of fully-local personalized text generation. Third Conference on Parsimony and Learning (Proceedings Track). Tübíngen, Germany: OpenReview.
[Accepted Version] View | Files available | Download Accepted Version (ext.)
 
[8]
2026 | Draft | Preprint | IST-REx-ID: 21859 | OA
Iofinova, E. B., & Alistarh, D.-A. (n.d.). Behemoth: Benchmarking unlearning in LLMs using fully synthetic data. arXiv. https://doi.org/10.48550/arXiv.2601.23153
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
[7]
2025 | Draft | Preprint | IST-REx-ID: 21858 | OA
Iofinova, E. B., Jovanovic, A., & Alistarh, D.-A. (n.d.). Position: It’s time to act on the risk of efficient personalized text generation. arXiv. https://doi.org/10.48550/arXiv.2502.06560
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
[6]
2024 | Published | Conference Paper | IST-REx-ID: 18121 | OA
Moakhar, A. S., Iofinova, E. B., Frantar, E., & Alistarh, D.-A. (2024). SPADE: Sparsity-guided debugging for deep neural networks. In Proceedings of the 41st International Conference on Machine Learning (Vol. 235, pp. 45955–45987). Vienna, Austria: ML Research Press.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[5]
2023 | Published | Conference Paper | IST-REx-ID: 14460 | OA
Nikdan, M., Pegolotti, T., Iofinova, E. B., Kurtic, E., & Alistarh, D.-A. (2023). SparseProp: Efficient sparse backpropagation for faster training of neural networks at the edge. In Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 26215–26227). Honolulu, Hawaii, HI, United States: ML Research Press.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[4]
2023 | Published | Conference Paper | IST-REx-ID: 14771 | OA
Iofinova, E. B., Krumes, A., & Alistarh, D.-A. (2023). Bias in pruned vision models: In-depth analysis and countermeasures. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 24364–24373). Vancouver, BC, Canada: IEEE. https://doi.org/10.1109/cvpr52729.2023.02334
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[3]
2022 | Published | Conference Paper | IST-REx-ID: 12299 | OA
Iofinova, E. B., Krumes, A., Kurtz, M., & Alistarh, D.-A. (2022). How well do sparse ImageNet models transfer? In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 12256–12266). New Orleans, LA, United States: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/cvpr52688.2022.01195
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[2]
2022 | Published | Journal Article | IST-REx-ID: 12495 | OA
Iofinova, E. B., Konstantinov, N. H., & Lampert, C. (2022). FLEA: Provably robust fair multisource learning from unreliable training data. Transactions on Machine Learning Research. ML Research Press.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[1]
2021 | Published | Conference Paper | IST-REx-ID: 11458 | OA
Krumes, A., Iofinova, E. B., Vladu, A., & Alistarh, D.-A. (2021). AC/DC: Alternating Compressed/DeCompressed training of deep neural networks. In 35th Conference on Neural Information Processing Systems (Vol. 34, pp. 8557–8570). Virtual, Online: Neural Information Processing Systems Foundation.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
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