Eugenia Iofinova

11 Publications

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[11]
2026 | Published | Journal Article | IST-REx-ID: 21839 | OA
Sin, Celine, Martin Luther Watzenboeck, Eugenia B Iofinova, Lorenz Balcar, Georg Semmler, Bernhard Scheiner, Katharina Lampichler, et al. “Radiomics‐based Assessment of Portal Hypertension Severity and Risk Stratification of Cirrhotic Patients Using Routine CT Scans.” Liver International. Wiley, 2026. 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, Eugenia B. “On the Utility and Effects of Efficiency in Artificial Neural Networks.” Institute of Science and Technology Austria, 2026. 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, Armand, Eugenia B Iofinova, Andrej Jovanovic, Eldar Kurtic, Mahdi Nikdan, Andrei Panferov, Ilia Markov, Nir Shavit, and Dan-Adrian Alistarh. Panza: Investigating the Feasibility of Fully-Local Personalized Text Generation. Third Conference on Parsimony and Learning (Proceedings Track). OpenReview, 2026.
[Accepted Version] View | Files available | Download Accepted Version (ext.)
 
[8]
2026 | Draft | Preprint | IST-REx-ID: 21859 | OA
Iofinova, Eugenia B, and Dan-Adrian Alistarh. “Behemoth: Benchmarking Unlearning in LLMs Using Fully Synthetic Data.” ArXiv, n.d. 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, Eugenia B, Andrej Jovanovic, and Dan-Adrian Alistarh. “Position: It’s Time to Act on the Risk of Efficient Personalized Text Generation.” ArXiv, n.d. 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, Arshia Soltani, Eugenia B Iofinova, Elias Frantar, and Dan-Adrian Alistarh. “SPADE: Sparsity-Guided Debugging for Deep Neural Networks.” In Proceedings of the 41st International Conference on Machine Learning, 235:45955–87. ML Research Press, 2024.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[5]
2023 | Published | Conference Paper | IST-REx-ID: 14460 | OA
Nikdan, Mahdi, Tommaso Pegolotti, Eugenia B Iofinova, Eldar Kurtic, and Dan-Adrian Alistarh. “SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks at the Edge.” In Proceedings of the 40th International Conference on Machine Learning, 202:26215–27. ML Research Press, 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[4]
2023 | Published | Conference Paper | IST-REx-ID: 14771 | OA
Iofinova, Eugenia B, Alexandra Krumes, and Dan-Adrian Alistarh. “Bias in Pruned Vision Models: In-Depth Analysis and Countermeasures.” In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 24364–73. IEEE, 2023. 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, Eugenia B, Alexandra Krumes, Mark Kurtz, and Dan-Adrian Alistarh. “How Well Do Sparse ImageNet Models Transfer?” 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.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[2]
2022 | Published | Journal Article | IST-REx-ID: 12495 | OA
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.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[1]
2021 | Published | Conference Paper | IST-REx-ID: 11458 | OA
Krumes, Alexandra, Eugenia B Iofinova, Adrian Vladu, and Dan-Adrian Alistarh. “AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks.” In 35th Conference on Neural Information Processing Systems, 34:8557–70. Neural Information Processing Systems Foundation, 2021.
[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, Celine, Martin Luther Watzenboeck, Eugenia B Iofinova, Lorenz Balcar, Georg Semmler, Bernhard Scheiner, Katharina Lampichler, et al. “Radiomics‐based Assessment of Portal Hypertension Severity and Risk Stratification of Cirrhotic Patients Using Routine CT Scans.” Liver International. Wiley, 2026. 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, Eugenia B. “On the Utility and Effects of Efficiency in Artificial Neural Networks.” Institute of Science and Technology Austria, 2026. 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, Armand, Eugenia B Iofinova, Andrej Jovanovic, Eldar Kurtic, Mahdi Nikdan, Andrei Panferov, Ilia Markov, Nir Shavit, and Dan-Adrian Alistarh. Panza: Investigating the Feasibility of Fully-Local Personalized Text Generation. Third Conference on Parsimony and Learning (Proceedings Track). OpenReview, 2026.
[Accepted Version] View | Files available | Download Accepted Version (ext.)
 
[8]
2026 | Draft | Preprint | IST-REx-ID: 21859 | OA
Iofinova, Eugenia B, and Dan-Adrian Alistarh. “Behemoth: Benchmarking Unlearning in LLMs Using Fully Synthetic Data.” ArXiv, n.d. 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, Eugenia B, Andrej Jovanovic, and Dan-Adrian Alistarh. “Position: It’s Time to Act on the Risk of Efficient Personalized Text Generation.” ArXiv, n.d. 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, Arshia Soltani, Eugenia B Iofinova, Elias Frantar, and Dan-Adrian Alistarh. “SPADE: Sparsity-Guided Debugging for Deep Neural Networks.” In Proceedings of the 41st International Conference on Machine Learning, 235:45955–87. ML Research Press, 2024.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[5]
2023 | Published | Conference Paper | IST-REx-ID: 14460 | OA
Nikdan, Mahdi, Tommaso Pegolotti, Eugenia B Iofinova, Eldar Kurtic, and Dan-Adrian Alistarh. “SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks at the Edge.” In Proceedings of the 40th International Conference on Machine Learning, 202:26215–27. ML Research Press, 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[4]
2023 | Published | Conference Paper | IST-REx-ID: 14771 | OA
Iofinova, Eugenia B, Alexandra Krumes, and Dan-Adrian Alistarh. “Bias in Pruned Vision Models: In-Depth Analysis and Countermeasures.” In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 24364–73. IEEE, 2023. 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, Eugenia B, Alexandra Krumes, Mark Kurtz, and Dan-Adrian Alistarh. “How Well Do Sparse ImageNet Models Transfer?” 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.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[2]
2022 | Published | Journal Article | IST-REx-ID: 12495 | OA
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.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[1]
2021 | Published | Conference Paper | IST-REx-ID: 11458 | OA
Krumes, Alexandra, Eugenia B Iofinova, Adrian Vladu, and Dan-Adrian Alistarh. “AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks.” In 35th Conference on Neural Information Processing Systems, 34:8557–70. Neural Information Processing Systems Foundation, 2021.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
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