Radiomicsbased assessment of portal hypertension severity and risk stratification of cirrhotic patients using routine CT scans
C. Sin, M.L. Watzenboeck, E.B. Iofinova, L. Balcar, G. Semmler, B. Scheiner, K. Lampichler, M. Mandorfer, L. Moga, P. Rautou, M. Ronot, J. Menche, T. Reiberger, M. Scharitzer, Liver International 46 (2026).

On the utility and effects of efficiency in artificial neural networks
E.B. Iofinova, On the Utility and Effects of Efficiency in Artificial Neural Networks, Institute of Science and Technology Austria, 2026.

Panza: Investigating the feasibility of fully-local personalized text generation
A. Nicolicioiu, E.B. Iofinova, A. Jovanovic, E. Kurtic, M. Nikdan, A. Panferov, I. Markov, N. Shavit, D.-A. Alistarh, Panza: Investigating the Feasibility of Fully-Local Personalized Text Generation, OpenReview, 2026.

Behemoth: Benchmarking unlearning in LLMs using fully synthetic data
E.B. Iofinova, D.-A. Alistarh, ArXiv (n.d.).

Position: It's time to act on the risk of efficient personalized text generation
E.B. Iofinova, A. Jovanovic, D.-A. Alistarh, ArXiv (n.d.).

SPADE: Sparsity-guided debugging for deep neural networks
A.S. Moakhar, E.B. Iofinova, E. Frantar, D.-A. Alistarh, in:, Proceedings of the 41st International Conference on Machine Learning, ML Research Press, 2024, pp. 45955–45987.

SparseProp: Efficient sparse backpropagation for faster training of neural networks at the edge
M. Nikdan, T. Pegolotti, E.B. Iofinova, E. Kurtic, D.-A. Alistarh, in:, Proceedings of the 40th International Conference on Machine Learning, ML Research Press, 2023, pp. 26215–26227.

Bias in pruned vision models: In-depth analysis and countermeasures
E.B. Iofinova, A. Krumes, D.-A. Alistarh, in:, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2023, pp. 24364–24373.

How well do sparse ImageNet models transfer?
E.B. Iofinova, A. Krumes, 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.

FLEA: Provably robust fair multisource learning from unreliable training data
E.B. Iofinova, N.H. Konstantinov, C. Lampert, Transactions on Machine Learning Research (2022).

AC/DC: Alternating Compressed/DeCompressed training of deep neural networks
A. Krumes, E.B. Iofinova, A. Vladu, D.-A. Alistarh, in:, 35th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2021, pp. 8557–8570.