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.

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

Overcoming degeneracy and singularity : Techniques for semidefinite programs and homotopy continuation endgames
J. Zapata, Overcoming Degeneracy and Singularityf: Techniques for Semidefinite Programs and Homotopy Continuation Endgames, Institute of Science and Technology Austria, 2026.

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.).

Banana trees for the persistence in time series experimentally
L. Ost, S. Cultrera di Montesano, H. Edelsbrunner, in:, 41st International Symposium on Computational Geometry, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2025.

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.

High-dimensional limits in artificial neural networks
A. Shevchenko, High-Dimensional Limits in Artificial Neural Networks, Institute of Science and Technology Austria, 2024.

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).