CrAM: A Compression-Aware Minimizer
A. Krumes, A. Vladu, E. Kurtic, C. Lampert, D.-A. Alistarh, in:, 11th International Conference on Learning Representations , OpenReview, 2023.

Knowledge distillation performs partial variance reduction
M. Safaryan, A. Krumes, D.-A. Alistarh, in:, 36th Conference on Neural Information Processing Systems, 2023.

Efficiency and generalization of sparse neural networks
A. Krumes, Efficiency and Generalization of Sparse Neural Networks, Institute of Science and Technology Austria, 2023.

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.

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.

Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks
T. Hoefler, D.-A. Alistarh, T. Ben-Nun, N. Dryden, A. Krumes, Journal of Machine Learning Research 22 (2021) 1–124.