Kurtic, E., Hoefler, T., &; Alistarh, D.-A. (2024). How to prune your language model: Recovering accuracy on the “Sparsity May Cry” benchmark. In Proceedings of Machine Learning Research (Vol. 234, pp. 542–553). Hongkong, China: ML Research Press.
LibreCat: https://research-explorer.ista.ac.at/record/15011
arXiv: 2312.13547

Koval, N., Alistarh, D.-A., &; Elizarov, R. (2023). Fast and scalable channels in Kotlin Coroutines. In Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (pp. 107–118). Montreal, QC, Canada: Association for Computing Machinery.https://doi.org/10.1145/3572848.3577481 
LibreCat: https://research-explorer.ista.ac.at/record/12735
DOI: 10.1145/3572848.3577481
arXiv: 2211.04986

Aksenov, V., Brown, T. A., Fedorov, A., &; Kokorin, I. (2023). Unexpected scaling in path copying trees. Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (pp. 438–440). Montreal, QB, Canada: Association for Computing Machinery.https://doi.org/10.1145/3572848.3577512 
LibreCat: https://research-explorer.ista.ac.at/record/12736
DOI: 10.1145/3572848.3577512

Peste, E.-A., Vladu, A., Kurtic, E., Lampert, C., &; Alistarh, D.-A. (n.d.). CrAM: A Compression-Aware Minimizer. In 11th International Conference on Learning Representations . Kigali, Rwanda .
LibreCat: https://research-explorer.ista.ac.at/record/13053
arXiv: 2207.14200

Koval, N., Khalanskiy, D., &; Alistarh, D.-A. (2023). CQS: A formally-verified framework for fair and abortable synchronization. Proceedings of the ACM on Programming Languages. Association for Computing Machinery .https://doi.org/10.1145/3591230 
LibreCat: https://research-explorer.ista.ac.at/record/13179
DOI: 10.1145/3591230

Alistarh, D.-A., Ellen, F., &; Rybicki, J. (2023). Wait-free approximate agreement on graphs. Theoretical Computer Science. Elsevier.https://doi.org/10.1016/j.tcs.2023.113733 
LibreCat: https://research-explorer.ista.ac.at/record/12566
DOI: 10.1016/j.tcs.2023.113733
WoS: 000934262700001

Peste, E.-A. (2023). Efficiency and generalization of sparse neural networks. Institute of Science and Technology Austria.https://doi.org/10.15479/at:ista:13074 
LibreCat: https://research-explorer.ista.ac.at/record/13074
DOI: 10.15479/at:ista:13074

Aksenov, V., Alistarh, D.-A., Drozdova, A., &; Mohtashami, A. (2023). The splay-list: A distribution-adaptive concurrent skip-list. Distributed Computing. Springer Nature.https://doi.org/10.1007/s00446-022-00441-x 
LibreCat: https://research-explorer.ista.ac.at/record/12330
DOI: 10.1007/s00446-022-00441-x
WoS: 000913424000001
arXiv: 2008.01009

Markov, I., Vladu, A., Guo, Q., &; Alistarh, D.-A. (2023). Quantized distributed training of large models with convergence guarantees. In Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 24020–24044). Honolulu, Hawaii, HI, United States: ML Research Press.
LibreCat: https://research-explorer.ista.ac.at/record/14461
arXiv: 2302.02390

Shevchenko, A., Kögler, K., Hassani, H., &; Mondelli, M. (2023). Fundamental limits of two-layer autoencoders, and achieving them with gradient methods. In Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 31151–31209). Honolulu, Hawaii, HI, United States: ML Research Press.
LibreCat: https://research-explorer.ista.ac.at/record/14459
arXiv: 2212.13468

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.
LibreCat: https://research-explorer.ista.ac.at/record/14460
arXiv: 2302.04852

Frantar, E., &; Alistarh, D.-A. (2023). SparseGPT: Massive language models can be accurately pruned in one-shot. In Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 10323–10337). Honolulu, Hawaii, HI, United States: ML Research Press.
LibreCat: https://research-explorer.ista.ac.at/record/14458
arXiv: 2301.00774

Alistarh, D.-A., Aspnes, J., Ellen, F., Gelashvili, R., &; Zhu, L. (2023). Why extension-based proofs fail. SIAM Journal on Computing. Society for Industrial and Applied Mathematics.https://doi.org/10.1137/20M1375851 
LibreCat: https://research-explorer.ista.ac.at/record/14364
DOI: 10.1137/20M1375851
WoS: 001082972300004
arXiv: 1811.01421

Iofinova, E. B., Peste, E.-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 
LibreCat: https://research-explorer.ista.ac.at/record/14771
DOI: 10.1109/cvpr52729.2023.02334
WoS: 001062531308068
arXiv: 2304.12622

Beznosikov, A., Horvath, S., Richtarik, P., &; Safaryan, M. (2023). On biased compression for distributed learning. Journal of Machine Learning Research. Journal of Machine Learning Research.
LibreCat: https://research-explorer.ista.ac.at/record/14815
WoS: 001111578500001
arXiv: 2002.12410

Koval, N., Fedorov, A., Sokolova, M., Tsitelov, D., &; Alistarh, D.-A. (2023). Lincheck: A practical framework for testing concurrent data structures on JVM. In 35th International Conference on Computer Aided Verification  (Vol. 13964, pp. 156–169). Paris, France: Springer Nature.https://doi.org/10.1007/978-3-031-37706-8_8 
LibreCat: https://research-explorer.ista.ac.at/record/14260
DOI: 10.1007/978-3-031-37706-8_8

Koval, N., Fedorov, A., Sokolova, M., Tsitelov, D., &; Alistarh, D.-A. (2023). Lincheck: A practical framework for testing concurrent data structures on JVM. Zenodo.https://doi.org/10.5281/ZENODO.7877757 
LibreCat: https://research-explorer.ista.ac.at/record/14995
DOI: 10.5281/ZENODO.7877757

Fedorov, A., Hashemi, D., Nadiradze, G., &; Alistarh, D.-A. (2023). Provably-efficient and internally-deterministic parallel Union-Find. In Proceedings of the 35th ACM Symposium on Parallelism in Algorithms and Architectures (pp. 261–271). Orlando, FL, United States: Association for Computing Machinery.https://doi.org/10.1145/3558481.3591082 
LibreCat: https://research-explorer.ista.ac.at/record/13262
DOI: 10.1145/3558481.3591082
arXiv: 2304.09331

Safaryan, M., Krumes, A., &; Alistarh, D.-A. (2023). Knowledge distillation performs partial variance reduction. In 36th Conference on Neural Information Processing Systems (Vol. 36). New Orleans, LA, United States.
LibreCat: https://research-explorer.ista.ac.at/record/15363
arXiv: 2305.17581

Alistarh, D.-A., Gelashvili, R., &; Rybicki, J. (2022). Fast graphical population protocols. In Q. Bramas, V. Gramoli, &; A. Milani (Eds.), 25th International Conference on Principles of Distributed Systems (Vol. 217). Strasbourg, France: Schloss Dagstuhl - Leibniz-Zentrum für Informatik.https://doi.org/10.4230/LIPIcs.OPODIS.2021.14 
LibreCat: https://research-explorer.ista.ac.at/record/11184
DOI: 10.4230/LIPIcs.OPODIS.2021.14
arXiv: 2102.08808