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

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

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

Koval N, Khalanskiy D, Alistarh D-A. CQS: A formally-verified framework for fair and abortable synchronization. Proceedings of the ACM on Programming Languages. 2023;7. doi:10.1145/3591230
LibreCat: https://research-explorer.ista.ac.at/record/13179
DOI: 10.1145/3591230

Alistarh D-A, Ellen F, Rybicki J. Wait-free approximate agreement on graphs. Theoretical Computer Science. 2023;948(2). doi:10.1016/j.tcs.2023.113733
LibreCat: https://research-explorer.ista.ac.at/record/12566
DOI: 10.1016/j.tcs.2023.113733
WoS: 000934262700001

Aksenov V, Alistarh D-A, Drozdova A, Mohtashami A. The splay-list: A distribution-adaptive concurrent skip-list. Distributed Computing. 2023;36:395-418. doi: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. Quantized distributed training of large models with convergence guarantees. In: Proceedings of the 40th International Conference on Machine Learning. Vol 202. ML Research Press; 2023:24020-24044.
LibreCat: https://research-explorer.ista.ac.at/record/14461
arXiv: 2302.02390

Nikdan M, Pegolotti T, Iofinova EB, Kurtic E, Alistarh D-A. 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. ML Research Press; 2023:26215-26227.
LibreCat: https://research-explorer.ista.ac.at/record/14460
arXiv: 2302.04852

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

Alistarh D-A, Aspnes J, Ellen F, Gelashvili R, Zhu L. Why extension-based proofs fail. SIAM Journal on Computing. 2023;52(4):913-944. doi:10.1137/20M1375851
LibreCat: https://research-explorer.ista.ac.at/record/14364
DOI: 10.1137/20M1375851
WoS: 001082972300004
arXiv: 1811.01421

Iofinova EB, Peste E-A, Alistarh D-A. Bias in pruned vision models: In-depth analysis and countermeasures. In: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE; 2023:24364-24373. doi: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

Koval N, Fedorov A, Sokolova M, Tsitelov D, Alistarh D-A. Lincheck: A practical framework for testing concurrent data structures on JVM. In: 35th International Conference on Computer Aided Verification . Vol 13964. Springer Nature; 2023:156-169. doi: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. Lincheck: A practical framework for testing concurrent data structures on JVM. 2023. doi: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. Provably-efficient and internally-deterministic parallel Union-Find. In: Proceedings of the 35th ACM Symposium on Parallelism in Algorithms and Architectures. Association for Computing Machinery; 2023:261-271. doi:10.1145/3558481.3591082
LibreCat: https://research-explorer.ista.ac.at/record/13262
DOI: 10.1145/3558481.3591082
arXiv: 2304.09331

Alistarh D-A, Gelashvili R, Rybicki J. Fast graphical population protocols. In: Bramas Q, Gramoli V, Milani A, eds. 25th International Conference on Principles of Distributed Systems. Vol 217. Schloss Dagstuhl - Leibniz-Zentrum für Informatik; 2022. doi: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

Markov I, Ramezanikebrya H, Alistarh D-A. CGX: Adaptive system support for communication-efficient deep learning. In: Proceedings of the 23rd ACM/IFIP International Middleware Conference. Association for Computing Machinery; 2022:241-254. doi:10.1145/3528535.3565248
LibreCat: https://research-explorer.ista.ac.at/record/12780
DOI: 10.1145/3528535.3565248
arXiv: 2111.08617

Alistarh D-A, Rybicki J, Voitovych S. Near-optimal leader election in population protocols on graphs. In: Proceedings of the Annual ACM Symposium on Principles of Distributed Computing. Association for Computing Machinery; 2022:246-256. doi:10.1145/3519270.3538435
LibreCat: https://research-explorer.ista.ac.at/record/11844
DOI: 10.1145/3519270.3538435
arXiv: 2205.12597

Brown TA, Sigouin W, Alistarh D-A. PathCAS: An efficient middle ground for concurrent search data structures. In: Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. Association for Computing Machinery; 2022:385-399. doi:10.1145/3503221.3508410
LibreCat: https://research-explorer.ista.ac.at/record/11181
DOI: 10.1145/3503221.3508410
WoS: 000883318200027

Postnikova A, Koval N, Nadiradze G, Alistarh D-A. Multi-queues can be state-of-the-art priority schedulers. In: Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. Association for Computing Machinery; 2022:353-367. doi:10.1145/3503221.3508432
LibreCat: https://research-explorer.ista.ac.at/record/11180
DOI: 10.1145/3503221.3508432
WoS: 000883318200025
arXiv: 2109.00657

Postnikova A, Koval N, Nadiradze G, Alistarh D-A. Multi-queues can be state-of-the-art priority schedulers. 2022. doi:10.5281/ZENODO.5733408
LibreCat: https://research-explorer.ista.ac.at/record/13076
DOI: 10.5281/ZENODO.5733408