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118 Publications
2024 | Conference Paper | IST-REx-ID: 15011 | 
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.
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| arXiv
2023 | Conference Paper | IST-REx-ID: 12735 |
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
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| arXiv
2023 | Conference Poster | IST-REx-ID: 12736 |
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
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2023 | Conference Paper | IST-REx-ID: 13053 |
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 .
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| arXiv
2023 | Journal Article | IST-REx-ID: 13179 |
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
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2023 | Journal Article | IST-REx-ID: 12566 |
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
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2023 | Thesis | IST-REx-ID: 13074 |
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
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2023 | Journal Article | IST-REx-ID: 12330 |
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
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| arXiv
2023 | Conference Paper | IST-REx-ID: 14461 |
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.
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| arXiv
2023 | Conference Paper | IST-REx-ID: 14459 |
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.
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| arXiv