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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. Proceedings of Machine Learning Research. CPAL: Conference on Parsimony and Learning, PMLR, vol. 234, 542–553.
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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. Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. PPoPP: Sympopsium on Principles and Practice of Parallel Programming, 107–118.
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| arXiv
2023 | Conference Poster | IST-REx-ID: 12736 |
Aksenov V, Brown TA, Fedorov A, Kokorin I. 2023. Unexpected scaling in path copying trees, Association for Computing Machinery,p.
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2023 | Conference Paper | IST-REx-ID: 13053 |
Peste E-A, Vladu A, Kurtic E, Lampert C, Alistarh D-A. CrAM: A Compression-Aware Minimizer. 11th International Conference on Learning Representations . ICLR: International Conference on Learning Representations.
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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. 7, 116.
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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. 948(2), 113733.
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| WoS
2023 | Thesis | IST-REx-ID: 13074 |
Peste E-A. 2023. Efficiency and generalization of sparse neural networks. Institute of Science and Technology Austria.
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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. 36, 395–418.
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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. Proceedings of the 40th International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 202, 24020–24044.
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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. Proceedings of the 40th International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 202, 31151–31209.
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| arXiv