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118 Publications
2024 | Conference Paper | IST-REx-ID: 15011 | 
Kurtic, Eldar, et al. “How to Prune Your Language Model: Recovering Accuracy on the ‘Sparsity May Cry’ Benchmark.” Proceedings of Machine Learning Research, vol. 234, ML Research Press, 2024, pp. 542–53.
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2023 | Conference Paper | IST-REx-ID: 12735 |
Koval, Nikita, et al. “Fast and Scalable Channels in Kotlin Coroutines.” Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, Association for Computing Machinery, 2023, pp. 107–18, doi:10.1145/3572848.3577481.
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2023 | Conference Poster | IST-REx-ID: 12736 |
Aksenov, Vitaly, et al. “Unexpected Scaling in Path Copying Trees.” Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, Association for Computing Machinery, 2023, pp. 438–40, doi:10.1145/3572848.3577512.
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2023 | Conference Paper | IST-REx-ID: 13053 |
Peste, Elena-Alexandra, et al. “CrAM: A Compression-Aware Minimizer.” 11th International Conference on Learning Representations .
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2023 | Journal Article | IST-REx-ID: 13179 |
Koval, Nikita, et al. “CQS: A Formally-Verified Framework for Fair and Abortable Synchronization.” Proceedings of the ACM on Programming Languages, vol. 7, 116, Association for Computing Machinery , 2023, doi:10.1145/3591230.
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2023 | Journal Article | IST-REx-ID: 12566 |
Alistarh, Dan-Adrian, et al. “Wait-Free Approximate Agreement on Graphs.” Theoretical Computer Science, vol. 948, no. 2, 113733, Elsevier, 2023, doi:10.1016/j.tcs.2023.113733.
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2023 | Thesis | IST-REx-ID: 13074 |
Peste, Elena-Alexandra. Efficiency and Generalization of Sparse Neural Networks. Institute of Science and Technology Austria, 2023, doi:10.15479/at:ista:13074.
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2023 | Journal Article | IST-REx-ID: 12330 |
Aksenov, Vitalii, et al. “The Splay-List: A Distribution-Adaptive Concurrent Skip-List.” Distributed Computing, vol. 36, Springer Nature, 2023, pp. 395–418, doi:10.1007/s00446-022-00441-x.
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2023 | Conference Paper | IST-REx-ID: 14461 |
Markov, Ilia, et al. “Quantized Distributed Training of Large Models with Convergence Guarantees.” Proceedings of the 40th International Conference on Machine Learning, vol. 202, ML Research Press, 2023, pp. 24020–44.
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2023 | Conference Paper | IST-REx-ID: 14459 |
Shevchenko, Aleksandr, et al. “Fundamental Limits of Two-Layer Autoencoders, and Achieving Them with Gradient Methods.” Proceedings of the 40th International Conference on Machine Learning, vol. 202, ML Research Press, 2023, pp. 31151–209.
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