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117 Publications


2024 | Conference Paper | IST-REx-ID: 15011 | OA
E. Kurtic, T. Hoefler, and D.-A. Alistarh, “How to prune your language model: Recovering accuracy on the ‘Sparsity May Cry’ benchmark,” in Proceedings of Machine Learning Research, Hongkong, China, 2024, vol. 234, pp. 542–553.
[Preprint] View | Download Preprint (ext.) | arXiv
 

2023 | Conference Paper | IST-REx-ID: 12735 | OA
N. Koval, D.-A. Alistarh, and R. Elizarov, “Fast and scalable channels in Kotlin Coroutines,” in Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, Montreal, QC, Canada, 2023, pp. 107–118.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 

2023 | Conference Poster | IST-REx-ID: 12736 | OA
V. Aksenov, T. A. Brown, A. Fedorov, and I. Kokorin, Unexpected scaling in path copying trees. Association for Computing Machinery, 2023, pp. 438–440.
[Published Version] View | DOI | Download Published Version (ext.)
 

2023 | Conference Paper | IST-REx-ID: 13053 | OA
E.-A. Peste, A. Vladu, E. Kurtic, C. Lampert, and D.-A. Alistarh, “CrAM: A Compression-Aware Minimizer,” in 11th International Conference on Learning Representations , Kigali, Rwanda .
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 

2023 | Journal Article | IST-REx-ID: 13179 | OA
N. Koval, D. Khalanskiy, and D.-A. Alistarh, “CQS: A formally-verified framework for fair and abortable synchronization,” Proceedings of the ACM on Programming Languages, vol. 7. Association for Computing Machinery , 2023.
[Published Version] View | Files available | DOI
 

2023 | Conference Paper | IST-REx-ID: 13262 | OA
A. Fedorov, D. Hashemi, G. Nadiradze, and D.-A. Alistarh, “Provably-efficient and internally-deterministic parallel Union-Find,” in Proceedings of the 35th ACM Symposium on Parallelism in Algorithms and Architectures, Orlando, FL, United States, 2023, pp. 261–271.
[Published Version] View | Files available | DOI | arXiv
 

2023 | Journal Article | IST-REx-ID: 12566 | OA
D.-A. Alistarh, F. Ellen, and J. Rybicki, “Wait-free approximate agreement on graphs,” Theoretical Computer Science, vol. 948, no. 2. Elsevier, 2023.
[Published Version] View | Files available | DOI | WoS
 

2023 | Thesis | IST-REx-ID: 13074 | OA
E.-A. Peste, “Efficiency and generalization of sparse neural networks,” Institute of Science and Technology Austria, 2023.
[Published Version] View | Files available | DOI
 

2023 | Journal Article | IST-REx-ID: 12330 | OA
V. Aksenov, D.-A. Alistarh, A. Drozdova, and A. Mohtashami, “The splay-list: A distribution-adaptive concurrent skip-list,” Distributed Computing, vol. 36. Springer Nature, pp. 395–418, 2023.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 

2023 | Conference Paper | IST-REx-ID: 14461 | OA
I. Markov, A. Vladu, Q. Guo, and D.-A. Alistarh, “Quantized distributed training of large models with convergence guarantees,” in Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, HI, United States, 2023, vol. 202, pp. 24020–24044.
[Preprint] View | Download Preprint (ext.) | arXiv
 

2023 | Conference Paper | IST-REx-ID: 14459 | OA
A. Shevchenko, K. Kögler, H. Hassani, and M. Mondelli, “Fundamental limits of two-layer autoencoders, and achieving them with gradient methods,” in Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, HI, United States, 2023, vol. 202, pp. 31151–31209.
[Preprint] View | Download Preprint (ext.) | arXiv
 

2023 | Conference Paper | IST-REx-ID: 14460 | OA
M. Nikdan, T. Pegolotti, E. B. Iofinova, E. Kurtic, and D.-A. Alistarh, “SparseProp: Efficient sparse backpropagation for faster training of neural networks at the edge,” in Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, HI, United States, 2023, vol. 202, pp. 26215–26227.
[Preprint] View | Download Preprint (ext.) | arXiv
 

2023 | Conference Paper | IST-REx-ID: 14458 | OA
E. Frantar and D.-A. Alistarh, “SparseGPT: Massive language models can be accurately pruned in one-shot,” in Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, HI, United States, 2023, vol. 202, pp. 10323–10337.
[Preprint] View | Download Preprint (ext.) | arXiv
 

2023 | Journal Article | IST-REx-ID: 14364 | OA
D.-A. Alistarh, J. Aspnes, F. Ellen, R. Gelashvili, and L. Zhu, “Why extension-based proofs fail,” SIAM Journal on Computing, vol. 52, no. 4. Society for Industrial and Applied Mathematics, pp. 913–944, 2023.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 

2023 | Conference Paper | IST-REx-ID: 14771 | OA
E. B. Iofinova, E.-A. Peste, and D.-A. Alistarh, “Bias in pruned vision models: In-depth analysis and countermeasures,” in 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada, 2023, pp. 24364–24373.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 

2023 | Journal Article | IST-REx-ID: 14815 | OA
A. Beznosikov, S. Horvath, P. Richtarik, and M. Safaryan, “On biased compression for distributed learning,” Journal of Machine Learning Research, vol. 24. Journal of Machine Learning Research, pp. 1–50, 2023.
[Published Version] View | Files available | WoS | arXiv
 

2023 | Conference Paper | IST-REx-ID: 14260 | OA
N. Koval, A. Fedorov, M. Sokolova, D. Tsitelov, and D.-A. Alistarh, “Lincheck: A practical framework for testing concurrent data structures on JVM,” in 35th International Conference on Computer Aided Verification , Paris, France, 2023, vol. 13964, pp. 156–169.
[Published Version] View | Files available | DOI
 

2023 | Research Data Reference | IST-REx-ID: 14995 | OA
N. Koval, A. Fedorov, M. Sokolova, D. Tsitelov, and D.-A. Alistarh, “Lincheck: A practical framework for testing concurrent data structures on JVM.” Zenodo, 2023.
[Published Version] View | Files available | DOI | Download Published Version (ext.)
 

2022 | Conference Paper | IST-REx-ID: 11184 | OA
D.-A. Alistarh, R. Gelashvili, and J. Rybicki, “Fast graphical population protocols,” in 25th International Conference on Principles of Distributed Systems, Strasbourg, France, 2022, vol. 217.
[Published Version] View | Files available | DOI | arXiv
 

2022 | Conference Paper | IST-REx-ID: 11183 | OA
A. Nikabadi and J. Korhonen, “Beyond distributed subgraph detection: Induced subgraphs, multicolored problems and graph parameters,” in 25th International Conference on Principles of Distributed Systems, Strasbourg, France, 2022, vol. 217.
[Published Version] View | Files available | DOI
 

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