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164 Publications


2023 | Published | Conference Paper | IST-REx-ID: 14460 | OA
Nikdan, M., Pegolotti, T., Iofinova, E. B., Kurtic, E., & Alistarh, D.-A. (2023). 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, pp. 26215–26227). Honolulu, Hawaii, HI, United States: ML Research Press.
[Preprint] View | Download Preprint (ext.) | arXiv
 

2023 | Published | Conference Paper | IST-REx-ID: 14461 | OA
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.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 

2023 | Published | Journal Article | IST-REx-ID: 12330 | OA
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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2023 | Published | Journal Article | IST-REx-ID: 12566 | OA
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
[Published Version] View | Files available | DOI | WoS
 

2023 | Published | Conference Paper | IST-REx-ID: 12735 | OA
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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2023 | Published | Conference Poster | IST-REx-ID: 12736 | OA
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 | Published | Conference Paper | IST-REx-ID: 13053 | OA
Krumes, A., Vladu, A., Kurtic, E., Lampert, C., & Alistarh, D.-A. (2023). CrAM: A Compression-Aware Minimizer. In 11th International Conference on Learning Representations . Kigali, Rwanda : OpenReview.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 

2023 | Published | Thesis | PhD | IST-REx-ID: 13074 | OA
Krumes, A. (2023). Efficiency and generalization of sparse neural networks. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:13074
[Published Version] View | Files available | DOI
 

2023 | Published | Journal Article | IST-REx-ID: 13179 | OA
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 | Published | Conference Paper | IST-REx-ID: 13262 | OA
Fedorov, A., Hashemi, D., Nadiradze, G., & Alistarh, D.-A. (2023). Provably-efficient and internally-deterministic parallel Union-Find. In Proceedings of the 35th ACM Symposium on Parallelism in Algorithms and Architectures (pp. 261–271). Orlando, FL, United States: Association for Computing Machinery. https://doi.org/10.1145/3558481.3591082
[Published Version] View | Files available | DOI | WoS | arXiv
 

2023 | Published | Book Chapter | IST-REx-ID: 19983 | OA
Balliu, A., Korhonen, J., Kuhn, F., Lievonen, H., Olivetti, D., Pai, S., … Uitto, J. (2023). Sinkless Orientation Made Simple. In Symposium on Simplicity in Algorithms (pp. 175–191). Florence, Italy: 2023 Society for Industrial and Applied Mathematics. https://doi.org/10.1137/1.9781611977585.ch17
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2023 | Published | Conference Paper | IST-REx-ID: 14771 | OA
Iofinova, E. B., Krumes, A., & Alistarh, D.-A. (2023). Bias in pruned vision models: In-depth analysis and countermeasures. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 24364–24373). Vancouver, BC, Canada: IEEE. https://doi.org/10.1109/cvpr52729.2023.02334
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 

2023 | Published | Journal Article | IST-REx-ID: 14815 | OA
Beznosikov, A., Horvath, S., Richtarik, P., & Safaryan, M. (2023). On biased compression for distributed learning. Journal of Machine Learning Research. Journal of Machine Learning Research.
[Published Version] View | Files available | WoS | arXiv
 

2023 | Research Data Reference | IST-REx-ID: 14995 | OA
Koval, N., Fedorov, A., Sokolova, M., Tsitelov, D., & Alistarh, D.-A. (2023). Lincheck: A practical framework for testing concurrent data structures on JVM. Zenodo. https://doi.org/10.5281/ZENODO.7877757
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2023 | Published | Conference Paper | IST-REx-ID: 15363 | OA
Safaryan, M., Krumes, A., & Alistarh, D.-A. (2023). Knowledge distillation performs partial variance reduction. In 36th Conference on Neural Information Processing Systems (Vol. 36). New Orleans, LA, United States.
[Published Version] View | Files available | arXiv
 

2023 | Published | Conference Paper | IST-REx-ID: 17378 | OA
Frantar, E., Ashkboos, S., Hoefler, T., & Alistarh, D.-A. (2023). OPTQ: Accurate post-training quantization for generative pre-trained transformers. In 11th International Conference on Learning Representations . Kigali, Rwanda: International Conference on Learning Representations.
[Published Version] View | Files available
 

2023 | Published | Conference Paper | IST-REx-ID: 14459 | OA
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.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 

2022 | Published | Conference Paper | IST-REx-ID: 11707 | OA
Balliu, A., Hirvonen, J., Melnyk, D., Olivetti, D., Rybicki, J., & Suomela, J. (2022). Local mending. In M. Parter (Ed.), International Colloquium on Structural Information and Communication Complexity (Vol. 13298, pp. 1–20). Paderborn, Germany: Springer Nature. https://doi.org/10.1007/978-3-031-09993-9_1
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 

2022 | Published | Conference Paper | IST-REx-ID: 11844 | OA
Alistarh, D.-A., Rybicki, J., & Voitovych, S. (2022). Near-optimal leader election in population protocols on graphs. In Proceedings of the Annual ACM Symposium on Principles of Distributed Computing (pp. 246–256). Salerno, Italy: Association for Computing Machinery. https://doi.org/10.1145/3519270.3538435
[Published Version] View | Files available | DOI | WoS | arXiv
 

2022 | Published | Conference Paper | IST-REx-ID: 12182 | OA
Pacut, M., Parham, M., Rybicki, J., Schmid, S., Suomela, J., & Tereshchenko, A. (2022). Brief announcement: Temporal locality in online algorithms. In 36th International Symposium on Distributed Computing (Vol. 246). Augusta, GA, United States: Schloss Dagstuhl - Leibniz-Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.DISC.2022.52
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