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164 Publications


2023 | Published | Conference Paper | IST-REx-ID: 14460 | OA
Nikdan, Mahdi, Tommaso Pegolotti, Eugenia B Iofinova, Eldar Kurtic, and Dan-Adrian Alistarh. “SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks at the Edge.” In Proceedings of the 40th International Conference on Machine Learning, 202:26215–27. ML Research Press, 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 

2023 | Published | Conference Paper | IST-REx-ID: 14461 | OA
Markov, Ilia, Adrian Vladu, Qi Guo, and Dan-Adrian Alistarh. “Quantized Distributed Training of Large Models with Convergence Guarantees.” In Proceedings of the 40th International Conference on Machine Learning, 202:24020–44. ML Research Press, 2023.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 

2023 | Published | Journal Article | IST-REx-ID: 12330 | OA
Aksenov, Vitalii, Dan-Adrian Alistarh, Alexandra Drozdova, and Amirkeivan Mohtashami. “The Splay-List: A Distribution-Adaptive Concurrent Skip-List.” Distributed Computing. Springer Nature, 2023. https://doi.org/10.1007/s00446-022-00441-x.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 

2023 | Published | Journal Article | IST-REx-ID: 12566 | OA
Alistarh, Dan-Adrian, Faith Ellen, and Joel Rybicki. “Wait-Free Approximate Agreement on Graphs.” Theoretical Computer Science. Elsevier, 2023. 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, Nikita, Dan-Adrian Alistarh, and Roman Elizarov. “Fast and Scalable Channels in Kotlin Coroutines.” In Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 107–18. Association for Computing Machinery, 2023. https://doi.org/10.1145/3572848.3577481.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 

2023 | Published | Conference Poster | IST-REx-ID: 12736 | OA
Aksenov, Vitaly, Trevor A Brown, Alexander Fedorov, and Ilya Kokorin. Unexpected Scaling in Path Copying Trees. Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. Association for Computing Machinery, 2023. https://doi.org/10.1145/3572848.3577512.
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2023 | Published | Conference Paper | IST-REx-ID: 13053 | OA
Krumes, Alexandra, Adrian Vladu, Eldar Kurtic, Christoph Lampert, and Dan-Adrian Alistarh. “CrAM: A Compression-Aware Minimizer.” In 11th International Conference on Learning Representations . OpenReview, 2023.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 

2023 | Published | Thesis | PhD | IST-REx-ID: 13074 | OA
Krumes, Alexandra. “Efficiency and Generalization of Sparse Neural Networks.” Institute of Science and Technology Austria, 2023. https://doi.org/10.15479/at:ista:13074.
[Published Version] View | Files available | DOI
 

2023 | Published | Journal Article | IST-REx-ID: 13179 | OA
Koval, Nikita, Dmitry Khalanskiy, and Dan-Adrian Alistarh. “CQS: A Formally-Verified Framework for Fair and Abortable Synchronization.” Proceedings of the ACM on Programming Languages. Association for Computing Machinery , 2023. https://doi.org/10.1145/3591230.
[Published Version] View | Files available | DOI
 

2023 | Published | Conference Paper | IST-REx-ID: 13262 | OA
Fedorov, Alexander, Diba Hashemi, Giorgi Nadiradze, and Dan-Adrian Alistarh. “Provably-Efficient and Internally-Deterministic Parallel Union-Find.” In Proceedings of the 35th ACM Symposium on Parallelism in Algorithms and Architectures, 261–71. Association for Computing Machinery, 2023. 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, Alkida, Janne Korhonen, Fabian Kuhn, Henrik Lievonen, Dennis Olivetti, Shreyas Pai, Ami Paz, et al. “Sinkless Orientation Made Simple.” In Symposium on Simplicity in Algorithms, 175–91. 2023 Society for Industrial and Applied Mathematics, 2023. https://doi.org/10.1137/1.9781611977585.ch17.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 

2023 | Published | Conference Paper | IST-REx-ID: 14771 | OA
Iofinova, Eugenia B, Alexandra Krumes, and Dan-Adrian Alistarh. “Bias in Pruned Vision Models: In-Depth Analysis and Countermeasures.” In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 24364–73. IEEE, 2023. 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, Aleksandr, Samuel Horvath, Peter Richtarik, and Mher Safaryan. “On Biased Compression for Distributed Learning.” Journal of Machine Learning Research. Journal of Machine Learning Research, 2023.
[Published Version] View | Files available | WoS | arXiv
 

2023 | Research Data Reference | IST-REx-ID: 14995 | OA
Koval, Nikita, Alexander Fedorov, Maria Sokolova, Dmitry Tsitelov, and Dan-Adrian Alistarh. “Lincheck: A Practical Framework for Testing Concurrent Data Structures on JVM.” Zenodo, 2023. https://doi.org/10.5281/ZENODO.7877757.
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2023 | Published | Conference Paper | IST-REx-ID: 15363 | OA
Safaryan, Mher, Alexandra Krumes, and Dan-Adrian Alistarh. “Knowledge Distillation Performs Partial Variance Reduction.” In 36th Conference on Neural Information Processing Systems, Vol. 36, 2023.
[Published Version] View | Files available | arXiv
 

2023 | Published | Conference Paper | IST-REx-ID: 17378 | OA
Frantar, Elias, Saleh Ashkboos, Torsten Hoefler, and Dan-Adrian Alistarh. “OPTQ: Accurate Post-Training Quantization for Generative Pre-Trained Transformers.” In 11th International Conference on Learning Representations . International Conference on Learning Representations, 2023.
[Published Version] View | Files available
 

2023 | Published | Conference Paper | IST-REx-ID: 14459 | OA
Shevchenko, Alexander, Kevin Kögler, Hamed Hassani, and Marco Mondelli. “Fundamental Limits of Two-Layer Autoencoders, and Achieving Them with Gradient Methods.” In Proceedings of the 40th International Conference on Machine Learning, 202:31151–209. ML Research Press, 2023.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 

2022 | Published | Conference Paper | IST-REx-ID: 11707 | OA
Balliu, Alkida, Juho Hirvonen, Darya Melnyk, Dennis Olivetti, Joel Rybicki, and Jukka Suomela. “Local Mending.” In International Colloquium on Structural Information and Communication Complexity, edited by Merav Parter, 13298:1–20. LNCS. Springer Nature, 2022. 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, Dan-Adrian, Joel Rybicki, and Sasha Voitovych. “Near-Optimal Leader Election in Population Protocols on Graphs.” In Proceedings of the Annual ACM Symposium on Principles of Distributed Computing, 246–56. Association for Computing Machinery, 2022. 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, Maciej, Mahmoud Parham, Joel Rybicki, Stefan Schmid, Jukka Suomela, and Aleksandr Tereshchenko. “Brief Announcement: Temporal Locality in Online Algorithms.” In 36th International Symposium on Distributed Computing, Vol. 246. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2022. https://doi.org/10.4230/LIPIcs.DISC.2022.52.
[Published Version] View | Files available | DOI
 

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