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Alexander Fedorov

Sergei Pankratov

Soroush Tabesh

Eldar Kurtic

Jiale Chen

Vage Egiazarian

Mahdi Nikdan

Erik Schultheis

Ionut-Vlad Modoranu

Alexandra Volkova

Andrei Panferov

Dan Alistarh

Elias Frantar

Joel Rybicki

Peter Davies

Sidak Singh

Alexandra Krumes

Roberto Lopez Castro

Mher Safaryan

Janne Korhonen

Sharareh Alipour

Trevor Brown

Ilia Markov

Martin Töpfer

Bapi Chatterjee

Vitalii Aksenov

Sokolova, Mariia

Elvir Crncevic

Giorgi Nadiradze



164 Publications

2022 | Published | Conference Paper | IST-REx-ID: 12299 | OA
E. B. Iofinova, A. Krumes, M. Kurtz, and D.-A. Alistarh, “How well do sparse ImageNet models transfer?,” in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, United States, 2022, pp. 12256–12266.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
2022 | Published | Conference Paper | IST-REx-ID: 12780 | OA
I. Markov, H. Ramezanikebrya, and D.-A. Alistarh, “CGX: Adaptive system support for communication-efficient deep learning,” in Proceedings of the 23rd ACM/IFIP International Middleware Conference, Quebec, QC, Canada, 2022, pp. 241–254.
[Published Version] View | Files available | DOI | WoS | arXiv
 
2022 | Research Data Reference | IST-REx-ID: 13076 | OA
A. Postnikova, N. Koval, G. Nadiradze, and D.-A. Alistarh, “Multi-queues can be state-of-the-art priority schedulers.” Zenodo, 2022.
[Published Version] View | Files available | DOI | Download Published Version (ext.)
 
2022 | Published | Conference Paper | IST-REx-ID: 11180 | OA
A. Postnikova, N. Koval, G. Nadiradze, and D.-A. Alistarh, “Multi-queues can be state-of-the-art priority schedulers,” in Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, Seoul, Republic of Korea, 2022, pp. 353–367.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
2022 | Published | Conference Paper | IST-REx-ID: 11181 | OA
T. A. Brown, W. Sigouin, and D.-A. Alistarh, “PathCAS: An efficient middle ground for concurrent search data structures,” in Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, Seoul, Republic of Korea, 2022, pp. 385–399.
[Published Version] View | Files available | DOI | WoS
 
2022 | Published | 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
 
2022 | Published | 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 | Published | Conference Paper | IST-REx-ID: 17059 | OA
E. Frantar and D.-A. Alistarh, “SPDY: Accurate pruning with speedup guarantees,” in 39th International Conference on Machine Learning, Baltimore, MD, United States, 2022, vol. 162, pp. 6726–6743.
[Published Version] View | Files available | WoS
 
2022 | Published | Conference Paper | IST-REx-ID: 17087 | OA
E. Frantar, S. P. Singh, and D.-A. Alistarh, “Optimal brain compression: A framework for accurate post-training quantization and pruning,” in 36th Conference on Neural Information Processing Systems, New Orleans, LA, United States, 2022, vol. 35.
[Submitted Version] View | Files available | arXiv
 
2022 | Published | Conference Paper | IST-REx-ID: 17088 | OA
E. Kurtic et al., “The optimal BERT surgeon: Scalable and accurate second-order pruning for large language models,” in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Abu Dhabi, United Arab Emirates, 2022, pp. 4163–4181.
[Published Version] View | Files available | DOI | arXiv
 
2022 | Published | Journal Article | IST-REx-ID: 8286 | OA
D.-A. Alistarh, G. Nadiradze, and A. Sabour, “Dynamic averaging load balancing on cycles,” Algorithmica, vol. 84, no. 4. Springer Nature, pp. 1007–1029, 2022.
[Published Version] View | Files available | DOI | WoS | arXiv
 
2022 | Published | Journal Article | IST-REx-ID: 11420 | OA
A. Shevchenko, V. Kungurtsev, and M. Mondelli, “Mean-field analysis of piecewise linear solutions for wide ReLU networks,” Journal of Machine Learning Research, vol. 23, no. 130. Journal of Machine Learning Research, pp. 1–55, 2022.
[Published Version] View | Files available | arXiv
 
2021 | Published | Conference Paper | IST-REx-ID: 11452 | OA
F. Alimisis, P. Davies, B. Vandereycken, and D.-A. Alistarh, “Distributed principal component analysis with limited communication,” in Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems, Virtual, Online, 2021, vol. 4, pp. 2823–2834.
[Published Version] View | Download Published Version (ext.) | arXiv
 
2021 | Published | Conference Paper | IST-REx-ID: 11458 | OA
A. Krumes, E. B. Iofinova, A. Vladu, and D.-A. Alistarh, “AC/DC: Alternating Compressed/DeCompressed training of deep neural networks,” in 35th Conference on Neural Information Processing Systems, Virtual, Online, 2021, vol. 34, pp. 8557–8570.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
2021 | Published | Conference Paper | IST-REx-ID: 11463 | OA
E. Frantar, E. Kurtic, and D.-A. Alistarh, “M-FAC: Efficient matrix-free approximations of second-order information,” in 35th Conference on Neural Information Processing Systems, Virtual, Online, 2021, vol. 34, pp. 14873–14886.
[Published Version] View | Download Published Version (ext.) | arXiv
 
2021 | Published | Conference Paper | IST-REx-ID: 11464 | OA
D.-A. Alistarh and J. Korhonen, “Towards tight communication lower bounds for distributed optimisation,” in 35th Conference on Neural Information Processing Systems, Virtual, Online, 2021, vol. 34, pp. 7254–7266.
[Published Version] View | Download Published Version (ext.) | arXiv
 
2021 | Published | Conference Paper | IST-REx-ID: 13147 | OA
F. Alimisis, P. Davies, and D.-A. Alistarh, “Communication-efficient distributed optimization with quantized preconditioners,” in Proceedings of the 38th International Conference on Machine Learning, Virtual, 2021, vol. 139, pp. 196–206.
[Published Version] View | Files available | arXiv
 
2021 | Published | Conference Paper | IST-REx-ID: 15263 | OA
F. Alimisis, A. Orvieto, G. Becigneul, and A. Lucchi, “Momentum improves optimization on Riemannian manifolds,” in Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, San Diego, CA, United States; Virtual, 2021, vol. 130, pp. 1351–1359.
[Published Version] View | Download Published Version (ext.) | arXiv
 
2021 | Published | Journal Article | IST-REx-ID: 15267 | OA
A. Czumaj and P. Davies, “Exploiting spontaneous transmissions for broadcasting and leader election in radio networks,” Journal of the ACM, vol. 68, no. 2. Association for Computing Machinery, 2021.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
2021 | Published | Journal Article | IST-REx-ID: 15271
A. Czumaj, P. Davies, and M. Parter, “Simple, deterministic, constant-round coloring in congested clique and MPC,” SIAM Journal on Computing, vol. 50, no. 5. Society for Industrial and Applied Mathematics, pp. 1603–1626, 2021.
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