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156 Publications
2023 | Published | Conference Paper | IST-REx-ID: 14458 | 
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.
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| arXiv
2023 | Published | Journal Article | IST-REx-ID: 12566 |
D.-A. Alistarh, F. Ellen, and J. Rybicki, “Wait-free approximate agreement on graphs,” Theoretical Computer Science, vol. 948, no. 2. Elsevier, 2023.
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| WoS
2023 | Published | Conference Paper | IST-REx-ID: 13053 |
A. Krumes, A. Vladu, E. Kurtic, C. Lampert, and D.-A. Alistarh, “CrAM: A Compression-Aware Minimizer,” in 11th International Conference on Learning Representations , Kigali, Rwanda , 2023.
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| arXiv
2023 | Published | Conference Paper | IST-REx-ID: 15363 |
M. Safaryan, A. Krumes, and D.-A. Alistarh, “Knowledge distillation performs partial variance reduction,” in 36th Conference on Neural Information Processing Systems, New Orleans, LA, United States, 2023, vol. 36.
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| arXiv
2023 | Published | Thesis | IST-REx-ID: 13074 |
A. Krumes, “Efficiency and generalization of sparse neural networks,” Institute of Science and Technology Austria, 2023.
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2023 | Published | Conference Paper | IST-REx-ID: 14771 |
E. B. Iofinova, A. Krumes, 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.
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| arXiv
2023 | Published | Conference Paper | IST-REx-ID: 14461 |
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.
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| arXiv
2023 | Published | Journal Article | IST-REx-ID: 14364 |
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.
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| arXiv
2023 | Published | Conference Paper | IST-REx-ID: 14459 |
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.
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| arXiv
2022 | Published | Conference Paper | IST-REx-ID: 11181 |
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.
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2022 | Published | Conference Paper | IST-REx-ID: 17088 |
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.
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| arXiv
2022 | Published | Conference Paper | IST-REx-ID: 12182 |
M. Pacut, M. Parham, J. Rybicki, S. Schmid, J. Suomela, and A. Tereshchenko, “Brief announcement: Temporal locality in online algorithms,” in 36th International Symposium on Distributed Computing, Augusta, GA, United States, 2022, vol. 246.
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2022 | Published | Conference Paper | IST-REx-ID: 11707 |
A. Balliu, J. Hirvonen, D. Melnyk, D. Olivetti, J. Rybicki, and J. Suomela, “Local mending,” in International Colloquium on Structural Information and Communication Complexity, Paderborn, Germany, 2022, vol. 13298, pp. 1–20.
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| arXiv
2022 | Published | Conference Paper | IST-REx-ID: 11183 |
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.
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2022 | Published | Conference Paper | IST-REx-ID: 11180 |
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.
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| arXiv
2022 | Research Data Reference | IST-REx-ID: 13076 |
A. Postnikova, N. Koval, G. Nadiradze, and D.-A. Alistarh, “Multi-queues can be state-of-the-art priority schedulers.” Zenodo, 2022.
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2022 | Published | Conference Paper | IST-REx-ID: 17087 |
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.
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| arXiv
2022 | Published | Conference Paper | IST-REx-ID: 17059 |
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.
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2022 | Published | Conference Paper | IST-REx-ID: 11184 |
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.
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| arXiv
2022 | Published | Conference Paper | IST-REx-ID: 12299 |
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.
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