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144 Publications


2024 | Published | Conference Paper | IST-REx-ID: 18975 | OA
Modoranu, I.-V., Kalinov, A., Kurtic, E., Frantar, E., & Alistarh, D.-A. (2024). Error feedback can accurately compress preconditioners. In 41st International Conference on Machine Learning (Vol. 235, pp. 35910–35933). Vienna, Austria: ML Research Press.
[Preprint] View | Download Preprint (ext.) | arXiv
 

2024 | Published | Conference Paper | IST-REx-ID: 18976 | OA
Islamov, R., Safaryan, M., & Alistarh, D.-A. (2024). AsGrad: A sharp unified analysis of asynchronous-SGD algorithms. In Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (Vol. 238, pp. 649–657). Valencia, Spain: ML Research Press.
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2024 | Published | Conference Paper | IST-REx-ID: 18977 | OA
Dettmers, T., Svirschevski, R. A., Egiazarian, V., Kuznedelev, D., Frantar, E., Ashkboos, S., … Alistarh, D.-A. (2024). SpQR: A sparse-quantized representation for near-lossless LLM weight compression. In 12th International Conference on Learning Representations. Vienna, Austria: OpenReview.
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2024 | Published | Conference Paper | IST-REx-ID: 17093 | OA
Zakerinia, H., Talaei, S., Nadiradze, G., & Alistarh, D.-A. (2024). Communication-efficient federated learning with data and client heterogeneity. In Proceedings of the 27th International Conference on Artificial Intelligence and Statistics (Vol. 238, pp. 3448–3456). Valencia, Spain: ML Research Press.
[Preprint] View | Download Preprint (ext.) | arXiv
 

2024 | Published | Conference Paper | IST-REx-ID: 17329 | OA
Alistarh, D.-A., Chatterjee, K., Karrabi, M., & Lazarsfeld, J. M. (2024). Game dynamics and equilibrium computation in the population protocol model. In Proceedings of the 43rd Annual ACM Symposium on Principles of Distributed Computing (pp. 40–49). Nantes, France: Association for Computing Machinery. https://doi.org/10.1145/3662158.3662768
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2024 | Published | Conference Paper | IST-REx-ID: 17332 | OA
Kokorin, I., Yudov, V., Aksenov, V., & Alistarh, D.-A. (2024). Wait-free trees with asymptotically-efficient range queries. In 2024 IEEE International Parallel and Distributed Processing Symposium (pp. 169–179). San Francisco, CA, United States: IEEE. https://doi.org/10.1109/IPDPS57955.2024.00023
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2024 | Published | Conference Paper | IST-REx-ID: 17456 | OA
Markov, I., Alimohammadi, K., Frantar, E., & Alistarh, D.-A. (2024). L-GreCo: Layerwise-adaptive gradient compression for efficient data-parallel deep learning. In P. Gibbons, G. Pekhimenko, & C. De Sa (Eds.), Proceedings of Machine Learning and Systems (Vol. 6). Athens, Greece: Association for Computing Machinery.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 

2024 | Published | Thesis | IST-REx-ID: 17485 | OA
Frantar, E. (2024). Compressing large neural networks : Algorithms, systems and scaling laws. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:17485
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2024 | Published | Thesis | IST-REx-ID: 17490 | OA
Markov, I. (2024). Communication-efficient distributed training of deep neural networks: An algorithms and systems perspective. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:17490
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2024 | Published | Conference Paper | IST-REx-ID: 15011 | OA
Kurtic, E., Hoefler, T., & Alistarh, D.-A. (2024). How to prune your language model: Recovering accuracy on the “Sparsity May Cry” benchmark. In Proceedings of Machine Learning Research (Vol. 234, pp. 542–553). Hongkong, China: ML Research Press.
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2024 | Published | Conference Paper | IST-REx-ID: 18061 | OA
Frantar, E., & Alistarh, D.-A. (2024). QMoE: Sub-1-bit compression of trillion parameter models. In P. Gibbons, G. Pekhimenko, & C. De Sa (Eds.), Proceedings of Machine Learning and Systems (Vol. 6). Santa Clara, CA, USA.
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2024 | Published | Conference Paper | IST-REx-ID: 18062 | OA
Frantar, E., Ruiz, C. R., Houlsby, N., Alistarh, D.-A., & Evci, U. (2024). Scaling laws for sparsely-connected foundation models. In The Twelfth International Conference on Learning Representations. Vienna, Austria.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 

2024 | Published | Conference Paper | IST-REx-ID: 18070
Chatterjee, B., Kungurtsev, V., & Alistarh, D.-A. (2024). Federated SGD with local asynchrony. In Proceedings of the 44th International Conference on Distributed Computing Systems (pp. 857–868). Jersey City, NJ, United States: IEEE. https://doi.org/10.1109/ICDCS60910.2024.00084
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2024 | Published | Conference Paper | IST-REx-ID: 18113 | OA
Egiazarian, V., Panferov, A., Kuznedelev, D., Frantar, E., Babenko, A., & Alistarh, D.-A. (2024). Extreme compression of large language models via additive quantization. In Proceedings of the 41st International Conference on Machine Learning (Vol. 235, pp. 12284–12303). Vienna, Austria: ML Research Press.
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2024 | Published | Conference Paper | IST-REx-ID: 18117 | OA
Nikdan, M., Tabesh, S., Crncevic, E., & Alistarh, D.-A. (2024). RoSA: Accurate parameter-efficient fine-tuning via robust adaptation. In Proceedings of the 41st International Conference on Machine Learning (Vol. 235, pp. 38187–38206). Vienna, Austria: ML Research Press.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 

2024 | Published | Conference Paper | IST-REx-ID: 18121 | OA
Moakhar, A. S., Iofinova, E. B., Frantar, E., & Alistarh, D.-A. (2024). SPADE: Sparsity-guided debugging for deep neural networks. In Proceedings of the 41st International Conference on Machine Learning (Vol. 235, pp. 45955–45987). Vienna, Austria: ML Research Press.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 

2024 | Published | Thesis | IST-REx-ID: 17465 | OA
Shevchenko, A. (2024). High-dimensional limits in artificial neural networks. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:17465
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2024 | Published | Conference Paper | IST-REx-ID: 17469 | OA
Kögler, K., Shevchenko, A., Hassani, H., & Mondelli, M. (2024). Compression of structured data with autoencoders: Provable benefit of nonlinearities and depth. In Proceedings of the 41st International Conference on Machine Learning (Vol. 235, pp. 24964–25015). Vienna, Austria: ML Research Press.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 

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 | arXiv
 

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