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144 Publications
2024 | Published | Conference Paper | IST-REx-ID: 18975 | 
Modoranu I-V, Kalinov A, Kurtic E, Frantar E, Alistarh D-A. Error feedback can accurately compress preconditioners. In: 41st International Conference on Machine Learning. Vol 235. ML Research Press; 2024:35910-35933.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18976 |
Islamov R, Safaryan M, Alistarh D-A. AsGrad: A sharp unified analysis of asynchronous-SGD algorithms. In: Proceedings of The 27th International Conference on Artificial Intelligence and Statistics. Vol 238. ML Research Press; 2024:649-657.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18977 |
Dettmers T, Svirschevski RA, Egiazarian V, et al. SpQR: A sparse-quantized representation for near-lossless LLM weight compression. In: 12th International Conference on Learning Representations. OpenReview; 2024.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 17093 |
Zakerinia H, Talaei S, Nadiradze G, Alistarh D-A. Communication-efficient federated learning with data and client heterogeneity. In: Proceedings of the 27th International Conference on Artificial Intelligence and Statistics. Vol 238. ML Research Press; 2024:3448-3456.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 17329 |
Alistarh D-A, Chatterjee K, Karrabi M, Lazarsfeld JM. Game dynamics and equilibrium computation in the population protocol model. In: Proceedings of the 43rd Annual ACM Symposium on Principles of Distributed Computing. Association for Computing Machinery; 2024:40-49. doi:10.1145/3662158.3662768
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2024 | Published | Conference Paper | IST-REx-ID: 17332 |
Kokorin I, Yudov V, Aksenov V, Alistarh D-A. Wait-free trees with asymptotically-efficient range queries. In: 2024 IEEE International Parallel and Distributed Processing Symposium. IEEE; 2024:169-179. doi:10.1109/IPDPS57955.2024.00023
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 17456 |
Markov I, Alimohammadi K, Frantar E, Alistarh D-A. L-GreCo: Layerwise-adaptive gradient compression for efficient data-parallel deep learning. In: Gibbons P, Pekhimenko G, De Sa C, eds. Proceedings of Machine Learning and Systems . Vol 6. Association for Computing Machinery; 2024.
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2024 | Published | Thesis | IST-REx-ID: 17485 |
Frantar E. Compressing large neural networks : Algorithms, systems and scaling laws. 2024. doi:10.15479/at:ista:17485
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2024 | Published | Thesis | IST-REx-ID: 17490 |
Markov I. Communication-efficient distributed training of deep neural networks: An algorithms and systems perspective. 2024. doi:10.15479/at:ista:17490
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2024 | Published | Conference Paper | IST-REx-ID: 15011 |
Kurtic E, Hoefler T, Alistarh D-A. How to prune your language model: Recovering accuracy on the “Sparsity May Cry” benchmark. In: Proceedings of Machine Learning Research. Vol 234. ML Research Press; 2024:542-553.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18061 |
Frantar E, Alistarh D-A. QMoE: Sub-1-bit compression of trillion parameter models. In: Gibbons P, Pekhimenko G, De Sa C, eds. Proceedings of Machine Learning and Systems. Vol 6. ; 2024.
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2024 | Published | Conference Paper | IST-REx-ID: 18062 |
Frantar E, Ruiz CR, Houlsby N, Alistarh D-A, Evci U. Scaling laws for sparsely-connected foundation models. In: The Twelfth International Conference on Learning Representations. ; 2024.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18070
Chatterjee B, Kungurtsev V, Alistarh D-A. Federated SGD with local asynchrony. In: Proceedings of the 44th International Conference on Distributed Computing Systems. IEEE; 2024:857-868. doi:10.1109/ICDCS60910.2024.00084
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2024 | Published | Conference Paper | IST-REx-ID: 18113 |
Egiazarian V, Panferov A, Kuznedelev D, Frantar E, Babenko A, Alistarh D-A. Extreme compression of large language models via additive quantization. In: Proceedings of the 41st International Conference on Machine Learning. Vol 235. ML Research Press; 2024:12284-12303.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18117 |
Nikdan M, Tabesh S, Crncevic E, Alistarh D-A. RoSA: Accurate parameter-efficient fine-tuning via robust adaptation. In: Proceedings of the 41st International Conference on Machine Learning. Vol 235. ML Research Press; 2024:38187-38206.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18121 |
Moakhar AS, Iofinova EB, Frantar E, Alistarh D-A. SPADE: Sparsity-guided debugging for deep neural networks. In: Proceedings of the 41st International Conference on Machine Learning. Vol 235. ML Research Press; 2024:45955-45987.
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| arXiv
2024 | Published | Thesis | IST-REx-ID: 17465 |
Shevchenko A. High-dimensional limits in artificial neural networks. 2024. doi:10.15479/at:ista:17465
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2024 | Published | Conference Paper | IST-REx-ID: 17469 |
Kögler K, Shevchenko A, Hassani H, Mondelli M. Compression of structured data with autoencoders: Provable benefit of nonlinearities and depth. In: Proceedings of the 41st International Conference on Machine Learning. Vol 235. ML Research Press; 2024:24964-25015.
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| arXiv
2023 | Published | Journal Article | IST-REx-ID: 13179 |
Koval N, Khalanskiy D, Alistarh D-A. CQS: A formally-verified framework for fair and abortable synchronization. Proceedings of the ACM on Programming Languages. 2023;7. doi:10.1145/3591230
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2023 | Published | Conference Paper | IST-REx-ID: 13262 |
Fedorov A, Hashemi D, Nadiradze G, Alistarh D-A. Provably-efficient and internally-deterministic parallel Union-Find. In: Proceedings of the 35th ACM Symposium on Parallelism in Algorithms and Architectures. Association for Computing Machinery; 2023:261-271. doi:10.1145/3558481.3591082
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