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162 Publications
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: 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: 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: 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 | 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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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 19518 |
Wu D, Modoranu I-V, Safaryan M, Kuznedelev D, Alistarh D-A. The iterative optimal brain surgeon: Faster sparse recovery by leveraging second-order information. In: 38th Conference on Neural Information Processing Systems. Vol 37. Neural Information Processing Systems Foundation; 2024.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 19510 |
Modoranu I-V, Safaryan M, Malinovsky G, et al. MICROADAM: Accurate adaptive optimization with low space overhead and provable convergence. In: 38th Conference on Neural Information Processing Systems. Vol 37. Neural Information Processing Systems Foundation; 2024.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 19511 |
Ashkboos S, Mohtashami A, Croci ML, et al. QuaRot: Outlier-free 4-bit inference in rotated LLMs. In: 38th Conference on Neural Information Processing Systems. Vol 37. Neural Information Processing Systems Foundation; 2024.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 19519 |
Malinovskii V, Mazur D, Ilin I, et al. PV-tuning: Beyond straight-through estimation for extreme LLM compression. In: 38th Conference on Neural Information Processing Systems. Vol 37. Neural Information Processing Systems Foundation; 2024.
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| arXiv
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: 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 | 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 | Research Data Reference | IST-REx-ID: 19884 |
Frantar E, Castro R, Chen J, Hoefler T, Alistarh D-A. MARLIN: Mixed-precision auto-regressive parallel inference on Large Language Models. 2024. doi:10.5281/ZENODO.14213091
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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 | Journal Article | IST-REx-ID: 12330 |
Aksenov V, Alistarh D-A, Drozdova A, Mohtashami A. The splay-list: A distribution-adaptive concurrent skip-list. Distributed Computing. 2023;36:395-418. doi:10.1007/s00446-022-00441-x
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| arXiv
2023 | Published | Conference Paper | IST-REx-ID: 12735 |
Koval N, Alistarh D-A, Elizarov R. Fast and scalable channels in Kotlin Coroutines. In: Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. Association for Computing Machinery; 2023:107-118. doi:10.1145/3572848.3577481
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| arXiv
2023 | Published | Conference Poster | IST-REx-ID: 12736 |
Aksenov V, Brown TA, Fedorov A, Kokorin I. Unexpected Scaling in Path Copying Trees. Association for Computing Machinery; 2023:438-440. doi:10.1145/3572848.3577512
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2023 | Published | Journal Article | IST-REx-ID: 14815 |
Beznosikov A, Horvath S, Richtarik P, Safaryan M. On biased compression for distributed learning. Journal of Machine Learning Research. 2023;24:1-50.
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