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141 Publications
2024 | Published | Conference Paper | IST-REx-ID: 17093 | 
H. Zakerinia, S. Talaei, G. Nadiradze, and D.-A. Alistarh, “Communication-efficient federated learning with data and client heterogeneity,” in Proceedings of the 27th International Conference on Artificial Intelligence and Statistics, Valencia, Spain, 2024, vol. 238, pp. 3448–3456.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 17329 |
D.-A. Alistarh, K. Chatterjee, M. Karrabi, and J. M. Lazarsfeld, “Game dynamics and equilibrium computation in the population protocol model,” in Proceedings of the 43rd Annual ACM Symposium on Principles of Distributed Computing, Nantes, France, 2024, pp. 40–49.
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2024 | Published | Conference Paper | IST-REx-ID: 17332 |
I. Kokorin, V. Yudov, V. Aksenov, and D.-A. Alistarh, “Wait-free trees with asymptotically-efficient range queries,” in 2024 IEEE International Parallel and Distributed Processing Symposium, San Francisco, CA, United States, 2024, pp. 169–179.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 17456 |
I. Markov, K. Alimohammadi, E. Frantar, and D.-A. Alistarh, “L-GreCo: Layerwise-adaptive gradient compression for efficient data-parallel deep learning,” in Proceedings of Machine Learning and Systems , Athens, Greece, 2024, vol. 6.
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| arXiv
2024 | Published | Thesis | IST-REx-ID: 17485 |
E. Frantar, “Compressing large neural networks : Algorithms, systems and scaling laws,” Institute of Science and Technology Austria, 2024.
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2024 | Published | Thesis | IST-REx-ID: 17490 |
I. Markov, “Communication-efficient distributed training of deep neural networks: An algorithms and systems perspective,” Institute of Science and Technology Austria, 2024.
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2024 | Published | Conference Paper | IST-REx-ID: 18061 |
E. Frantar and D.-A. Alistarh, “QMoE: Sub-1-bit compression of trillion parameter models,” in Proceedings of Machine Learning and Systems, Santa Clara, CA, USA, 2024, vol. 6.
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2024 | Published | Conference Paper | IST-REx-ID: 18062 |
E. Frantar, C. R. Ruiz, N. Houlsby, D.-A. Alistarh, and U. Evci, “Scaling laws for sparsely-connected foundation models,” in The Twelfth International Conference on Learning Representations, Vienna, Austria, 2024.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18113 |
V. Egiazarian, A. Panferov, D. Kuznedelev, E. Frantar, A. Babenko, and D.-A. Alistarh, “Extreme compression of large language models via additive quantization,” in Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 12284–12303.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18117 |
M. Nikdan, S. Tabesh, E. Crncevic, and D.-A. Alistarh, “RoSA: Accurate parameter-efficient fine-tuning via robust adaptation,” in Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 38187–38206.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18121 |
A. S. Moakhar, E. B. Iofinova, E. Frantar, and D.-A. Alistarh, “SPADE: Sparsity-guided debugging for deep neural networks,” in Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 45955–45987.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 15011 |
E. Kurtic, T. Hoefler, and D.-A. Alistarh, “How to prune your language model: Recovering accuracy on the ‘Sparsity May Cry’ benchmark,” in Proceedings of Machine Learning Research, Hongkong, China, 2024, vol. 234, pp. 542–553.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 17469 |
K. Kögler, A. Shevchenko, H. Hassani, and M. Mondelli, “Compression of structured data with autoencoders: Provable benefit of nonlinearities and depth,” in Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 24964–25015.
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| arXiv
2024 | Published | Thesis | IST-REx-ID: 17465 |
A. Shevchenko, “High-dimensional limits in artificial neural networks,” Institute of Science and Technology Austria, 2024.
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2023 | Published | Conference Paper | IST-REx-ID: 17378 |
E. Frantar, S. Ashkboos, T. Hoefler, and D.-A. Alistarh, “OPTQ: Accurate post-training quantization for generative pre-trained transformers,” in 11th International Conference on Learning Representations , Kigali, Rwanda, 2023.
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2023 | Published | Journal Article | IST-REx-ID: 12330 |
V. Aksenov, D.-A. Alistarh, A. Drozdova, and A. Mohtashami, “The splay-list: A distribution-adaptive concurrent skip-list,” Distributed Computing, vol. 36. Springer Nature, pp. 395–418, 2023.
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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: 12735 |
N. Koval, D.-A. Alistarh, and R. Elizarov, “Fast and scalable channels in Kotlin Coroutines,” in Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, Montreal, QC, Canada, 2023, pp. 107–118.
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| arXiv
2023 | Published | Conference Poster | IST-REx-ID: 12736 |
V. Aksenov, T. A. Brown, A. Fedorov, and I. Kokorin, Unexpected scaling in path copying trees. Association for Computing Machinery, 2023, pp. 438–440.
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