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162 Publications
2024 | Published | Conference Paper | IST-REx-ID: 18062 | 
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.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 17329 |
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: 18976 |
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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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 17456 |
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.
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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. (2024). The iterative optimal brain surgeon: Faster sparse recovery by leveraging second-order information. In 38th Conference on Neural Information Processing Systems (Vol. 37). Vancouver, Canada: Neural Information Processing Systems Foundation.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 19510 |
Modoranu, I.-V., Safaryan, M., Malinovsky, G., Kurtic, E., Robert, T., Richtárik, P., & Alistarh, D.-A. (2024). 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.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 19511 |
Ashkboos, S., Mohtashami, A., Croci, M. L., Li, B., Cameron, P., Jaggi, M., … Hensman, J. (2024). QuaRot: Outlier-free 4-bit inference in rotated LLMs. In 38th Conference on Neural Information Processing Systems (Vol. 37). Vancouver, Canada: Neural Information Processing Systems Foundation.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 19519 |
Malinovskii, V., Mazur, D., Ilin, I., Kuznedelev, D., Burlachenko, K., Yi, K., … Richtarik, P. (2024). PV-tuning: Beyond straight-through estimation for extreme LLM compression. In 38th Conference on Neural Information Processing Systems (Vol. 37). Vancouver, Canada: Neural Information Processing Systems Foundation.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 17332 |
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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| 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 | Thesis | IST-REx-ID: 17490 |
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 | Research Data Reference | IST-REx-ID: 19884 |
Frantar, E., Castro, R., Chen, J., Hoefler, T., & Alistarh, D.-A. (2024). MARLIN: Mixed-precision auto-regressive parallel inference on Large Language Models. Zenodo. https://doi.org/10.5281/ZENODO.14213091
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2024 | Published | Conference Paper | IST-REx-ID: 18121 |
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.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 17469 |
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.
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| arXiv
2024 | Published | Thesis | IST-REx-ID: 17465 |
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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2023 | Published | Journal Article | IST-REx-ID: 13179 |
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 | Journal Article | IST-REx-ID: 12330 |
Aksenov, V., Alistarh, D.-A., Drozdova, A., & Mohtashami, A. (2023). The splay-list: A distribution-adaptive concurrent skip-list. Distributed Computing. Springer Nature. https://doi.org/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. (2023). Fast and scalable channels in Kotlin Coroutines. In Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (pp. 107–118). Montreal, QC, Canada: Association for Computing Machinery. https://doi.org/10.1145/3572848.3577481
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| arXiv
2023 | Published | Conference Poster | IST-REx-ID: 12736 |
Aksenov, V., Brown, T. A., Fedorov, A., & Kokorin, I. (2023). Unexpected scaling in path copying trees. Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (pp. 438–440). Montreal, QB, Canada: Association for Computing Machinery. https://doi.org/10.1145/3572848.3577512
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2023 | Published | Journal Article | IST-REx-ID: 14815 |
Beznosikov, A., Horvath, S., Richtarik, P., & Safaryan, M. (2023). On biased compression for distributed learning. Journal of Machine Learning Research. Journal of Machine Learning Research.
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