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158 Publications
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 | 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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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
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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| arXiv
2023 | Published | Conference Paper | IST-REx-ID: 14460 |
Nikdan, M., Pegolotti, T., Iofinova, E. B., Kurtic, E., & Alistarh, D.-A. (2023). SparseProp: Efficient sparse backpropagation for faster training of neural networks at the edge. In Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 26215–26227). Honolulu, Hawaii, HI, United States: ML Research Press.
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| arXiv
2023 | Published | Conference Paper | IST-REx-ID: 17378 |
Frantar, E., Ashkboos, S., Hoefler, T., & Alistarh, D.-A. (2023). OPTQ: Accurate post-training quantization for generative pre-trained transformers. In 11th International Conference on Learning Representations . Kigali, Rwanda: International Conference on Learning Representations.
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2023 | Published | Conference Paper | IST-REx-ID: 14458 |
Frantar, E., & Alistarh, D.-A. (2023). SparseGPT: Massive language models can be accurately pruned in one-shot. In Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 10323–10337). Honolulu, Hawaii, HI, United States: ML Research Press.
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| arXiv
2023 | Published | Journal Article | IST-REx-ID: 12566 |
Alistarh, D.-A., Ellen, F., & Rybicki, J. (2023). Wait-free approximate agreement on graphs. Theoretical Computer Science. Elsevier. https://doi.org/10.1016/j.tcs.2023.113733
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