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121 Publications
2023 | 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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2023 | Thesis | IST-REx-ID: 13074 |
Peste, E.-A. (2023). Efficiency and generalization of sparse neural networks. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:13074
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2023 | 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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2023 | Conference Paper | IST-REx-ID: 14461 |
Markov, I., Vladu, A., Guo, Q., & Alistarh, D.-A. (2023). Quantized distributed training of large models with convergence guarantees. In Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 24020–24044). Honolulu, Hawaii, HI, United States: ML Research Press.
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2023 | Conference Paper | IST-REx-ID: 14459 |
Shevchenko, A., Kögler, K., Hassani, H., & Mondelli, M. (2023). Fundamental limits of two-layer autoencoders, and achieving them with gradient methods. In Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 31151–31209). Honolulu, Hawaii, HI, United States: ML Research Press.
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2023 | 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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2023 | 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 | Journal Article | IST-REx-ID: 14364 |
Alistarh, D.-A., Aspnes, J., Ellen, F., Gelashvili, R., & Zhu, L. (2023). Why extension-based proofs fail. SIAM Journal on Computing. Society for Industrial and Applied Mathematics. https://doi.org/10.1137/20M1375851
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2023 | Conference Paper | IST-REx-ID: 14771 |
Iofinova, E. B., Peste, E.-A., & Alistarh, D.-A. (2023). Bias in pruned vision models: In-depth analysis and countermeasures. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 24364–24373). Vancouver, BC, Canada: IEEE. https://doi.org/10.1109/cvpr52729.2023.02334
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2023 | 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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