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121 Publications


2023 | Journal Article | IST-REx-ID: 12566 | OA
D.-A. Alistarh, F. Ellen, and J. Rybicki, “Wait-free approximate agreement on graphs,” Theoretical Computer Science, vol. 948, no. 2. Elsevier, 2023.
[Published Version] View | Files available | DOI | WoS
 

2023 | Thesis | IST-REx-ID: 13074 | OA
E.-A. Peste, “Efficiency and generalization of sparse neural networks,” Institute of Science and Technology Austria, 2023.
[Published Version] View | Files available | DOI
 

2023 | Journal Article | IST-REx-ID: 12330 | OA
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.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 

2023 | Conference Paper | IST-REx-ID: 14461 | OA
I. Markov, A. Vladu, Q. Guo, and D.-A. Alistarh, “Quantized distributed training of large models with convergence guarantees,” in Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, HI, United States, 2023, vol. 202, pp. 24020–24044.
[Preprint] View | Download Preprint (ext.) | arXiv
 

2023 | Conference Paper | IST-REx-ID: 14459 | OA
A. Shevchenko, K. Kögler, H. Hassani, and M. Mondelli, “Fundamental limits of two-layer autoencoders, and achieving them with gradient methods,” in Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, HI, United States, 2023, vol. 202, pp. 31151–31209.
[Preprint] View | Download Preprint (ext.) | arXiv
 

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