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121 Publications

2021 | Conference Paper | IST-REx-ID: 10049 | OA
K. Klein et al., “Keep the dirt: tainted TreeKEM, adaptively and actively secure continuous group key agreement,” in 2021 IEEE Symposium on Security and Privacy , San Francisco, CA, United States, 2021, pp. 268–284.
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2021 | Conference Paper | IST-REx-ID: 10854 | OA
K.-T. Foerster, J. Korhonen, A. Paz, J. Rybicki, and S. Schmid, “Input-dynamic distributed algorithms for communication networks,” in Abstract Proceedings of the 2021 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems, Virtual, Online, 2021, pp. 71–72.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
2021 | Journal Article | IST-REx-ID: 10855 | OA
K.-T. Foerster, J. Korhonen, A. Paz, J. Rybicki, and S. Schmid, “Input-dynamic distributed algorithms for communication networks,” Proceedings of the ACM on Measurement and Analysis of Computing Systems, vol. 5, no. 1. Association for Computing Machinery, pp. 1–33, 2021.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
2021 | Thesis | IST-REx-ID: 10429 | OA
G. Nadiradze, “On achieving scalability through relaxation,” Institute of Science and Technology Austria, 2021.
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2021 | Conference Paper | IST-REx-ID: 10435 | OA
G. Nadiradze, A. Sabour, P. Davies, S. Li, and D.-A. Alistarh, “Asynchronous decentralized SGD with quantized and local updates,” in 35th Conference on Neural Information Processing Systems, Sydney, Australia, 2021.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
2021 | Journal Article | IST-REx-ID: 9541 | OA
A. Czumaj, P. Davies, and M. Parter, “Graph sparsification for derandomizing massively parallel computation with low space,” ACM Transactions on Algorithms, vol. 17, no. 2. Association for Computing Machinery, 2021.
[Submitted Version] View | Files available | DOI | Download Submitted Version (ext.) | WoS | arXiv
 
2021 | Conference Paper | IST-REx-ID: 9678 | OA
S. Brandt, B. Keller, J. Rybicki, J. Suomela, and J. Uitto, “Efficient load-balancing through distributed token dropping,” in Annual ACM Symposium on Parallelism in Algorithms and Architectures, Virtual Event, United States, 2021, pp. 129–139.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
2021 | Journal Article | IST-REx-ID: 8286 | OA
D.-A. Alistarh, G. Nadiradze, and A. Sabour, “Dynamic averaging load balancing on cycles,” Algorithmica. Springer Nature, 2021.
[Published Version] View | Files available | DOI | WoS | arXiv
 
2021 | Journal Article | IST-REx-ID: 9571 | OA
A. Ramezani-Kebrya, F. Faghri, I. Markov, V. Aksenov, D.-A. Alistarh, and D. M. Roy, “NUQSGD: Provably communication-efficient data-parallel SGD via nonuniform quantization,” Journal of Machine Learning Research, vol. 22, no. 114. Journal of Machine Learning Research, p. 1−43, 2021.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
2021 | Journal Article | IST-REx-ID: 7939 | OA
K. Censor-Hillel, M. Dory, J. Korhonen, and D. Leitersdorf, “Fast approximate shortest paths in the congested clique,” Distributed Computing, vol. 34. Springer Nature, pp. 463–487, 2021.
[Published Version] View | Files available | DOI | Download Published Version (ext.) | WoS | arXiv
 
2021 | Journal Article | IST-REx-ID: 15271
A. Czumaj, P. Davies, and M. Parter, “Simple, deterministic, constant-round coloring in congested clique and MPC,” SIAM Journal on Computing, vol. 50, no. 5. Society for Industrial & Applied Mathematics, pp. 1603–1626, 2021.
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2021 | Journal Article | IST-REx-ID: 15267 | OA
A. Czumaj and P. Davies, “Exploiting spontaneous transmissions for broadcasting and leader election in radio networks,” Journal of the ACM, vol. 68, no. 2. Association for Computing Machinery, 2021.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
2021 | Conference Paper | IST-REx-ID: 15263 | OA
F. Alimisis, A. Orvieto, G. Becigneul, and A. Lucchi, “Momentum improves optimization on Riemannian manifolds,” in Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, San Diego, CA, United States; Virtual, 2021, vol. 130, pp. 1351–1359.
[Published Version] View | Download Published Version (ext.) | arXiv
 
2020 | Conference Paper | IST-REx-ID: 7272 | OA
M. Arbel-Raviv, T. A. Brown, and A. Morrison, “Getting to the root of concurrent binary search tree performance,” in Proceedings of the 2018 USENIX Annual Technical Conference, Boston, MA, United States, 2020, pp. 295–306.
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2020 | Conference Paper | IST-REx-ID: 7605 | OA
D.-A. Alistarh, A. Fedorov, and N. Koval, “In search of the fastest concurrent union-find algorithm,” in 23rd International Conference on Principles of Distributed Systems, Neuchatal, Switzerland, 2020, vol. 153, p. 15:1-15:16.
[Published Version] View | Files available | DOI | arXiv
 
2020 | Conference Paper | IST-REx-ID: 7803 | OA
A. Czumaj, P. Davies, and M. Parter, “Simple, deterministic, constant-round coloring in the congested clique,” in Proceedings of the 2020 ACM Symposium on Principles of Distributed Computing, Salerno, Italy, 2020, pp. 309–318.
[Submitted Version] View | Files available | DOI | arXiv
 
2020 | Conference Paper | IST-REx-ID: 8725 | OA
V. Aksenov, D.-A. Alistarh, A. Drozdova, and A. Mohtashami, “The splay-list: A distribution-adaptive concurrent skip-list,” in 34th International Symposium on Distributed Computing, Freiburg, Germany, 2020, vol. 179, p. 3:1-3:18.
[Published Version] View | Files available | DOI | arXiv
 
2020 | Conference Paper | IST-REx-ID: 9632 | OA
S. P. Singh and D.-A. Alistarh, “WoodFisher: Efficient second-order approximation for neural network compression,” in Advances in Neural Information Processing Systems, Vancouver, Canada, 2020, vol. 33, pp. 18098–18109.
[Published Version] View | Download Published Version (ext.) | arXiv
 
2020 | Conference Paper | IST-REx-ID: 9631 | OA
V. Aksenov, D.-A. Alistarh, and J. Korhonen, “Scalable belief propagation via relaxed scheduling,” in Advances in Neural Information Processing Systems, Vancouver, Canada, 2020, vol. 33, pp. 22361–22372.
[Published Version] View | Download Published Version (ext.) | arXiv
 
2020 | Conference Paper | IST-REx-ID: 9415 | OA
M. Kurtz et al., “Inducing and exploiting activation sparsity for fast neural network inference,” in 37th International Conference on Machine Learning, ICML 2020, Online, 2020, vol. 119, pp. 5533–5543.
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