Please note that LibreCat no longer supports Internet Explorer versions 8 or 9 (or earlier).
We recommend upgrading to the latest Internet Explorer, Google Chrome, or Firefox.
122 Publications
2021 | Thesis | IST-REx-ID: 10429 | 
G. Nadiradze, “On achieving scalability through relaxation,” Institute of Science and Technology Austria, 2021.
[Published Version]
View
| Files available
| DOI
2021 | Conference Paper | IST-REx-ID: 10435 |
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 |
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 |
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: 9571 |
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 |
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: 15267 |
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 |
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 |
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.
[Published Version]
View
| Download Published Version (ext.)
2020 | Conference Paper | IST-REx-ID: 7605 |
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 |
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 |
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 |
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 |
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 |
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.
[Published Version]
View
| Files available
2020 | Journal Article | IST-REx-ID: 8268 |
N. M. Gurel et al., “Compressive sensing using iterative hard thresholding with low precision data representation: Theory and applications,” IEEE Transactions on Signal Processing, vol. 68. IEEE, pp. 4268–4282, 2020.
[Preprint]
View
| DOI
| Download Preprint (ext.)
| WoS
| arXiv
2020 | Conference Paper | IST-REx-ID: 8722 |
S. Li, T. B.-N. Tal Ben-Nun, S. D. Girolamo, D.-A. Alistarh, and T. Hoefler, “Taming unbalanced training workloads in deep learning with partial collective operations,” in Proceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, San Diego, CA, United States, 2020, pp. 45–61.
[Preprint]
View
| DOI
| Download Preprint (ext.)
| WoS
| arXiv
2020 | Journal Article | IST-REx-ID: 7224 |
J. Rybicki, N. Abrego, and O. Ovaskainen, “Habitat fragmentation and species diversity in competitive communities,” Ecology Letters, vol. 23, no. 3. Wiley, pp. 506–517, 2020.
[Published Version]
View
| Files available
| DOI
| WoS
2020 | Conference Paper | IST-REx-ID: 8724 |
N. H. Konstantinov, E. Frantar, D.-A. Alistarh, and C. Lampert, “On the sample complexity of adversarial multi-source PAC learning,” in Proceedings of the 37th International Conference on Machine Learning, Online, 2020, vol. 119, pp. 5416–5425.
[Published Version]
View
| Files available
| arXiv