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118 Publications


2022 | Journal Article | IST-REx-ID: 11420 | OA
Shevchenko, A., Kungurtsev, V., & Mondelli, M. (2022). Mean-field analysis of piecewise linear solutions for wide ReLU networks. Journal of Machine Learning Research. Journal of Machine Learning Research.
[Published Version] View | Files available | arXiv
 

2022 | Conference Paper | IST-REx-ID: 12182 | OA
Pacut, M., Parham, M., Rybicki, J., Schmid, S., Suomela, J., & Tereshchenko, A. (2022). Brief announcement: Temporal locality in online algorithms. In 36th International Symposium on Distributed Computing (Vol. 246). Augusta, GA, United States: Schloss Dagstuhl - Leibniz-Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.DISC.2022.52
[Published Version] View | Files available | DOI
 

2022 | Conference Paper | IST-REx-ID: 12780 | OA
Markov, I., Ramezanikebrya, H., & Alistarh, D.-A. (2022). CGX: Adaptive system support for communication-efficient deep learning. In Proceedings of the 23rd ACM/IFIP International Middleware Conference (pp. 241–254). Quebec, QC, Canada: Association for Computing Machinery. https://doi.org/10.1145/3528535.3565248
[Published Version] View | Files available | DOI | arXiv
 

2022 | Conference Paper | IST-REx-ID: 11844 | OA
Alistarh, D.-A., Rybicki, J., & Voitovych, S. (2022). Near-optimal leader election in population protocols on graphs. In Proceedings of the Annual ACM Symposium on Principles of Distributed Computing (pp. 246–256). Salerno, Italy: Association for Computing Machinery. https://doi.org/10.1145/3519270.3538435
[Published Version] View | Files available | DOI | arXiv
 

2022 | Conference Paper | IST-REx-ID: 11181 | OA
Brown, T. A., Sigouin, W., & Alistarh, D.-A. (2022). PathCAS: An efficient middle ground for concurrent search data structures. In Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (pp. 385–399). Seoul, Republic of Korea: Association for Computing Machinery. https://doi.org/10.1145/3503221.3508410
[Published Version] View | Files available | DOI | WoS
 

2022 | Conference Paper | IST-REx-ID: 11180 | OA
Postnikova, A., Koval, N., Nadiradze, G., & Alistarh, D.-A. (2022). Multi-queues can be state-of-the-art priority schedulers. In Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (pp. 353–367). Seoul, Republic of Korea: Association for Computing Machinery. https://doi.org/10.1145/3503221.3508432
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 

2022 | Research Data Reference | IST-REx-ID: 13076 | OA
Postnikova, A., Koval, N., Nadiradze, G., & Alistarh, D.-A. (2022). Multi-queues can be state-of-the-art priority schedulers. Zenodo. https://doi.org/10.5281/ZENODO.5733408
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2022 | Conference Paper | IST-REx-ID: 11707 | OA
Balliu, A., Hirvonen, J., Melnyk, D., Olivetti, D., Rybicki, J., & Suomela, J. (2022). Local mending. In M. Parter (Ed.), International Colloquium on Structural Information and Communication Complexity (Vol. 13298, pp. 1–20). Paderborn, Germany: Springer Nature. https://doi.org/10.1007/978-3-031-09993-9_1
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 

2022 | Conference Paper | IST-REx-ID: 12299 | OA
Iofinova, E. B., Peste, E.-A., Kurtz, M., & Alistarh, D.-A. (2022). How well do sparse ImageNet models transfer? In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 12256–12266). New Orleans, LA, United States: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/cvpr52688.2022.01195
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 

2021 | Journal Article | IST-REx-ID: 10180 | OA
Hoefler, T., Alistarh, D.-A., Ben-Nun, T., Dryden, N., & Peste, E.-A. (2021). Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks. Journal of Machine Learning Research. Journal of Machine Learning Research.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 

2021 | Conference Paper | IST-REx-ID: 10218 | OA
Alistarh, D.-A., Gelashvili, R., & Rybicki, J. (2021). Brief announcement: Fast graphical population protocols. In 35th International Symposium on Distributed Computing (Vol. 209). Freiburg, Germany: Schloss Dagstuhl - Leibniz-Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.DISC.2021.43
[Published Version] View | Files available | DOI | arXiv
 

2021 | Conference Paper | IST-REx-ID: 10217 | OA
Alistarh, D.-A., Gelashvili, R., & Nadiradze, G. (2021). Lower bounds for shared-memory leader election under bounded write contention. In 35th International Symposium on Distributed Computing (Vol. 209). Freiburg, Germany: Schloss Dagstuhl - Leibniz Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.DISC.2021.4
[Published Version] View | Files available | DOI
 

2021 | Conference Paper | IST-REx-ID: 10216 | OA
Chatterjee, B., Peri, S., & Sa, M. (2021). Brief announcement: Non-blocking dynamic unbounded graphs with worst-case amortized bounds. In 35th International Symposium on Distributed Computing (Vol. 209). Freiburg, Germany: Schloss Dagstuhl - Leibniz Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.DISC.2021.52
[Published Version] View | Files available | DOI | arXiv
 

2021 | Conference Paper | IST-REx-ID: 10219 | OA
Korhonen, J., Paz, A., Rybicki, J., Schmid, S., & Suomela, J. (2021). Brief announcement: Sinkless orientation is hard also in the supported LOCAL model. In 35th International Symposium on Distributed Computing (Vol. 209). Freiburg, Germany: Schloss Dagstuhl - Leibniz Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.DISC.2021.58
[Published Version] View | Files available | DOI | arXiv
 

2021 | Conference Paper | IST-REx-ID: 10853 | OA
Fedorov, A., Koval, N., & Alistarh, D.-A. (2021). A scalable concurrent algorithm for dynamic connectivity. In Proceedings of the 33rd ACM Symposium on Parallelism in Algorithms and Architectures (pp. 208–220). Virtual, Online: Association for Computing Machinery. https://doi.org/10.1145/3409964.3461810
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 

2021 | Conference Paper | IST-REx-ID: 11436 | OA
Kungurtsev, V., Egan, M., Chatterjee, B., & Alistarh, D.-A. (2021). Asynchronous optimization methods for efficient training of deep neural networks with guarantees. In 35th AAAI Conference on Artificial Intelligence, AAAI 2021 (Vol. 35, pp. 8209–8216). Virtual, Online: AAAI Press.
[Preprint] View | Download Preprint (ext.) | arXiv
 

2021 | Conference Paper | IST-REx-ID: 11452 | OA
Alimisis, F., Davies, P., Vandereycken, B., & Alistarh, D.-A. (2021). Distributed principal component analysis with limited communication. In Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems (Vol. 4, pp. 2823–2834). Virtual, Online: Neural Information Processing Systems Foundation.
[Published Version] View | Download Published Version (ext.) | arXiv
 

2021 | Conference Paper | IST-REx-ID: 11463 | OA
Frantar, E., Kurtic, E., & Alistarh, D.-A. (2021). M-FAC: Efficient matrix-free approximations of second-order information. In 35th Conference on Neural Information Processing Systems (Vol. 34, pp. 14873–14886). Virtual, Online: Curran Associates.
[Published Version] View | Download Published Version (ext.) | arXiv
 

2021 | Conference Paper | IST-REx-ID: 11464 | OA
Alistarh, D.-A., & Korhonen, J. (2021). Towards tight communication lower bounds for distributed optimisation. In 35th Conference on Neural Information Processing Systems (Vol. 34, pp. 7254–7266). Virtual, Online: Curran Associates.
[Published Version] View | Download Published Version (ext.) | arXiv
 

2021 | Conference Paper | IST-REx-ID: 9543 | OA
Davies, P., Gurunanthan, V., Moshrefi, N., Ashkboos, S., & Alistarh, D.-A. (2021). New bounds for distributed mean estimation and variance reduction. In 9th International Conference on Learning Representations. Virtual.
[Published Version] View | Download Published Version (ext.) | arXiv
 

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