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118 Publications
2018 | Journal Article | IST-REx-ID: 43 | 
J. Rybicki, E. Kisdi, and J. Anttila, “Model of bacterial toxin-dependent pathogenesis explains infective dose,” PNAS, vol. 115, no. 42. National Academy of Sciences, pp. 10690–10695, 2018.
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2018 | Journal Article | IST-REx-ID: 76 |
C. Lenzen and J. Rybicki, “Near-optimal self-stabilising counting and firing squads,” Distributed Computing. Springer, 2018.
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2018 | Conference Paper | IST-REx-ID: 85 |
E. Gilad, T. A. Brown, M. Oskin, and Y. Etsion, “Snapshot based synchronization: A fast replacement for Hand-over-Hand locking,” presented at the Euro-Par: European Conference on Parallel Processing, Turin, Italy, 2018, vol. 11014, pp. 465–479.
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2018 | Conference Paper | IST-REx-ID: 5962 |
D.-A. Alistarh, C. De Sa, and N. H. Konstantinov, “The convergence of stochastic gradient descent in asynchronous shared memory,” in Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing - PODC ’18, Egham, United Kingdom, 2018, pp. 169–178.
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| arXiv
2018 | Conference Paper | IST-REx-ID: 5963 |
D.-A. Alistarh, T. A. Brown, J. Kopinsky, and G. Nadiradze, “Relaxed schedulers can efficiently parallelize iterative algorithms,” in Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing - PODC ’18, Egham, United Kingdom, 2018, pp. 377–386.
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| arXiv
2018 | Conference Paper | IST-REx-ID: 5965 |
D.-A. Alistarh, T. A. Brown, J. Kopinsky, J. Z. Li, and G. Nadiradze, “Distributionally linearizable data structures,” in Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures - SPAA ’18, Vienna, Austria, 2018, pp. 133–142.
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| arXiv
2018 | Conference Paper | IST-REx-ID: 5966 |
D.-A. Alistarh, S. K. Haider, R. Kübler, and G. Nadiradze, “The transactional conflict problem,” in Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures - SPAA ’18, Vienna, Austria, 2018, pp. 383–392.
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| arXiv
2018 | Conference Paper | IST-REx-ID: 5964 |
V. Aksenov, D.-A. Alistarh, and P. Kuznetsov, “Brief Announcement: Performance prediction for coarse-grained locking,” in Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing - PODC ’18, Egham, United Kingdom, 2018, pp. 411–413.
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2018 | Conference Paper | IST-REx-ID: 6031
A. Stojanov, T. M. Smith, D.-A. Alistarh, and M. Puschel, “Fast quantized arithmetic on x86: Trading compute for data movement,” in 2018 IEEE International Workshop on Signal Processing Systems, Cape Town, South Africa, 2018, vol. 2018–October.
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2018 | Conference Paper | IST-REx-ID: 7123 |
D.-A. Alistarh, J. Aspnes, and R. Gelashvili, “Space-optimal majority in population protocols,” in Proceedings of the 29th Annual ACM-SIAM Symposium on Discrete Algorithms, New Orleans, LA, United States, 2018, pp. 2221–2239.
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| arXiv
2018 | Conference Paper | IST-REx-ID: 6558 |
D.-A. Alistarh, Z. Allen-Zhu, and J. Li, “Byzantine stochastic gradient descent,” in Advances in Neural Information Processing Systems, Montreal, Canada, 2018, vol. 2018, pp. 4613–4623.
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| arXiv
2018 | Conference Paper | IST-REx-ID: 6589 |
D.-A. Alistarh, T. Hoefler, M. Johansson, N. H. Konstantinov, S. Khirirat, and C. Renggli, “The convergence of sparsified gradient methods,” in Advances in Neural Information Processing Systems 31, Montreal, Canada, 2018, vol. Volume 2018, pp. 5973–5983.
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| arXiv
2017 | Conference Paper | IST-REx-ID: 487
G. Baig, B. Radunovic, D.-A. Alistarh, M. Balkwill, T. Karagiannis, and L. Qiu, “Towards unlicensed cellular networks in TV white spaces,” in Proceedings of the 2017 13th International Conference on emerging Networking EXperiments and Technologies, Incheon, South Korea, 2017, pp. 2–14.
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2017 | Conference Paper | IST-REx-ID: 791 |
D.-A. Alistarh, J. Kopinsky, J. Li, and G. Nadiradze, “The power of choice in priority scheduling,” in Proceedings of the ACM Symposium on Principles of Distributed Computing, Washington, WA, USA, 2017, vol. Part F129314, pp. 283–292.
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2017 | Conference Paper | IST-REx-ID: 431 |
D.-A. Alistarh, D. Grubic, J. Li, R. Tomioka, and M. Vojnović, “QSGD: Communication-efficient SGD via gradient quantization and encoding,” presented at the NIPS: Neural Information Processing System, Long Beach, CA, United States, 2017, vol. 2017, pp. 1710–1721.
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2017 | Conference Paper | IST-REx-ID: 432 |
H. Zhang, J. Li, K. Kara, D.-A. Alistarh, J. Liu, and C. Zhang, “ZipML: Training linear models with end-to-end low precision, and a little bit of deep learning,” in Proceedings of Machine Learning Research, Sydney, Australia, 2017, vol. 70, pp. 4035–4043.
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