D.-A. Alistarh, “A brief tutorial on distributed and concurrent machine learning,” in Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, Egham, United Kingdom, 2018, pp. 487–488.
LibreCat: https://research-explorer.ista.ac.at/record/5961
DOI: 10.1145/3212734.3212798
WoS: 000458186900063

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.
LibreCat: https://research-explorer.ista.ac.at/record/5963
DOI: 10.1145/3212734.3212756
WoS: 000458186900048
arXiv: 1808.04155

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.
LibreCat: https://research-explorer.ista.ac.at/record/5965
DOI: 10.1145/3210377.3210411
WoS: 000545269600016
arXiv: 1804.01018

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.
LibreCat: https://research-explorer.ista.ac.at/record/5966
DOI: 10.1145/3210377.3210406
WoS: 000545269600046
arXiv: 1804.00947

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.
LibreCat: https://research-explorer.ista.ac.at/record/5964
DOI: 10.1145/3212734.3212785
WoS: 000458186900052

A. Stojanov, T. M. Smith, D.-A. Alistarh, and M. Puschel, “Fast quantized arithmetic on : Trading compute for data movement,” in 2018 IEEE International Workshop on Signal Processing Systems, Cape Town, South Africa, 2018, vol. 2018–October.
LibreCat: https://research-explorer.ista.ac.at/record/6031
DOI: 10.1109/SiPS.2018.8598402
WoS: 000465106800060

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.
LibreCat: https://research-explorer.ista.ac.at/record/7123
DOI: 10.1137/1.9781611975031.144
WoS: 000483921200145
arXiv: 1704.04947

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.
LibreCat: https://research-explorer.ista.ac.at/record/6558
WoS: 000461823304061
arXiv: 1803.08917

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.
LibreCat: https://research-explorer.ista.ac.at/record/6589
WoS: 000461852000047
arXiv: 1809.10505

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.
LibreCat: https://research-explorer.ista.ac.at/record/487
DOI: 10.1145/3143361.3143367

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 9314, pp. 283–292.
LibreCat: https://research-explorer.ista.ac.at/record/791
DOI: 10.1145/3087801.3087810
WoS: 000462995000035

D.-A. Alistarh, D. Grubic, J. Li, R. Tomioka, and M. Vojnovi, “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.
LibreCat: https://research-explorer.ista.ac.at/record/431
arXiv: 1610.02132

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.
LibreCat: https://research-explorer.ista.ac.at/record/432