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arXiv: 1705.06173

Khirirat S, Johansson M, Alistarh D-A. Gradient compression for communication-limited convex optimization. In: 2018 IEEE Conference on Decision and Control. IEEE; 2019. doi:10.1109/cdc.2018.8619625
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Renggli C, Ashkboos S, Aghagolzadeh M, Alistarh D-A, Hoefler T. SparCML: High-performance sparse communication for machine learning. In: International Conference for High Performance Computing, Networking, Storage and Analysis, SC. ACM; 2019. doi:10.1145/3295500.3356222
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arXiv: 1802.08021

Aganezov S, Zban I, Aksenov V, Alexeev N, Schatz MC. Recovering rearranged cancer chromosomes from karyotype graphs. BMC Bioinformatics. 2019;20. doi:10.1186/s12859-019-3208-4
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Koval N, Alistarh D-A, Elizarov R. Scalable FIFO channels for programming via communicating sequential processes. In: 25th Anniversary of Euro-Par. Vol 11725. Springer Nature; 2019:317-333. doi:10.1007/978-3-030-29400-7_23
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Yu C, Tang H, Renggli C, et al. Distributed learning over unreliable networks. In: 36th International Conference on Machine Learning, ICML 2019. Vol 2019-June. IMLS; 2019:12481-12512.
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Alistarh D-A, Nadiradze G, Koval N. Efficiency guarantees for parallel incremental algorithms under relaxed schedulers. In: 31st ACM Symposium on Parallelism in Algorithms and Architectures. ACM Press; 2019:145-154. doi:10.1145/3323165.3323201
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Wendler C, Alistarh D-A, Püschel M. Powerset convolutional neural networks. In: Vol 32. Neural Information Processing Systems Foundation; 2019:927-938.
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Foerster K-T, Korhonen J, Rybicki J, Schmid S. Does preprocessing help under congestion? In: Proceedings of the 2019 ACM Symposium on Principles of Distributed Computing. ACM; 2019:259-261. doi:10.1145/3293611.3331581
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Alistarh D-A, Aspnes J, Ellen F, Gelashvili R, Zhu L. Why extension-based proofs fail. In: Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing. ACM Press; 2019:986-996. doi:10.1145/3313276.3316407
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Censor-Hillel K, Dory M, Korhonen J, Leitersdorf D. Fast approximate shortest paths in the congested clique. In: Proceedings of the 2019 ACM Symposium on Principles of Distributed Computin. ACM; 2019:74-83. doi:10.1145/3293611.3331633
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Alistarh D-A, Aspnes J, King V, Saia J. Communication-efficient randomized consensus. Distributed Computing. 2018;31(6):489-501. doi:10.1007/s00446-017-0315-1
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Grubic D, Tam L, Alistarh D-A, Zhang C. Synchronous multi-GPU training for deep learning with low-precision communications: An empirical study. In: Proceedings of the 21st International Conference on Extending Database Technology. OpenProceedings; 2018:145-156. doi:10.5441/002/EDBT.2018.14
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Alistarh D-A, Leiserson W, Matveev A, Shavit N. ThreadScan: Automatic and scalable memory reclamation. ACM Transactions on Parallel Computing. 2018;4(4). doi:10.1145/3201897
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Polino A, Pascanu R, Alistarh D-A. Model compression via distillation and quantization. In: 6th International Conference on Learning Representations. ; 2018.
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Arbel Raviv M, Brown TA. Harnessing epoch-based reclamation for efficient range queries. In: Vol 53. ACM; 2018:14-27. doi:10.1145/3178487.3178489
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Rybicki J, Kisdi E, Anttila J. Model of bacterial toxin-dependent pathogenesis explains infective dose. PNAS. 2018;115(42):10690-10695. doi:10.1073/pnas.1721061115
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Lenzen C, Rybicki J. Near-optimal self-stabilising counting and firing squads. Distributed Computing. 2018. doi:10.1007/s00446-018-0342-6
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Gilad E, Brown TA, Oskin M, Etsion Y. Snapshot based synchronization: A fast replacement for Hand-over-Hand locking. In: Vol 11014. Springer; 2018:465-479. doi:10.1007/978-3-319-96983-1_33
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Alistarh D-A, De Sa C, Konstantinov NH. The convergence of stochastic gradient descent in asynchronous shared memory. In: Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18. ACM Press; 2018:169-178. doi:10.1145/3212734.3212763
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arXiv: 1803.08841