Censor-Hillel K, Dory M, Korhonen J, Leitersdorf D. 2019. Fast approximate shortest paths in the congested clique. Proceedings of the 2019 ACM Symposium on Principles of Distributed Computin. PODC: Symposium on Principles of Distributed Computing, 74–83.

Alistarh D-A, Aspnes J, King V, Saia J. 2018. Communication-efficient randomized consensus. Distributed Computing. 31(6), 489–501.

Grubic D, Tam L, Alistarh D-A, Zhang C. 2018. Synchronous multi-GPU training for deep learning with low-precision communications: An empirical study. Proceedings of the 21st International Conference on Extending Database Technology. EDBT: Conference on Extending Database Technology, 145–156.

Alistarh D-A, Leiserson W, Matveev A, Shavit N. 2018. ThreadScan: Automatic and scalable memory reclamation. ACM Transactions on Parallel Computing. 4(4), 18.

Polino A, Pascanu R, Alistarh D-A. 2018. Model compression via distillation and quantization. 6th International Conference on Learning Representations. ICLR: International Conference on Learning Representations.

Arbel Raviv M, Brown TA. 2018. Harnessing epoch-based reclamation for efficient range queries. PPoPP: Principles and Practice of Parallel Programming, PPoPP, vol. 53, 14–27.

Rybicki J, Kisdi E, Anttila J. 2018. Model of bacterial toxin-dependent pathogenesis explains infective dose. PNAS. 115(42), 10690–10695.

Lenzen C, Rybicki J. 2018. Near-optimal self-stabilising counting and firing squads. Distributed Computing.

Gilad E, Brown TA, Oskin M, Etsion Y. 2018. Snapshot based synchronization: A fast replacement for Hand-over-Hand locking. Euro-Par: European Conference on Parallel Processing, LNCS, vol. 11014, 465–479.

Alistarh D-A, De Sa C, Konstantinov NH. 2018. The convergence of stochastic gradient descent in asynchronous shared memory. Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18. PODC: Principles of Distributed Computing, 169–178.

Alistarh D-A. 2018. A brief tutorial on distributed and concurrent machine learning. Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18. PODC: Principles of Distributed Computing, 487–488.

Alistarh D-A, Brown TA, Kopinsky J, Nadiradze G. 2018. Relaxed schedulers can efficiently parallelize iterative algorithms. Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18. PODC: Principles of Distributed Computing, 377–386.

Alistarh D-A, Brown TA, Kopinsky J, Li JZ, Nadiradze G. 2018. Distributionally linearizable data structures. Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18. SPAA: Symposium on Parallelism in Algorithms and Architectures, 133–142.

Alistarh D-A, Haider SK, Kübler R, Nadiradze G. 2018. The transactional conflict problem. Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18. SPAA: Symposium on Parallelism in Algorithms and Architectures, 383–392.

Aksenov V, Alistarh D-A, Kuznetsov P. 2018. Brief Announcement: Performance prediction for coarse-grained locking. Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18. PODC: Principles of Distributed Computing, 411–413.

Stojanov A, Smith TM, Alistarh D-A, Puschel M. 2018. Fast quantized arithmetic on : Trading compute for data movement. 2018 IEEE International Workshop on Signal Processing Systems. SiPS: Workshop on Signal Processing Systems vol. 2018–October, 8598402.

Alistarh D-A, Aspnes J, Gelashvili R. 2018. Space-optimal majority in population protocols. Proceedings of the 29th Annual ACM-SIAM Symposium on Discrete Algorithms. SODA: Symposium on Discrete Algorithms, 2221–2239.

Alistarh D-A, Allen-Zhu Z, Li J. 2018. Byzantine stochastic gradient descent. Advances in Neural Information Processing Systems. NeurIPS: Conference on Neural Information Processing Systems vol. 2018, 4613–4623.

Alistarh D-A, Hoefler T, Johansson M, Konstantinov NH, Khirirat S, Renggli C. 2018. The convergence of sparsified gradient methods. Advances in Neural Information Processing Systems 31. NeurIPS: Conference on Neural Information Processing Systems vol. Volume 2018, 5973–5983.

Baig G, Radunovic B, Alistarh D-A, Balkwill M, Karagiannis T, Qiu L. 2017. Towards unlicensed cellular networks in TV white spaces. Proceedings of the 2017 13th International Conference on emerging Networking EXperiments and Technologies. CoNEXT: Conference on emerging Networking EXperiments and Technologies, 2–14.

Alistarh D-A, Kopinsky J, Li J, Nadiradze G. 2017. The power of choice in priority scheduling. Proceedings of the ACM Symposium on Principles of Distributed Computing. PODC: Principles of Distributed Computing vol. Part 9314, 283–292.

Alistarh D-A, Grubic D, Li J, Tomioka R, Vojnovi M. 2017. QSGD: Communication-efficient SGD via gradient quantization and encoding. NIPS: Neural Information Processing System, Advances in Neural Information Processing Systems, vol. 2017, 1710–1721.

Zhang H, Li J, Kara K, Alistarh D-A, Liu J, Zhang C. 2017. ZipML: Training linear models with end-to-end low precision, and a little bit of deep learning. Proceedings of Machine Learning Research. ICML: International  Conference  on  Machine Learning, PMLR Press, vol. 70, 4035–4043.