Censor-Hillel, Keren, et al. “Fast Approximate Shortest Paths in the Congested Clique.”10.1145/3293611.3331633__Proceedings of the 2019 ACM Symposium on Principles of Distributed Computin, ACM, 2019, pp. 74–83, doi:10.1145/3293611.3331633.

Alistarh, Dan-Adrian, et al. “Communication-Efficient Randomized Consensus.”10.1007/s00446-017-0315-1__Distributed Computing, vol. 31, no. 6, Springer, 2018, pp. 489–501, doi:10.1007/s00446-017-0315-1.

Grubic, Demjan, et al. “Synchronous Multi-GPU Training for Deep Learning with Low-Precision Communications: An Empirical Study.”10.5441/002/EDBT.2018.14__Proceedings of the 21st International Conference on Extending Database Technology, OpenProceedings, 2018, pp. 145–56, doi:10.5441/002/EDBT.2018.14.

Alistarh, Dan-Adrian, et al. “ThreadScan: Automatic and Scalable Memory Reclamation.”10.1145/3201897__ACM Transactions on Parallel Computing, vol. 4, no. 4, 18, Association for Computing Machinery, 2018, doi:10.1145/3201897.

Polino, Antonio, et al. “Model Compression via Distillation and Quantization.” 6th International Conference on Learning Representations, 2018.

Arbel Raviv, Maya, and Trevor A. Brown. Harnessing Epoch-Based Reclamation for Efficient Range Queries. Vol. 53, no. 1, ACM, 2018, pp. 14–27, doi:10.1145/3178487.3178489.

Rybicki, Joel, et al. “Model of Bacterial Toxin-Dependent Pathogenesis Explains Infective Dose.”10.1073/pnas.1721061115__PNAS, vol. 115, no. 42, National Academy of Sciences, 2018, pp. 10690–95, doi:10.1073/pnas.1721061115.

Lenzen, Christoph, and Joel Rybicki. “Near-Optimal Self-Stabilising Counting and Firing Squads.”10.1007/s00446-018-0342-6__Distributed Computing, Springer, 2018, doi:10.1007/s00446-018-0342-6.

Gilad, Eran, et al. Snapshot Based Synchronization: A Fast Replacement for Hand-over-Hand Locking. Vol. 11014, Springer, 2018, pp. 465–79, doi:10.1007/978-3-319-96983-1_33.

Alistarh, Dan-Adrian, et al. “The Convergence of Stochastic Gradient Descent in Asynchronous Shared Memory.”10.1145/3212734.3212763__Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, ACM Press, 2018, pp. 169–78, doi:10.1145/3212734.3212763.

Alistarh, Dan-Adrian. “A Brief Tutorial on Distributed and Concurrent Machine Learning.”10.1145/3212734.3212798__Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, ACM Press, 2018, pp. 487–88, doi:10.1145/3212734.3212798.

Alistarh, Dan-Adrian, et al. “Relaxed Schedulers Can Efficiently Parallelize Iterative Algorithms.”10.1145/3212734.3212756__Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, ACM Press, 2018, pp. 377–86, doi:10.1145/3212734.3212756.

Alistarh, Dan-Adrian, et al. “Distributionally Linearizable Data Structures.”10.1145/3210377.3210411__Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18, ACM Press, 2018, pp. 133–42, doi:10.1145/3210377.3210411.

Alistarh, Dan-Adrian, et al. “The Transactional Conflict Problem.”10.1145/3210377.3210406__Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18, ACM Press, 2018, pp. 383–92, doi:10.1145/3210377.3210406.

Aksenov, Vitaly, et al. “Brief Announcement: Performance Prediction for Coarse-Grained Locking.”10.1145/3212734.3212785__Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, ACM Press, 2018, pp. 411–13, doi:10.1145/3212734.3212785.

Stojanov, Alen, et al. “Fast Quantized Arithmetic on : Trading Compute for Data Movement.”10.1109/SiPS.2018.8598402__2018 IEEE International Workshop on Signal Processing Systems, vol. 2018–October, 8598402, IEEE, 2018, doi:10.1109/SiPS.2018.8598402.

Alistarh, Dan-Adrian, et al. “Space-Optimal Majority in Population Protocols.”10.1137/1.9781611975031.144__Proceedings of the 29th Annual ACM-SIAM Symposium on Discrete Algorithms, ACM, 2018, pp. 2221–39, doi:10.1137/1.9781611975031.144.

Alistarh, Dan-Adrian, et al. “Byzantine Stochastic Gradient Descent.” Advances in Neural Information Processing Systems, vol. 2018, Neural Information Processing Systems Foundation, 2018, pp. 4613–23.

Alistarh, Dan-Adrian, et al. “The Convergence of Sparsified Gradient Methods.” Advances in Neural Information Processing Systems 31, vol. Volume 2018, Neural Information Processing Systems Foundation, 2018, pp. 5973–83.

Baig, Ghufran, et al. “Towards Unlicensed Cellular Networks in TV White Spaces.”10.1145/3143361.3143367__Proceedings of the 2017 13th International Conference on Emerging Networking EXperiments and Technologies, ACM, 2017, pp. 2–14, doi:10.1145/3143361.3143367.