Censor-Hillel, Keren, Michal Dory, Janne Korhonen, and Dean Leitersdorf. “Fast Approximate Shortest Paths in the Congested Clique.”https://doi.org/10.1145/3293611.3331633__In Proceedings of the 2019 ACM Symposium on Principles of Distributed Computin, 74–83. ACM, 2019.https://doi.org/10.1145/3293611.3331633 .

Alistarh, Dan-Adrian, James Aspnes, Valerie King, and Jared Saia. “Communication-Efficient Randomized Consensus.”https://doi.org/10.1007/s00446-017-0315-1__Distributed Computing. Springer, 2018.https://doi.org/10.1007/s00446-017-0315-1 .

Grubic, Demjan, Leo Tam, Dan-Adrian Alistarh, and Ce Zhang. “Synchronous Multi-GPU Training for Deep Learning with Low-Precision Communications: An Empirical Study.”https://doi.org/10.5441/002/EDBT.2018.14__In Proceedings of the 21st International Conference on Extending Database Technology, 145–56. OpenProceedings, 2018.https://doi.org/10.5441/002/EDBT.2018.14 .

Alistarh, Dan-Adrian, William Leiserson, Alexander Matveev, and Nir Shavit. “ThreadScan: Automatic and Scalable Memory Reclamation.”https://doi.org/10.1145/3201897__ACM Transactions on Parallel Computing. Association for Computing Machinery, 2018.https://doi.org/10.1145/3201897 .

Polino, Antonio, Razvan Pascanu, and Dan-Adrian Alistarh. “Model Compression via Distillation and Quantization.” In 6th International Conference on Learning Representations, 2018.

Arbel Raviv, Maya, and Trevor A Brown. “Harnessing Epoch-Based Reclamation for Efficient Range Queries,”https://doi.org/10.1145/3178487.3178489__53:14–27. ACM, 2018.https://doi.org/10.1145/3178487.3178489 .

Rybicki, Joel, Eva Kisdi, and Jani Anttila. “Model of Bacterial Toxin-Dependent Pathogenesis Explains Infective Dose.”https://doi.org/10.1073/pnas.1721061115__PNAS. National Academy of Sciences, 2018.https://doi.org/10.1073/pnas.1721061115 .

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

Gilad, Eran, Trevor A Brown, Mark Oskin, and Yoav Etsion. “Snapshot Based Synchronization: A Fast Replacement for Hand-over-Hand Locking,”https://doi.org/10.1007/978-3-319-96983-1_33__11014:465–79. Springer, 2018.https://doi.org/10.1007/978-3-319-96983-1_33 .

Alistarh, Dan-Adrian, Christopher De Sa, and Nikola H Konstantinov. “The Convergence of Stochastic Gradient Descent in Asynchronous Shared Memory.”https://doi.org/10.1145/3212734.3212763__In Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, 169–78. ACM Press, 2018.https://doi.org/10.1145/3212734.3212763 .

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

Alistarh, Dan-Adrian, Trevor A Brown, Justin Kopinsky, and Giorgi Nadiradze. “Relaxed Schedulers Can Efficiently Parallelize Iterative Algorithms.”https://doi.org/10.1145/3212734.3212756__In Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, 377–86. ACM Press, 2018.https://doi.org/10.1145/3212734.3212756 .

Alistarh, Dan-Adrian, Trevor A Brown, Justin Kopinsky, Jerry Z. Li, and Giorgi Nadiradze. “Distributionally Linearizable Data Structures.”https://doi.org/10.1145/3210377.3210411__In Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18, 133–42. ACM Press, 2018.https://doi.org/10.1145/3210377.3210411 .

Alistarh, Dan-Adrian, Syed Kamran Haider, Raphael Kübler, and Giorgi Nadiradze. “The Transactional Conflict Problem.”https://doi.org/10.1145/3210377.3210406__In Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18, 383–92. ACM Press, 2018.https://doi.org/10.1145/3210377.3210406 .

Aksenov, Vitaly, Dan-Adrian Alistarh, and Petr Kuznetsov. “Brief Announcement: Performance Prediction for Coarse-Grained Locking.”https://doi.org/10.1145/3212734.3212785__In Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, 411–13. ACM Press, 2018.https://doi.org/10.1145/3212734.3212785 .

Stojanov, Alen, Tyler Michael Smith, Dan-Adrian Alistarh, and Markus Puschel. “Fast Quantized Arithmetic on : Trading Compute for Data Movement.”https://doi.org/10.1109/SiPS.2018.8598402__In 2018 IEEE International Workshop on Signal Processing Systems, Vol. 2018–October. IEEE, 2018.https://doi.org/10.1109/SiPS.2018.8598402 .

Alistarh, Dan-Adrian, James Aspnes, and Rati Gelashvili. “Space-Optimal Majority in Population Protocols.”https://doi.org/10.1137/1.9781611975031.144__In Proceedings of the 29th Annual ACM-SIAM Symposium on Discrete Algorithms, 2221–39. ACM, 2018.https://doi.org/10.1137/1.9781611975031.144 .

Alistarh, Dan-Adrian, Zeyuan Allen-Zhu, and Jerry Li. “Byzantine Stochastic Gradient Descent.” In Advances in Neural Information Processing Systems, 2018:4613–23. Neural Information Processing Systems Foundation, 2018.

Alistarh, Dan-Adrian, Torsten Hoefler, Mikael Johansson, Nikola H Konstantinov, Sarit Khirirat, and Cedric Renggli. “The Convergence of Sparsified Gradient Methods.” In Advances in Neural Information Processing Systems 31, Volume 2018:5973–83. Neural Information Processing Systems Foundation, 2018.

Baig, Ghufran, Bozidar Radunovic, Dan-Adrian Alistarh, Matthew Balkwill, Thomas Karagiannis, and Lili Qiu. “Towards Unlicensed Cellular Networks in TV White Spaces.”https://doi.org/10.1145/3143361.3143367__In Proceedings of the 2017 13th International Conference on Emerging Networking EXperiments and Technologies, 2–14. ACM, 2017.https://doi.org/10.1145/3143361.3143367 .