Lenzen, Christoph, and Joel Rybicki. “Self-Stabilising Byzantine Clock Synchronisation Is Almost as Easy as Consensus.”https://doi.org/10.1145/3339471__Journal of the ACM. ACM, 2019.https://doi.org/10.1145/3339471 .

Khirirat, Sarit, Mikael Johansson, and Dan-Adrian Alistarh. “Gradient Compression for Communication-Limited Convex Optimization.”https://doi.org/10.1109/cdc.2018.8619625__In 2018 IEEE Conference on Decision and Control. IEEE, 2019.https://doi.org/10.1109/cdc.2018.8619625 .

Renggli, Cedric, Saleh Ashkboos, Mehdi Aghagolzadeh, Dan-Adrian Alistarh, and Torsten Hoefler. “SparCML: High-Performance Sparse Communication for Machine Learning.”https://doi.org/10.1145/3295500.3356222__In International Conference for High Performance Computing, Networking, Storage and Analysis, SC. ACM, 2019.https://doi.org/10.1145/3295500.3356222 .

Aganezov, Sergey, Ilya Zban, Vitalii Aksenov, Nikita Alexeev, and Michael C. Schatz. “Recovering Rearranged Cancer Chromosomes from Karyotype Graphs.”https://doi.org/10.1186/s12859-019-3208-4__BMC Bioinformatics. BMC, 2019.https://doi.org/10.1186/s12859-019-3208-4 .

Koval, Nikita, Dan-Adrian Alistarh, and Roman Elizarov. “Scalable FIFO Channels for Programming via Communicating Sequential Processes.”https://doi.org/10.1007/978-3-030-29400-7_23__In 25th Anniversary of Euro-Par, 11725:317–33. Springer Nature, 2019.https://doi.org/10.1007/978-3-030-29400-7_23 .

Yu, Chen, Hanlin Tang, Cedric Renggli, Simon Kassing, Ankit Singla, Dan-Adrian Alistarh, Ce Zhang, and Ji Liu. “Distributed Learning over Unreliable Networks.” In 36th International Conference on Machine Learning, ICML 2019, 2019–June:12481–512. IMLS, 2019.

Alistarh, Dan-Adrian, Giorgi Nadiradze, and Nikita Koval. “Efficiency Guarantees for Parallel Incremental Algorithms under Relaxed Schedulers.”https://doi.org/10.1145/3323165.3323201__In 31st ACM Symposium on Parallelism in Algorithms and Architectures, 145–54. ACM Press, 2019.https://doi.org/10.1145/3323165.3323201 .

Wendler, Chris, Dan-Adrian Alistarh, and Markus Püschel. “Powerset Convolutional Neural Networks,” 32:927–38. Neural Information Processing Systems Foundation, 2019.

Foerster, Klaus-Tycho, Janne Korhonen, Joel Rybicki, and Stefan Schmid. “Does Preprocessing Help under Congestion?”https://doi.org/10.1145/3293611.3331581__In Proceedings of the 2019 ACM Symposium on Principles of Distributed Computing, 259–61. ACM, 2019.https://doi.org/10.1145/3293611.3331581 .

Alistarh, Dan-Adrian, James Aspnes, Faith Ellen, Rati Gelashvili, and Leqi Zhu. “Why Extension-Based Proofs Fail.”https://doi.org/10.1145/3313276.3316407__In Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing, 986–96. ACM Press, 2019.https://doi.org/10.1145/3313276.3316407 .

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 .