Alistarh, Dan-Adrian, James Aspnes, Faith Ellen, Rati Gelashvili, and Leqi Zhu. “Brief Announcement: Why Extension-Based Proofs Fail.”https://doi.org/10.1145/3382734.3405743__In Proceedings of the 39th Symposium on Principles of Distributed Computing, 54–56. Association for Computing Machinery, 2020.https://doi.org/10.1145/3382734.3405743 .

Brandt, Sebastian, Barbara Keller, Joel Rybicki, Jukka Suomela, and Jara Uitto. “Brief Announcement: Efficient Load-Balancing through Distributed Token Dropping.”https://doi.org/10.4230/LIPIcs.DISC.2020.40__In 34th International Symposium on Distributed Computing, Vol. 179. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2020.https://doi.org/10.4230/LIPIcs.DISC.2020.40 .

Faghri, Fartash , Iman  Tabrizian, Ilia Markov, Dan-Adrian Alistarh, Daniel  Roy, and Ali  Ramezani-Kebrya. “Adaptive Gradient Quantization for Data-Parallel SGD.” In Advances in Neural Information Processing Systems, Vol. 33. Neural Information Processing Systems Foundation, 2020.

Alistarh, Dan-Adrian, Giorgi Nadiradze, and Amirmojtaba Sabour. “Dynamic Averaging Load Balancing on Cycles.”https://doi.org/10.4230/LIPIcs.ICALP.2020.7__In 47th International Colloquium on Automata, Languages, and Programming, Vol. 168. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2020.https://doi.org/10.4230/LIPIcs.ICALP.2020.7 .

Jelínek, Vít, and Martin Töpfer. “On Grounded L-Graphs and Their Relatives.”https://doi.org/10.37236/8096__Electronic Journal of Combinatorics. Electronic Journal of Combinatorics, 2019.https://doi.org/10.37236/8096 .

Nowak, Thomas, and Joel Rybicki. “Byzantine Approximate Agreement on Graphs.”https://doi.org/10.4230/LIPICS.DISC.2019.29__In 33rd International Symposium on Distributed Computing, 146:29:1--29:17. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2019.https://doi.org/10.4230/LIPICS.DISC.2019.29 .

Chatterjee, Bapi, Sathya Peri, Muktikanta Sa, and Nandini Singhal. “A Simple and Practical Concurrent Non-Blocking Unbounded Graph with Linearizable Reachability Queries.”https://doi.org/10.1145/3288599.3288617__In ACM International Conference Proceeding Series, 168–77. ACM, 2019.https://doi.org/10.1145/3288599.3288617 .

Koval, Nikita, Dan-Adrian Alistarh, and Roman Elizarov. Lock-Free Channels for Programming via Communicating Sequential Processes. Proceedings of the 24th Symposium on Principles and Practice of Parallel Programming. ACM Press, 2019.https://doi.org/10.1145/3293883.3297000 .

Ovaskainen, Otso, Joel Rybicki, and Nerea Abrego. “What Can Observational Data Reveal about Metacommunity Processes?”https://doi.org/10.1111/ecog.04444__Ecography. Wiley, 2019.https://doi.org/10.1111/ecog.04444 .

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 .