Brief Announcement: Why Extension-Based Proofs Fail
D.-A. Alistarh, J. Aspnes, F. Ellen, R. Gelashvili, L. Zhu, in:, Proceedings of the 39th Symposium on Principles of Distributed Computing, Association for Computing Machinery, 2020, pp. 54–56.

Brief announcement: Efficient load-balancing through distributed token dropping
S. Brandt, B. Keller, J. Rybicki, J. Suomela, J. Uitto, in:, 34th International Symposium on Distributed Computing, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2020.

Adaptive gradient quantization for data-parallel SGD
F. Faghri, I. Tabrizian, I. Markov, D.-A. Alistarh, D. Roy, A. Ramezani-Kebrya, in:, Advances in Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2020.

Dynamic averaging load balancing on cycles
D.-A. Alistarh, G. Nadiradze, A. Sabour, in:, 47th International Colloquium on Automata, Languages, and Programming, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2020.

On grounded L-graphs and their relatives
V. Jelínek, M. Töpfer, Electronic Journal of Combinatorics 26 (2019).

Byzantine approximate agreement on graphs
T. Nowak, J. Rybicki, in:, 33rd International Symposium on Distributed Computing, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2019, p. 29:1--29:17.

A simple and practical concurrent non-blocking unbounded graph with linearizable reachability queries
B. Chatterjee, S. Peri, M. Sa, N. Singhal, in:, ACM International Conference Proceeding Series, ACM, 2019, pp. 168–177.

Lock-free channels for programming via communicating sequential processes
N. Koval, D.-A. Alistarh, R. Elizarov, Lock-Free Channels for Programming via Communicating Sequential Processes, ACM Press, 2019.

What can observational data reveal about metacommunity processes?
O. Ovaskainen, J. Rybicki, N. Abrego, Ecography 42 (2019) 1877–1886.

Self-stabilising Byzantine clock synchronisation is almost as easy as consensus
C. Lenzen, J. Rybicki, Journal of the ACM 66 (2019).

Gradient compression for communication-limited convex optimization
S. Khirirat, M. Johansson, D.-A. Alistarh, in:, 2018 IEEE Conference on Decision and Control, IEEE, 2019.

SparCML: High-performance sparse communication for machine learning
C. Renggli, S. Ashkboos, M. Aghagolzadeh, D.-A. Alistarh, T. Hoefler, in:, International Conference for High Performance Computing, Networking, Storage and Analysis, SC, ACM, 2019.

Recovering rearranged cancer chromosomes from karyotype graphs
S. Aganezov, I. Zban, V. Aksenov, N. Alexeev, M.C. Schatz, BMC Bioinformatics 20 (2019).

Scalable FIFO channels for programming via communicating sequential processes
N. Koval, D.-A. Alistarh, R. Elizarov, in:, 25th Anniversary of Euro-Par, Springer Nature, 2019, pp. 317–333.

Distributed learning over unreliable networks
C. Yu, H. Tang, C. Renggli, S. Kassing, A. Singla, D.-A. Alistarh, C. Zhang, J. Liu, in:, 36th International Conference on Machine Learning, ICML 2019, IMLS, 2019, pp. 12481–12512.

Efficiency guarantees for parallel incremental algorithms under relaxed schedulers
D.-A. Alistarh, G. Nadiradze, N. Koval, in:, 31st ACM Symposium on Parallelism in Algorithms and Architectures, ACM Press, 2019, pp. 145–154.

Powerset convolutional neural networks
C. Wendler, D.-A. Alistarh, M. Püschel, in:, Neural Information Processing Systems Foundation, 2019, pp. 927–938.

Does preprocessing help under congestion?
K.-T. Foerster, J. Korhonen, J. Rybicki, S. Schmid, in:, Proceedings of the 2019 ACM Symposium on Principles of Distributed Computing, ACM, 2019, pp. 259–261.

Why extension-based proofs fail
D.-A. Alistarh, J. Aspnes, F. Ellen, R. Gelashvili, L. Zhu, in:, Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing, ACM Press, 2019, pp. 986–996.

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