Alimisis, Foivos, et al. “Communication-Efficient Distributed Optimization with Quantized Preconditioners.” Proceedings of the 38th International Conference on Machine Learning, vol. 139, ML Research Press, 2021, pp. 196–206.

Li, Shigang, et al. “Breaking (Global) Barriers in Parallel Stochastic Optimization with Wait-Avoiding Group Averaging.”10.1109/TPDS.2020.3040606__IEEE Transactions on Parallel and Distributed Systems, vol. 32, no. 7, 9271898, IEEE, 2021, doi:10.1109/TPDS.2020.3040606.

Chatterjee, Bapi, et al. “Concurrent Linearizable Nearest Neighbour Search in LockFree-KD-Tree.”10.1016/j.tcs.2021.06.041__Theoretical Computer Science, vol. 886, Elsevier, 2021, pp. 27–48, doi:10.1016/j.tcs.2021.06.041.

Alistarh, Dan-Adrian, et al. “Comparison Dynamics in Population Protocols.”10.1145/3465084.3467915__Proceedings of the 2021 ACM Symposium on Principles of Distributed Computing, Association for Computing Machinery, 2021, pp. 55–65, doi:10.1145/3465084.3467915.

Czumaj, Artur, et al. “Improved Deterministic (+1) Coloring in Low-Space MPC.”10.1145/3465084.3467937__Proceedings of the 2021 ACM Symposium on Principles of Distributed Computing, Association for Computing Machinery, 2021, pp. 469–479, doi:10.1145/3465084.3467937.

Czumaj, Artur, et al. “Component Stability in Low-Space Massively Parallel Computation.”10.1145/3465084.3467903__Proceedings of the 2021 ACM Symposium on Principles of Distributed Computing, Association for Computing Machinery, 2021, pp. 481–491, doi:10.1145/3465084.3467903.

Nadiradze, Giorgi, et al. “Elastic Consistency: A Practical Consistency Model for Distributed Stochastic Gradient Descent.” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 10, 2021, pp. 9037–45.

Klein, Karen, et al. “Keep the Dirt: Tainted TreeKEM, Adaptively and Actively Secure Continuous Group Key Agreement.”10.1109/sp40001.2021.00035__2021 IEEE Symposium on Security and Privacy , IEEE, 2021, pp. 268–84, doi:10.1109/sp40001.2021.00035.

Foerster, Klaus-Tycho, et al. “Input-Dynamic Distributed Algorithms for Communication Networks.”10.1145/3410220.3453923__Abstract Proceedings of the 2021 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems, Association for Computing Machinery, 2021, pp. 71–72, doi:10.1145/3410220.3453923.

Foerster, Klaus-Tycho, et al. “Input-Dynamic Distributed Algorithms for Communication Networks.”10.1145/3447384__Proceedings of the ACM on Measurement and Analysis of Computing Systems, vol. 5, no. 1, Association for Computing Machinery, 2021, pp. 1–33, doi:10.1145/3447384.

Nadiradze, Giorgi. On Achieving Scalability through Relaxation. Institute of Science and Technology Austria, 2021, doi:10.15479/at:ista:10429.

Nadiradze, Giorgi, et al. “Asynchronous Decentralized SGD with Quantized and Local Updates.” 35th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2021.

Czumaj, Artur, et al. “Graph Sparsification for Derandomizing Massively Parallel Computation with Low Space.”10.1145/3451992__ACM Transactions on Algorithms, vol. 17, no. 2, 16, Association for Computing Machinery, 2021, doi:10.1145/3451992.

Brandt, Sebastian, et al. “Efficient Load-Balancing through Distributed Token Dropping.”10.1145/3409964.3461785__Annual ACM Symposium on Parallelism in Algorithms and Architectures, 2021, pp. 129–39, doi:10.1145/3409964.3461785.

Ramezani-Kebrya, Ali, et al. “NUQSGD: Provably Communication-Efficient Data-Parallel SGD via Nonuniform Quantization.” Journal of Machine Learning Research, vol. 22, no. 114, Journal of Machine Learning Research, 2021, p. 143.

Censor-Hillel, Keren, et al. “Fast Approximate Shortest Paths in the Congested Clique.”10.1007/s00446-020-00380-5__Distributed Computing, vol. 34, Springer Nature, 2021, pp. 463–87, doi:10.1007/s00446-020-00380-5.

Czumaj, Artur, et al. “Simple, Deterministic, Constant-Round Coloring in Congested Clique and MPC.”10.1137/20m1366502__SIAM Journal on Computing, vol. 50, no. 5, Society for Industrial &; Applied Mathematics, 2021, pp. 1603–26, doi:10.1137/20m1366502.

Czumaj, Artur, and Peter Davies. “Exploiting Spontaneous Transmissions for Broadcasting and Leader Election in Radio Networks.”10.1145/3446383__Journal of the ACM, vol. 68, no. 2, 13, Association for Computing Machinery, 2021, doi:10.1145/3446383.

Alimisis, Foivos, et al. “Momentum Improves Optimization on Riemannian Manifolds.” Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, vol. 130, ML Research Press, 2021, pp. 1351–59.

Arbel-Raviv, Maya, et al. “Getting to the Root of Concurrent Binary Search Tree Performance.” Proceedings of the 2018 USENIX Annual Technical Conference, USENIX Association, 2020, pp. 295–306.