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Davies, P., Gurunanthan, V., Moshrefi, N., Ashkboos, S., &; Alistarh, D.-A. (2021). New bounds for distributed mean estimation and variance reduction. In 9th International Conference on Learning Representations. Virtual.
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Alistarh, D.-A., Ellen, F., &; Rybicki, J. (2021). Wait-free approximate agreement on graphs. In Structural Information and Communication Complexity (Vol. 12810, pp. 87–105). Wrocaw, Poland: Springer Nature.https://doi.org/10.1007/978-3-030-79527-6_6 
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Peste, E.-A., Iofinova, E. B., Vladu, A., &; Alistarh, D.-A. (2021). AC/DC: Alternating Compressed/DeCompressed training of deep neural networks. In 35th Conference on Neural Information Processing Systems (Vol. 34, pp. 8557–8570). Virtual, Online: Curran Associates.
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Alimisis, F., Davies, P., &; Alistarh, D.-A. (2021). Communication-efficient distributed optimization with quantized preconditioners. In Proceedings of the 38th International Conference on Machine Learning (Vol. 139, pp. 196–206). Virtual: ML Research Press.
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Li, S., Tal Ben-Nun, T. B.-N., Nadiradze, G., Girolamo, S. D., Dryden, N., Alistarh, D.-A., &; Hoefler, T. (2021). Breaking (global) barriers in parallel stochastic optimization with wait-avoiding group averaging. IEEE Transactions on Parallel and Distributed Systems. IEEE.https://doi.org/10.1109/TPDS.2020.3040606 
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Chatterjee, B., Walulya, I., &; Tsigas, P. (2021). Concurrent linearizable nearest neighbour search in LockFree-kD-tree. Theoretical Computer Science. Elsevier.https://doi.org/10.1016/j.tcs.2021.06.041 
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Alistarh, D.-A., Töpfer, M., &; Uznaski, P. (2021). Comparison dynamics in population protocols. In Proceedings of the 2021 ACM Symposium on Principles of Distributed Computing (pp. 55–65). Virtual, Italy: Association for Computing Machinery.https://doi.org/10.1145/3465084.3467915 
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Czumaj, A., Davies, P., &; Parter, M. (2021). Improved deterministic (+1) coloring in low-space MPC. In Proceedings of the 2021 ACM Symposium on Principles of Distributed Computing (pp. 469–479). Virtual, Italy: Association for Computing Machinery.https://doi.org/10.1145/3465084.3467937 
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Czumaj, A., Davies, P., &; Parter, M. (2021). Component stability in low-space massively parallel computation. In Proceedings of the 2021 ACM Symposium on Principles of Distributed Computing (pp. 481–491). Virtual, Italy: Association for Computing Machinery.https://doi.org/10.1145/3465084.3467903 
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Nadiradze, G., Markov, I., Chatterjee, B., Kungurtsev, V., &; Alistarh, D.-A. (2021). Elastic consistency: A practical consistency model for distributed stochastic gradient descent. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, pp. 9037–9045). Virtual.
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Klein, K., Pascual Perez, G., Walter, M., Kamath Hosdurg, C., Capretto, M., Cueto Noval, M.,https://doi.org/10.1109/sp40001.2021.00035__Pietrzak, K. Z. (2021). Keep the dirt: tainted TreeKEM, adaptively and actively secure continuous group key agreement. In 2021 IEEE Symposium on Security and Privacy  (pp. 268–284). San Francisco, CA, United States: IEEE.https://doi.org/10.1109/sp40001.2021.00035 
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Foerster, K.-T., Korhonen, J., Paz, A., Rybicki, J., &; Schmid, S. (2021). Input-dynamic distributed algorithms for communication networks. In Abstract Proceedings of the 2021 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems (pp. 71–72). Virtual, Online: Association for Computing Machinery.https://doi.org/10.1145/3410220.3453923 
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Foerster, K.-T., Korhonen, J., Paz, A., Rybicki, J., &; Schmid, S. (2021). Input-dynamic distributed algorithms for communication networks. Proceedings of the ACM on Measurement and Analysis of Computing Systems. Association for Computing Machinery.https://doi.org/10.1145/3447384 
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Nadiradze, G. (2021). On achieving scalability through relaxation. Institute of Science and Technology Austria.https://doi.org/10.15479/at:ista:10429 
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Nadiradze, G., Sabour, A., Davies, P., Li, S., &; Alistarh, D.-A. (2021). Asynchronous decentralized SGD with quantized and local updates. In 35th Conference on Neural Information Processing Systems. Sydney, Australia: Neural Information Processing Systems Foundation.
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Czumaj, A., Davies, P., &; Parter, M. (2021). Graph sparsification for derandomizing massively parallel computation with low space. ACM Transactions on Algorithms. Association for Computing Machinery.https://doi.org/10.1145/3451992 
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Brandt, S., Keller, B., Rybicki, J., Suomela, J., &; Uitto, J. (2021). Efficient load-balancing through distributed token dropping. In Annual ACM Symposium on Parallelism in Algorithms and Architectures (pp. 129–139).https://doi.org/10.1145/3409964.3461785__Virtual Event, United States.https://doi.org/10.1145/3409964.3461785 
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Ramezani-Kebrya, A., Faghri, F., Markov, I., Aksenov, V., Alistarh, D.-A., &; Roy, D. M. (2021). NUQSGD: Provably communication-efficient data-parallel SGD via nonuniform quantization. Journal of Machine Learning Research. Journal of Machine Learning Research.
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