Alistarh, D.-A., Nadiradze, G., &; Sabour, A. (2022). Dynamic averaging load balancing on cycles. Algorithmica. Virtual, Online; Germany: Springer Nature.https://doi.org/10.1007/s00453-021-00905-9 

Hoefler, T., Alistarh, D.-A., Ben-Nun, T., Dryden, N., &; Peste, E.-A. (2021). Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks. Journal of Machine Learning Research. Journal of Machine Learning Research.

Alistarh, D.-A., Gelashvili, R., &; Rybicki, J. (2021). Brief announcement: Fast graphical population protocols. In 35th International Symposium on Distributed Computing (Vol. 209). Freiburg, Germany: Schloss Dagstuhl - Leibniz-Zentrum für Informatik.https://doi.org/10.4230/LIPIcs.DISC.2021.43 

Alistarh, D.-A., Gelashvili, R., &; Nadiradze, G. (2021). Lower bounds for shared-memory leader election under bounded write contention. In 35th International Symposium on Distributed Computing (Vol. 209). Freiburg, Germany: Schloss Dagstuhl - Leibniz Zentrum für Informatik.https://doi.org/10.4230/LIPIcs.DISC.2021.4 

Chatterjee, B., Peri, S., &; Sa, M. (2021). Brief announcement: Non-blocking dynamic unbounded graphs with worst-case amortized bounds. In 35th International Symposium on Distributed Computing (Vol. 209). Freiburg, Germany: Schloss Dagstuhl - Leibniz Zentrum für Informatik.https://doi.org/10.4230/LIPIcs.DISC.2021.52 

Korhonen, J., Paz, A., Rybicki, J., Schmid, S., &; Suomela, J. (2021). Brief announcement: Sinkless orientation is hard also in the supported LOCAL model. In 35th International Symposium on Distributed Computing (Vol. 209). Freiburg, Germany: Schloss Dagstuhl - Leibniz Zentrum für Informatik.https://doi.org/10.4230/LIPIcs.DISC.2021.58 

Fedorov, A., Koval, N., &; Alistarh, D.-A. (2021). A scalable concurrent algorithm for dynamic connectivity. In Proceedings of the 33rd ACM Symposium on Parallelism in Algorithms and Architectures (pp. 208–220). Virtual, Online: Association for Computing Machinery.https://doi.org/10.1145/3409964.3461810 

Kungurtsev, V., Egan, M., Chatterjee, B., &; Alistarh, D.-A. (2021). Asynchronous optimization methods for efficient training of deep neural networks with guarantees. In 35th AAAI Conference on Artificial Intelligence, AAAI 2021 (Vol. 35, pp. 8209–8216). Virtual, Online: AAAI Press.

Alimisis, F., Davies, P., Vandereycken, B., &; Alistarh, D.-A. (2021). Distributed principal component analysis with limited communication. In Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems (Vol. 4, pp. 2823–2834). Virtual, Online: Neural Information Processing Systems Foundation.

Frantar, E., Kurtic, E., &; Alistarh, D.-A. (2021). M-FAC: Efficient matrix-free approximations of second-order information. In 35th Conference on Neural Information Processing Systems (Vol. 34, pp. 14873–14886). Virtual, Online: Curran Associates.

Alistarh, D.-A., &; Korhonen, J. (2021). Towards tight communication lower bounds for distributed optimisation. In 35th Conference on Neural Information Processing Systems (Vol. 34, pp. 7254–7266). Virtual, Online: Curran Associates.

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.

Alistarh, D.-A., &; Davies, P. (2021). Collecting coupons is faster with friends. In Structural Information and Communication Complexity (Vol. 12810, pp. 3–12). Wrocaw, Poland: Springer Nature.https://doi.org/10.1007/978-3-030-79527-6_1 

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 

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.

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.

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 

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 

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 

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