Near-optimal leader election in population protocols on graphs
D.-A. Alistarh, J. Rybicki, S. Voitovych, in:, Proceedings of the Annual ACM Symposium on Principles of Distributed Computing, Association for Computing Machinery, 2022, pp. 246–256.

PathCAS: An efficient middle ground for concurrent search data structures
T.A. Brown, W. Sigouin, D.-A. Alistarh, in:, Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, Association for Computing Machinery, 2022, pp. 385–399.

Multi-queues can be state-of-the-art priority schedulers
A. Postnikova, N. Koval, G. Nadiradze, D.-A. Alistarh, in:, Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, Association for Computing Machinery, 2022, pp. 353–367.

Multi-queues can be state-of-the-art priority schedulers
A. Postnikova, N. Koval, G. Nadiradze, D.-A. Alistarh, (2022).

Local mending
A. Balliu, J. Hirvonen, D. Melnyk, D. Olivetti, J. Rybicki, J. Suomela, in:, M. Parter (Ed.), International Colloquium on Structural Information and Communication Complexity, Springer Nature, 2022, pp. 1–20.

How well do sparse ImageNet models transfer?
E.B. Iofinova, E.-A. Peste, M. Kurtz, D.-A. Alistarh, in:, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Institute of Electrical and Electronics Engineers, 2022, pp. 12256–12266.

Dynamic averaging load balancing on cycles
D.-A. Alistarh, G. Nadiradze, A. Sabour, Algorithmica 84 (2022) 1007–1029.

Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks
T. Hoefler, D.-A. Alistarh, T. Ben-Nun, N. Dryden, E.-A. Peste, Journal of Machine Learning Research 22 (2021) 1–124.

Brief announcement: Fast graphical population protocols
D.-A. Alistarh, R. Gelashvili, J. Rybicki, in:, 35th International Symposium on Distributed Computing, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2021.

Lower bounds for shared-memory leader election under bounded write contention
D.-A. Alistarh, R. Gelashvili, G. Nadiradze, in:, 35th International Symposium on Distributed Computing, Schloss Dagstuhl - Leibniz Zentrum für Informatik, 2021.

Brief announcement: Non-blocking dynamic unbounded graphs with worst-case amortized bounds
B. Chatterjee, S. Peri, M. Sa, in:, 35th International Symposium on Distributed Computing, Schloss Dagstuhl - Leibniz Zentrum für Informatik, 2021.

Brief announcement: Sinkless orientation is hard also in the supported LOCAL model
J. Korhonen, A. Paz, J. Rybicki, S. Schmid, J. Suomela, in:, 35th International Symposium on Distributed Computing, Schloss Dagstuhl - Leibniz Zentrum für Informatik, 2021.

A scalable concurrent algorithm for dynamic connectivity
A. Fedorov, N. Koval, D.-A. Alistarh, in:, Proceedings of the 33rd ACM Symposium on Parallelism in Algorithms and Architectures, Association for Computing Machinery, 2021, pp. 208–220.

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

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

M-FAC: Efficient matrix-free approximations of second-order information
E. Frantar, E. Kurtic, D.-A. Alistarh, in:, 35th Conference on Neural Information Processing Systems, Curran Associates, 2021, pp. 14873–14886.

Towards tight communication lower bounds for distributed optimisation
D.-A. Alistarh, J. Korhonen, in:, 35th Conference on Neural Information Processing Systems, Curran Associates, 2021, pp. 7254–7266.

New bounds for distributed mean estimation and variance reduction
P. Davies, V. Gurunanthan, N. Moshrefi, S. Ashkboos, D.-A. Alistarh, in:, 9th International Conference on Learning Representations, 2021.

Collecting coupons is faster with friends
D.-A. Alistarh, P. Davies, in:, Structural Information and Communication Complexity, Springer Nature, 2021, pp. 3–12.

Wait-free approximate agreement on graphs
D.-A. Alistarh, F. Ellen, J. Rybicki, in:, Structural Information and Communication Complexity, Springer Nature, 2021, pp. 87–105.