Nadiradze G. On achieving scalability through relaxation. 2021. doi:10.15479/at:ista:10429

Nadiradze G, Sabour A, Davies P, Li S, Alistarh D-A. Asynchronous decentralized SGD with quantized and local updates. In: 35th Conference on Neural Information Processing Systems. Neural Information Processing Systems Foundation; 2021.

Czumaj A, Davies P, Parter M. Graph sparsification for derandomizing massively parallel computation with low space. ACM Transactions on Algorithms. 2021;17(2). doi:10.1145/3451992

Brandt S, Keller B, Rybicki J, Suomela J, Uitto J. Efficient load-balancing through distributed token dropping. In: Annual ACM Symposium on Parallelism in Algorithms and Architectures. ; 2021:129-139. doi:10.1145/3409964.3461785

Ramezani-Kebrya A, Faghri F, Markov I, Aksenov V, Alistarh D-A, Roy DM. NUQSGD: Provably communication-efficient data-parallel SGD via nonuniform quantization. Journal of Machine Learning Research. 2021;22(114):143.

Censor-Hillel K, Dory M, Korhonen J, Leitersdorf D. Fast approximate shortest paths in the congested clique. Distributed Computing. 2021;34:463-487. doi:10.1007/s00446-020-00380-5

Czumaj A, Davies P, Parter M. Simple, deterministic, constant-round coloring in congested clique and MPC. SIAM Journal on Computing. 2021;50(5):1603-1626. doi:10.1137/20m1366502

Czumaj A, Davies P. Exploiting spontaneous transmissions for broadcasting and leader election in radio networks. Journal of the ACM. 2021;68(2). doi:10.1145/3446383

Alimisis F, Orvieto A, Becigneul G, Lucchi A. Momentum improves optimization on Riemannian manifolds. In: Proceedings of the 24th International Conference on Artificial Intelligence and Statistics. Vol 130. ML Research Press; 2021:1351-1359.

Arbel-Raviv M, Brown TA, Morrison A. Getting to the root of concurrent binary search tree performance. In: Proceedings of the 2018 USENIX Annual Technical Conference. USENIX Association; 2020:295-306.

Alistarh D-A, Fedorov A, Koval N. In search of the fastest concurrent union-find algorithm. In: 23rd International Conference on Principles of Distributed Systems. Vol 153. Schloss Dagstuhl - Leibniz-Zentrum für Informatik; 2020:15:1-15:16. doi:10.4230/LIPIcs.OPODIS.2019.15

Czumaj A, Davies P, Parter M. Simple, deterministic, constant-round coloring in the congested clique. In: Proceedings of the 2020 ACM Symposium on Principles of Distributed Computing. Association for Computing Machinery; 2020:309-318. doi:10.1145/3382734.3405751

Aksenov V, Alistarh D-A, Drozdova A, Mohtashami A. The splay-list: A distribution-adaptive concurrent skip-list. In: 34th International Symposium on Distributed Computing. Vol 179. LIPIcs. Schloss Dagstuhl - Leibniz-Zentrum für Informatik; 2020:3:1-3:18. doi:10.4230/LIPIcs.DISC.2020.3

Singh SP, Alistarh D-A. WoodFisher: Efficient second-order approximation for neural network compression. In: Advances in Neural Information Processing Systems. Vol 33. Curran Associates; 2020:18098-18109.

Aksenov V, Alistarh D-A, Korhonen J. Scalable belief propagation via relaxed scheduling. In: Advances in Neural Information Processing Systems. Vol 33. Curran Associates; 2020:22361-22372.

Kurtz M, Kopinsky J, Gelashvili R, et al. Inducing and exploiting activation sparsity for fast neural network inference. In: 37th International Conference on Machine Learning, ICML 2020. Vol 119. ; 2020:5533-5543.

Gurel NM, Kara K, Stojanov A, et al. Compressive sensing using iterative hard thresholding with low precision data representation: Theory and applications. IEEE Transactions on Signal Processing. 2020;68:4268-4282. doi:10.1109/TSP.2020.3010355

Li S, Tal Ben-Nun TB-N, Girolamo SD, Alistarh D-A, Hoefler T. Taming unbalanced training workloads in deep learning with partial collective operations. In: Proceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. Association for Computing Machinery; 2020:45-61. doi:10.1145/3332466.3374528

Rybicki J, Abrego N, Ovaskainen O. Habitat fragmentation and species diversity in competitive communities. Ecology Letters. 2020;23(3):506-517. doi:10.1111/ele.13450

Konstantinov NH, Frantar E, Alistarh D-A, Lampert C. On the sample complexity of adversarial multi-source PAC learning. In: Proceedings of the 37th International Conference on Machine Learning. Vol 119. ML Research Press; 2020:5416-5425.