Brown, T. A., Sigouin, W., &; Alistarh, D.-A. (2022). PathCAS: An efficient middle ground for concurrent search data structures. In Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (pp. 385–399). Seoul, Republic of Korea: Association for Computing Machinery.https://doi.org/10.1145/3503221.3508410 
LibreCat: https://research-explorer.ista.ac.at/record/11181
DOI: 10.1145/3503221.3508410
WoS: 000883318200027

Postnikova, A., Koval, N., Nadiradze, G., &; Alistarh, D.-A. (2022). Multi-queues can be state-of-the-art priority schedulers. In Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (pp. 353–367). Seoul, Republic of Korea: Association for Computing Machinery.https://doi.org/10.1145/3503221.3508432 
LibreCat: https://research-explorer.ista.ac.at/record/11180
DOI: 10.1145/3503221.3508432
WoS: 000883318200025
arXiv: 2109.00657

Postnikova, A., Koval, N., Nadiradze, G., &; Alistarh, D.-A. (2022). Multi-queues can be state-of-the-art priority schedulers. Zenodo.https://doi.org/10.5281/ZENODO.5733408 
LibreCat: https://research-explorer.ista.ac.at/record/13076
DOI: 10.5281/ZENODO.5733408

Balliu, A., Hirvonen, J., Melnyk, D., Olivetti, D., Rybicki, J., &; Suomela, J. (2022). Local mending. In M. Parter (Ed.), International Colloquium on Structural Information and Communication Complexity (Vol. 13298, pp. 1–20). Paderborn, Germany: Springer Nature.https://doi.org/10.1007/978-3-031-09993-9_1 
LibreCat: https://research-explorer.ista.ac.at/record/11707
DOI: 10.1007/978-3-031-09993-9_1
WoS: 000876977400001
arXiv: 2102.08703

Iofinova, E. B., Peste, E.-A., Kurtz, M., &; Alistarh, D.-A. (2022). How well do sparse ImageNet models transfer? In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 12256–12266). New Orleans, LA, United States: Institute of Electrical and Electronics Engineers.https://doi.org/10.1109/cvpr52688.2022.01195 
LibreCat: https://research-explorer.ista.ac.at/record/12299
DOI: 10.1109/cvpr52688.2022.01195
WoS: 000870759105034
arXiv: 2111.13445

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 
LibreCat: https://research-explorer.ista.ac.at/record/8286
DOI: 10.1007/s00453-021-00905-9
WoS: 000734004600001
arXiv: 2003.09297

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.
LibreCat: https://research-explorer.ista.ac.at/record/10180
arXiv: 2102.00554

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 
LibreCat: https://research-explorer.ista.ac.at/record/10218
DOI: 10.4230/LIPIcs.DISC.2021.43
arXiv: 2102.08808

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 
LibreCat: https://research-explorer.ista.ac.at/record/10217
DOI: 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 
LibreCat: https://research-explorer.ista.ac.at/record/10216
DOI: 10.4230/LIPIcs.DISC.2021.52
arXiv: 2003.01697

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 
LibreCat: https://research-explorer.ista.ac.at/record/10219
DOI: 10.4230/LIPIcs.DISC.2021.58
arXiv: 2108.02655

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 
LibreCat: https://research-explorer.ista.ac.at/record/10853
DOI: 10.1145/3409964.3461810
arXiv: 2105.08098

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.
LibreCat: https://research-explorer.ista.ac.at/record/11436
arXiv: 1905.11845

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.
LibreCat: https://research-explorer.ista.ac.at/record/11452
arXiv: 2110.14391

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.
LibreCat: https://research-explorer.ista.ac.at/record/11463
arXiv: 2010.08222

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.
LibreCat: https://research-explorer.ista.ac.at/record/11464
arXiv: 2010.08222

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.
LibreCat: https://research-explorer.ista.ac.at/record/9543
arXiv: 2002.09268

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 
LibreCat: https://research-explorer.ista.ac.at/record/9620
DOI: 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 
LibreCat: https://research-explorer.ista.ac.at/record/9823
DOI: 10.1007/978-3-030-79527-6_6
arXiv: 2103.08949

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.
LibreCat: https://research-explorer.ista.ac.at/record/11458
arXiv: 2106.12379