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arXiv: 2002.12410

Koval, N., Fedorov, A., Sokolova, M., Tsitelov, D., &; Alistarh, D.-A. (2023). Lincheck: A practical framework for testing concurrent data structures on JVM. In 35th International Conference on Computer Aided Verification  (Vol. 13964, pp. 156–169). Paris, France: Springer Nature.https://doi.org/10.1007/978-3-031-37706-8_8 
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Koval, N., Fedorov, A., Sokolova, M., Tsitelov, D., &; Alistarh, D.-A. (2023). Lincheck: A practical framework for testing concurrent data structures on JVM. Zenodo.https://doi.org/10.5281/ZENODO.7877757 
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Fedorov, A., Hashemi, D., Nadiradze, G., &; Alistarh, D.-A. (2023). Provably-efficient and internally-deterministic parallel Union-Find. In Proceedings of the 35th ACM Symposium on Parallelism in Algorithms and Architectures (pp. 261–271). Orlando, FL, United States: Association for Computing Machinery.https://doi.org/10.1145/3558481.3591082 
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arXiv: 2304.09331

Safaryan, M., Krumes, A., &; Alistarh, D.-A. (2023). Knowledge distillation performs partial variance reduction. In 36th Conference on Neural Information Processing Systems (Vol. 36). New Orleans, LA, United States.
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Alistarh, D.-A., Gelashvili, R., &; Rybicki, J. (2022). Fast graphical population protocols. In Q. Bramas, V. Gramoli, &; A. Milani (Eds.), 25th International Conference on Principles of Distributed Systems (Vol. 217). Strasbourg, France: Schloss Dagstuhl - Leibniz-Zentrum für Informatik.https://doi.org/10.4230/LIPIcs.OPODIS.2021.14 
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arXiv: 2102.08808

Nikabadi, A., &; Korhonen, J. (2022). Beyond distributed subgraph detection: Induced subgraphs, multicolored problems and graph parameters. In Q. Bramas, V. Gramoli, &; A. Milani (Eds.), 25th International Conference on Principles of Distributed Systems (Vol. 217). Strasbourg, France: Schloss Dagstuhl - Leibniz-Zentrum für Informatik.https://doi.org/10.4230/LIPIcs.OPODIS.2021.15 
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Shevchenko, A., Kungurtsev, V., &; Mondelli, M. (2022). Mean-field analysis of piecewise linear solutions for wide ReLU networks. Journal of Machine Learning Research. Journal of Machine Learning Research.
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Pacut, M., Parham, M., Rybicki, J., Schmid, S., Suomela, J., &; Tereshchenko, A. (2022). Brief announcement: Temporal locality in online algorithms. In 36th International Symposium on Distributed Computing (Vol. 246). Augusta, GA, United States: Schloss Dagstuhl - Leibniz-Zentrum für Informatik.https://doi.org/10.4230/LIPIcs.DISC.2022.52 
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Markov, I., Ramezanikebrya, H., &; Alistarh, D.-A. (2022). CGX: Adaptive system support for communication-efficient deep learning. In Proceedings of the 23rd ACM/IFIP International Middleware Conference (pp. 241–254). Quebec, QC, Canada: Association for Computing Machinery.https://doi.org/10.1145/3528535.3565248 
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arXiv: 2111.08617

Alistarh, D.-A., Rybicki, J., &; Voitovych, S. (2022). Near-optimal leader election in population protocols on graphs. In Proceedings of the Annual ACM Symposium on Principles of Distributed Computing (pp. 246–256). Salerno, Italy: Association for Computing Machinery.https://doi.org/10.1145/3519270.3538435 
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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 
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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 
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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 
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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 
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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 
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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 
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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.
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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 
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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 
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