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Alistarh, D.-A., Ellen, F., &; Rybicki, J. (2023). Wait-free approximate agreement on graphs. Theoretical Computer Science. Elsevier.https://doi.org/10.1016/j.tcs.2023.113733 
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Peste, E.-A. (2023). Efficiency and generalization of sparse neural networks. Institute of Science and Technology Austria.https://doi.org/10.15479/at:ista:13074 
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Aksenov, V., Alistarh, D.-A., Drozdova, A., &; Mohtashami, A. (2023). The splay-list: A distribution-adaptive concurrent skip-list. Distributed Computing. Springer Nature.https://doi.org/10.1007/s00446-022-00441-x 
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Markov, I., Vladu, A., Guo, Q., &; Alistarh, D.-A. (2023). Quantized distributed training of large models with convergence guarantees. In Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 24020–24044). Honolulu, Hawaii, HI, United States: ML Research Press.
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Shevchenko, A., Kögler, K., Hassani, H., &; Mondelli, M. (2023). Fundamental limits of two-layer autoencoders, and achieving them with gradient methods. In Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 31151–31209). Honolulu, Hawaii, HI, United States: ML Research Press.
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arXiv: 2212.13468

Nikdan, M., Pegolotti, T., Iofinova, E. B., Kurtic, E., &; Alistarh, D.-A. (2023). SparseProp: Efficient sparse backpropagation for faster training of neural networks at the edge. In Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 26215–26227). Honolulu, Hawaii, HI, United States: ML Research Press.
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Frantar, E., &; Alistarh, D.-A. (2023). SparseGPT: Massive language models can be accurately pruned in one-shot. In Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 10323–10337). Honolulu, Hawaii, HI, United States: ML Research Press.
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Alistarh, D.-A., Aspnes, J., Ellen, F., Gelashvili, R., &; Zhu, L. (2023). Why extension-based proofs fail. SIAM Journal on Computing. Society for Industrial and Applied Mathematics.https://doi.org/10.1137/20M1375851 
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Iofinova, E. B., Peste, E.-A., &; Alistarh, D.-A. (2023). Bias in pruned vision models: In-depth analysis and countermeasures. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 24364–24373). Vancouver, BC, Canada: IEEE.https://doi.org/10.1109/cvpr52729.2023.02334 
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Beznosikov, A., Horvath, S., Richtarik, P., &; Safaryan, M. (2023). On biased compression for distributed learning. Journal of Machine Learning Research. Journal of Machine Learning Research.
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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. 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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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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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