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.
LibreCat: https://research-explorer.ista.ac.at/record/14459
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.
LibreCat: https://research-explorer.ista.ac.at/record/14460
arXiv: 2302.04852

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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arXiv: 2301.00774

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 
LibreCat: https://research-explorer.ista.ac.at/record/14364
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arXiv: 1811.01421

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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arXiv: 2304.12622

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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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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arXiv: 2305.17581

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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arXiv: 2111.02278

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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arXiv: 2205.12597

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 
LibreCat: https://research-explorer.ista.ac.at/record/11180
DOI: 10.1145/3503221.3508432
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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 
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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 
LibreCat: https://research-explorer.ista.ac.at/record/11707
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arXiv: 2102.08703