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Alistarh, D.-A., Fedorov, A., &; Koval, N. (2020). In search of the fastest concurrent union-find algorithm. In 23rd International Conference on Principles of Distributed Systems (Vol. 153, p. 15:1-15:16). Neuchatal, Switzerland: Schloss Dagstuhl - Leibniz-Zentrum für Informatik.https://doi.org/10.4230/LIPIcs.OPODIS.2019.15 
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arXiv: 1911.06347

Czumaj, A., Davies, P., &; Parter, M. (2020). Simple, deterministic, constant-round coloring in the congested clique. In Proceedings of the 2020 ACM Symposium on Principles of Distributed Computing (pp. 309–318). Salerno, Italy: Association for Computing Machinery.https://doi.org/10.1145/3382734.3405751 
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arXiv: 2009.06043

Aksenov, V., Alistarh, D.-A., Drozdova, A., &; Mohtashami, A. (2020). The splay-list: A distribution-adaptive concurrent skip-list. In 34th International Symposium on Distributed Computing (Vol. 179, p. 3:1-3:18). Freiburg, Germany: Schloss Dagstuhl - Leibniz-Zentrum für Informatik.https://doi.org/10.4230/LIPIcs.DISC.2020.3 
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arXiv: 2008.01009

Singh, S. P., &; Alistarh, D.-A. (2020). WoodFisher: Efficient second-order approximation for neural network compression. In Advances in Neural Information Processing Systems (Vol. 33, pp. 18098–18109). Vancouver, Canada: Curran Associates.
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arXiv: 2004.14340

Aksenov, V., Alistarh, D.-A., &; Korhonen, J. (2020). Scalable belief propagation via relaxed scheduling. In Advances in Neural Information Processing Systems (Vol. 33, pp. 22361–22372). Vancouver, Canada: Curran Associates.
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arXiv: 2002.11505

Kurtz, M., Kopinsky, J., Gelashvili, R., Matveev, A., Carr, J., Goin, M.,  Alistarh, D.-A. (2020). Inducing and exploiting activation sparsity for fast neural network inference. In 37th International Conference on Machine Learning, ICML 2020 (Vol. 119, pp. 5533–5543). Online.
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Gurel, N. M., Kara, K., Stojanov, A., Smith, T., Lemmin, T., Alistarh, D.-A.,https://doi.org/10.1109/TSP.2020.3010355__Zhang, C. (2020). Compressive sensing using iterative hard thresholding with low precision data representation: Theory and applications. IEEE Transactions on Signal Processing. IEEE.https://doi.org/10.1109/TSP.2020.3010355 
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arXiv: 1802.04907

Li, S., Tal Ben-Nun, T. B.-N., Girolamo, S. D., Alistarh, D.-A., &; Hoefler, T. (2020). 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 (pp. 45–61). San Diego, CA, United States: Association for Computing Machinery.https://doi.org/10.1145/3332466.3374528 
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arXiv: 1908.04207

Rybicki, J., Abrego, N., &; Ovaskainen, O. (2020). Habitat fragmentation and species diversity in competitive communities. Ecology Letters. Wiley.https://doi.org/10.1111/ele.13450 
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Konstantinov, N. H., Frantar, E., Alistarh, D.-A., &; Lampert, C. (2020). On the sample complexity of adversarial multi-source PAC learning. In Proceedings of the 37th International Conference on Machine Learning (Vol. 119, pp. 5416–5425). Online: ML Research Press.
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arXiv: 2002.10384

Bhatia, S., Chatterjee, B., Nathani, D., &; Kaul, M. (2020). A persistent homology perspective to the link prediction problem. In Complex Networks and their applications VIII (Vol. 881, pp. 27–39). Lisbon, Portugal: Springer Nature.https://doi.org/10.1007/978-3-030-36687-2_3 
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Czumaj, A., Davies, P., &; Parter, M. (2020). Graph sparsification for derandomizing massively parallel computation with low space. In Proceedings of the 32nd ACM Symposium on Parallelism in Algorithms and Architectures (SPAA 2020) (pp. 175–185). Virtual Event, United States: Association for Computing Machinery.https://doi.org/10.1145/3350755.3400282 
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arXiv: 1912.05390

Brown, T. A., Prokopec, A., &; Alistarh, D.-A. (2020). Non-blocking interpolation search trees with doubly-logarithmic running time. In Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (pp. 276–291). San Diego, CA, United States: Association for Computing Machinery.https://doi.org/10.1145/3332466.3374542 
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Alistarh, D.-A., Brown, T. A., &; Singhal, N. (2020). Memory tagging: Minimalist synchronization for scalable concurrent data structures. In Annual ACM Symposium on Parallelism in Algorithms and Architectures (pp. 37–49). Virtual Event, United States: Association for Computing Machinery.https://doi.org/10.1145/3350755.3400213 
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Koval, N., Sokolova, M., Fedorov, A., Alistarh, D.-A., &; Tsitelov, D. (2020). Testing concurrency on the JVM with Lincheck. In Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP (pp. 423–424). San Diego, CA, United States: Association for Computing Machinery.https://doi.org/10.1145/3332466.3374503 
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Alistarh, D.-A., Aspnes, J., Ellen, F., Gelashvili, R., &; Zhu, L. (2020). Brief Announcement: Why Extension-Based Proofs Fail. In Proceedings of the 39th Symposium on Principles of Distributed Computing (pp. 54–56). Virtual, Italy: Association for Computing Machinery.https://doi.org/10.1145/3382734.3405743 
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Brandt, S., Keller, B., Rybicki, J., Suomela, J., &; Uitto, J. (2020). Brief announcement: Efficient load-balancing through distributed token dropping. In 34th International Symposium on Distributed Computing (Vol. 179). Virtual: Schloss Dagstuhl - Leibniz-Zentrum für Informatik.https://doi.org/10.4230/LIPIcs.DISC.2020.40 
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arXiv: 2005.07761

Faghri, F., Tabrizian, I., Markov, I., Alistarh, D.-A., Roy, D., &; Ramezani-Kebrya, A. (2020). Adaptive gradient quantization for data-parallel SGD. In Advances in Neural Information Processing Systems (Vol. 33). Vancouver, Canada: Neural Information Processing Systems Foundation.
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arXiv: 2010.12460

Alistarh, D.-A., Nadiradze, G., &; Sabour, A. (2020). Dynamic averaging load balancing on cycles. In 47th International Colloquium on Automata, Languages, and Programming (Vol. 168). Saarbrücken, Germany, Virtual: Schloss Dagstuhl - Leibniz-Zentrum für Informatik.https://doi.org/10.4230/LIPIcs.ICALP.2020.7 
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arXiv: 2003.09297