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121 Publications


2022 | Journal Article | IST-REx-ID: 11420 | OA
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.
[Published Version] View | Files available | arXiv
 

2022 | Conference Paper | IST-REx-ID: 12182 | OA
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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2022 | Conference Paper | IST-REx-ID: 12780 | OA
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
[Published Version] View | Files available | DOI | arXiv
 

2022 | Conference Paper | IST-REx-ID: 11844 | OA
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
[Published Version] View | Files available | DOI | arXiv
 

2022 | Conference Paper | IST-REx-ID: 11181 | OA
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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2022 | Conference Paper | IST-REx-ID: 11180 | OA
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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2022 | Research Data Reference | IST-REx-ID: 13076 | OA
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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2022 | Conference Paper | IST-REx-ID: 11707 | OA
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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2022 | Conference Paper | IST-REx-ID: 12299 | OA
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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2021 | Journal Article | IST-REx-ID: 10180 | OA
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.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 

2021 | Conference Paper | IST-REx-ID: 10218 | OA
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
[Published Version] View | Files available | DOI | arXiv
 

2021 | Conference Paper | IST-REx-ID: 10217 | OA
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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2021 | Conference Paper | IST-REx-ID: 10216 | OA
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
[Published Version] View | Files available | DOI | arXiv
 

2021 | Conference Paper | IST-REx-ID: 10219 | OA
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
[Published Version] View | Files available | DOI | arXiv
 

2021 | Conference Paper | IST-REx-ID: 10853 | OA
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
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2021 | Conference Paper | IST-REx-ID: 11436 | OA
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.
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2021 | Conference Paper | IST-REx-ID: 11452 | OA
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.
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2021 | Conference Paper | IST-REx-ID: 11463 | OA
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.
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2021 | Conference Paper | IST-REx-ID: 11464 | OA
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.
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2021 | Conference Paper | IST-REx-ID: 9543 | OA
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.
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2021 | Conference Paper | IST-REx-ID: 9620 | OA
Alistarh, D.-A., & Davies, P. (2021). Collecting coupons is faster with friends. In Structural Information and Communication Complexity (Vol. 12810, pp. 3–12). Wrocław, Poland: Springer Nature. https://doi.org/10.1007/978-3-030-79527-6_1
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2021 | Conference Paper | IST-REx-ID: 9823 | OA
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). Wrocław, Poland: Springer Nature. https://doi.org/10.1007/978-3-030-79527-6_6
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2021 | Conference Paper | IST-REx-ID: 11458 | OA
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.
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2021 | Conference Paper | IST-REx-ID: 13147 | OA
Alimisis, F., Davies, P., & Alistarh, D.-A. (2021). Communication-efficient distributed optimization with quantized preconditioners. In Proceedings of the 38th International Conference on Machine Learning (Vol. 139, pp. 196–206). Virtual: ML Research Press.
[Published Version] View | Files available | arXiv
 

2021 | Journal Article | IST-REx-ID: 8723 | OA
Li, S., Tal Ben-Nun, T. B.-N., Nadiradze, G., Girolamo, S. D., Dryden, N., Alistarh, D.-A., & Hoefler, T. (2021). Breaking (global) barriers in parallel stochastic optimization with wait-avoiding group averaging. IEEE Transactions on Parallel and Distributed Systems. IEEE. https://doi.org/10.1109/TPDS.2020.3040606
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2021 | Journal Article | IST-REx-ID: 9827 | OA
Chatterjee, B., Walulya, I., & Tsigas, P. (2021). Concurrent linearizable nearest neighbour search in LockFree-kD-tree. Theoretical Computer Science. Elsevier. https://doi.org/10.1016/j.tcs.2021.06.041
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2021 | Conference Paper | IST-REx-ID: 9951
Alistarh, D.-A., Töpfer, M., & Uznański, P. (2021). Comparison dynamics in population protocols. In Proceedings of the 2021 ACM Symposium on Principles of Distributed Computing (pp. 55–65). Virtual, Italy: Association for Computing Machinery. https://doi.org/10.1145/3465084.3467915
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2021 | Conference Paper | IST-REx-ID: 9935 | OA
Czumaj, A., Davies, P., & Parter, M. (2021). Improved deterministic (Δ+1) coloring in low-space MPC. In Proceedings of the 2021 ACM Symposium on Principles of Distributed Computing (pp. 469–479). Virtual, Italy: Association for Computing Machinery. https://doi.org/10.1145/3465084.3467937
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2021 | Conference Paper | IST-REx-ID: 9933 | OA
Czumaj, A., Davies, P., & Parter, M. (2021). Component stability in low-space massively parallel computation. In Proceedings of the 2021 ACM Symposium on Principles of Distributed Computing (pp. 481–491). Virtual, Italy: Association for Computing Machinery. https://doi.org/10.1145/3465084.3467903
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2021 | Conference Paper | IST-REx-ID: 10432 | OA
Nadiradze, G., Markov, I., Chatterjee, B., Kungurtsev, V., & Alistarh, D.-A. (2021). Elastic consistency: A practical consistency model for distributed stochastic gradient descent. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, pp. 9037–9045). Virtual.
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2021 | Conference Paper | IST-REx-ID: 10049 | OA
Klein, K., Pascual Perez, G., Walter, M., Kamath Hosdurg, C., Capretto, M., Cueto Noval, M., … Pietrzak, K. Z. (2021). Keep the dirt: tainted TreeKEM, adaptively and actively secure continuous group key agreement. In 2021 IEEE Symposium on Security and Privacy (pp. 268–284). San Francisco, CA, United States: IEEE. https://doi.org/10.1109/sp40001.2021.00035
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2021 | Conference Paper | IST-REx-ID: 10854 | OA
Foerster, K.-T., Korhonen, J., Paz, A., Rybicki, J., & Schmid, S. (2021). Input-dynamic distributed algorithms for communication networks. In Abstract Proceedings of the 2021 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems (pp. 71–72). Virtual, Online: Association for Computing Machinery. https://doi.org/10.1145/3410220.3453923
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2021 | Journal Article | IST-REx-ID: 10855 | OA
Foerster, K.-T., Korhonen, J., Paz, A., Rybicki, J., & Schmid, S. (2021). Input-dynamic distributed algorithms for communication networks. Proceedings of the ACM on Measurement and Analysis of Computing Systems. Association for Computing Machinery. https://doi.org/10.1145/3447384
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2021 | Thesis | IST-REx-ID: 10429 | OA
Nadiradze, G. (2021). On achieving scalability through relaxation. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:10429
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2021 | Conference Paper | IST-REx-ID: 10435 | OA
Nadiradze, G., Sabour, A., Davies, P., Li, S., & Alistarh, D.-A. (2021). Asynchronous decentralized SGD with quantized and local updates. In 35th Conference on Neural Information Processing Systems. Sydney, Australia: Neural Information Processing Systems Foundation.
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2021 | Journal Article | IST-REx-ID: 9541 | OA
Czumaj, A., Davies, P., & Parter, M. (2021). Graph sparsification for derandomizing massively parallel computation with low space. ACM Transactions on Algorithms. Association for Computing Machinery. https://doi.org/10.1145/3451992
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2021 | Conference Paper | IST-REx-ID: 9678 | OA
Brandt, S., Keller, B., Rybicki, J., Suomela, J., & Uitto, J. (2021). Efficient load-balancing through distributed token dropping. In Annual ACM Symposium on Parallelism in Algorithms and Architectures (pp. 129–139). Virtual Event, United States. https://doi.org/10.1145/3409964.3461785
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2021 | Journal Article | IST-REx-ID: 8286 | OA
Alistarh, D.-A., Nadiradze, G., & Sabour, A. (2021). 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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2021 | Journal Article | IST-REx-ID: 9571 | OA
Ramezani-Kebrya, A., Faghri, F., Markov, I., Aksenov, V., Alistarh, D.-A., & Roy, D. M. (2021). NUQSGD: Provably communication-efficient data-parallel SGD via nonuniform quantization. Journal of Machine Learning Research. Journal of Machine Learning Research.
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2021 | Journal Article | IST-REx-ID: 7939 | OA
Censor-Hillel, K., Dory, M., Korhonen, J., & Leitersdorf, D. (2021). Fast approximate shortest paths in the congested clique. Distributed Computing. Springer Nature. https://doi.org/10.1007/s00446-020-00380-5
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2021 | Journal Article | IST-REx-ID: 15271
Czumaj, A., Davies, P., & Parter, M. (2021). Simple, deterministic, constant-round coloring in congested clique and MPC. SIAM Journal on Computing. Society for Industrial & Applied Mathematics. https://doi.org/10.1137/20m1366502
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2021 | Journal Article | IST-REx-ID: 15267 | OA
Czumaj, A., & Davies, P. (2021). Exploiting spontaneous transmissions for broadcasting and leader election in radio networks. Journal of the ACM. Association for Computing Machinery. https://doi.org/10.1145/3446383
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2021 | Conference Paper | IST-REx-ID: 15263 | OA
Alimisis, F., Orvieto, A., Becigneul, G., & Lucchi, A. (2021). Momentum improves optimization on Riemannian manifolds. In Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (Vol. 130, pp. 1351–1359). San Diego, CA, United States; Virtual: ML Research Press.
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2020 | Conference Paper | IST-REx-ID: 7272 | OA
Arbel-Raviv, M., Brown, T. A., & Morrison, A. (2020). Getting to the root of concurrent binary search tree performance. In Proceedings of the 2018 USENIX Annual Technical Conference (pp. 295–306). Boston, MA, United States: USENIX Association.
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2020 | Conference Paper | IST-REx-ID: 7605 | OA
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
[Published Version] View | Files available | DOI | arXiv
 

2020 | Conference Paper | IST-REx-ID: 7803 | OA
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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2020 | Conference Paper | IST-REx-ID: 8725 | OA
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
[Published Version] View | Files available | DOI | arXiv
 

2020 | Conference Paper | IST-REx-ID: 9632 | OA
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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2020 | Conference Paper | IST-REx-ID: 9631 | OA
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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2020 | Conference Paper | IST-REx-ID: 9415 | OA
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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2020 | Journal Article | IST-REx-ID: 8268 | OA
Gurel, N. M., Kara, K., Stojanov, A., Smith, T., Lemmin, T., Alistarh, D.-A., … 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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2020 | Conference Paper | IST-REx-ID: 8722 | OA
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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2020 | Journal Article | IST-REx-ID: 7224 | OA
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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2020 | Conference Paper | IST-REx-ID: 8724 | OA
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.
[Published Version] View | Files available | arXiv
 

2020 | Conference Paper | IST-REx-ID: 7213 | OA
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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2020 | Conference Paper | IST-REx-ID: 7802 | OA
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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2020 | Conference Paper | IST-REx-ID: 7636 | OA
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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2020 | Conference Paper | IST-REx-ID: 8191
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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2020 | Conference Paper | IST-REx-ID: 7635
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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2020 | Conference Paper | IST-REx-ID: 8383
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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2020 | Conference Paper | IST-REx-ID: 15074 | OA
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
[Published Version] View | Files available | DOI | arXiv
 

2020 | Conference Paper | IST-REx-ID: 15077 | OA
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
[Published Version] View | Files available | DOI | arXiv
 

2020 | Conference Paper | IST-REx-ID: 15086 | OA
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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2019 | Journal Article | IST-REx-ID: 6759 | OA
Jelínek, V., & Töpfer, M. (2019). On grounded L-graphs and their relatives. Electronic Journal of Combinatorics. Electronic Journal of Combinatorics. https://doi.org/10.37236/8096
[Published Version] View | Files available | DOI | arXiv
 

2019 | Conference Paper | IST-REx-ID: 6931 | OA
Nowak, T., & Rybicki, J. (2019). Byzantine approximate agreement on graphs. In 33rd International Symposium on Distributed Computing (Vol. 146, p. 29:1--29:17). Budapest, Hungary: Schloss Dagstuhl - Leibniz-Zentrum für Informatik. https://doi.org/10.4230/LIPICS.DISC.2019.29
[Published Version] View | Files available | DOI | arXiv
 

2019 | Conference Paper | IST-REx-ID: 5947 | OA
Chatterjee, B., Peri, S., Sa, M., & Singhal, N. (2019). A simple and practical concurrent non-blocking unbounded graph with linearizable reachability queries. In ACM International Conference Proceeding Series (pp. 168–177). Bangalore, India: ACM. https://doi.org/10.1145/3288599.3288617
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2019 | Conference Poster | IST-REx-ID: 6485
Koval, N., Alistarh, D.-A., & Elizarov, R. (2019). Lock-free channels for programming via communicating sequential processes. Proceedings of the 24th Symposium on Principles and Practice of Parallel Programming (pp. 417–418). Washington, NY, United States: ACM Press. https://doi.org/10.1145/3293883.3297000
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2019 | Journal Article | IST-REx-ID: 6936 | OA
Ovaskainen, O., Rybicki, J., & Abrego, N. (2019). What can observational data reveal about metacommunity processes? Ecography. Wiley. https://doi.org/10.1111/ecog.04444
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2019 | Journal Article | IST-REx-ID: 6972 | OA
Lenzen, C., & Rybicki, J. (2019). Self-stabilising Byzantine clock synchronisation is almost as easy as consensus. Journal of the ACM. ACM. https://doi.org/10.1145/3339471
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2019 | Conference Paper | IST-REx-ID: 7122
Khirirat, S., Johansson, M., & Alistarh, D.-A. (2019). Gradient compression for communication-limited convex optimization. In 2018 IEEE Conference on Decision and Control. Miami Beach, FL, United States: IEEE. https://doi.org/10.1109/cdc.2018.8619625
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2019 | Conference Paper | IST-REx-ID: 7201 | OA
Renggli, C., Ashkboos, S., Aghagolzadeh, M., Alistarh, D.-A., & Hoefler, T. (2019). SparCML: High-performance sparse communication for machine learning. In International Conference for High Performance Computing, Networking, Storage and Analysis, SC. Denver, CO, Unites States: ACM. https://doi.org/10.1145/3295500.3356222
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2019 | Journal Article | IST-REx-ID: 7214 | OA
Aganezov, S., Zban, I., Aksenov, V., Alexeev, N., & Schatz, M. C. (2019). Recovering rearranged cancer chromosomes from karyotype graphs. BMC Bioinformatics. BMC. https://doi.org/10.1186/s12859-019-3208-4
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2019 | Conference Paper | IST-REx-ID: 7228
Koval, N., Alistarh, D.-A., & Elizarov, R. (2019). Scalable FIFO channels for programming via communicating sequential processes. In 25th Anniversary of Euro-Par (Vol. 11725, pp. 317–333). Göttingen, Germany: Springer Nature. https://doi.org/10.1007/978-3-030-29400-7_23
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2019 | Conference Paper | IST-REx-ID: 7437 | OA
Yu, C., Tang, H., Renggli, C., Kassing, S., Singla, A., Alistarh, D.-A., … Liu, J. (2019). Distributed learning over unreliable networks. In 36th International Conference on Machine Learning, ICML 2019 (Vol. 2019–June, pp. 12481–12512). Long Beach, CA, United States: IMLS.
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2019 | Conference Paper | IST-REx-ID: 6673 | OA
Alistarh, D.-A., Nadiradze, G., & Koval, N. (2019). Efficiency guarantees for parallel incremental algorithms under relaxed schedulers. In 31st ACM Symposium on Parallelism in Algorithms and Architectures (pp. 145–154). Phoenix, AZ, United States: ACM Press. https://doi.org/10.1145/3323165.3323201
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2019 | Conference Paper | IST-REx-ID: 7542 | OA
Wendler, C., Alistarh, D.-A., & Püschel, M. (2019). Powerset convolutional neural networks (Vol. 32, pp. 927–938). Presented at the NIPS: Conference on Neural Information Processing Systems, Vancouver, Canada: Neural Information Processing Systems Foundation.
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2019 | Conference Paper | IST-REx-ID: 6935 | OA
Foerster, K.-T., Korhonen, J., Rybicki, J., & Schmid, S. (2019). Does preprocessing help under congestion? In Proceedings of the 2019 ACM Symposium on Principles of Distributed Computing (pp. 259–261). Toronto, ON, Canada: ACM. https://doi.org/10.1145/3293611.3331581
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2019 | Conference Paper | IST-REx-ID: 6676 | OA
Alistarh, D.-A., Aspnes, J., Ellen, F., Gelashvili, R., & Zhu, L. (2019). Why extension-based proofs fail. In Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing (pp. 986–996). Phoenix, AZ, United States: ACM Press. https://doi.org/10.1145/3313276.3316407
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2019 | Conference Paper | IST-REx-ID: 6933 | OA
Censor-Hillel, K., Dory, M., Korhonen, J., & Leitersdorf, D. (2019). Fast approximate shortest paths in the congested clique. In Proceedings of the 2019 ACM Symposium on Principles of Distributed Computin (pp. 74–83). Toronto, ON, Canada: ACM. https://doi.org/10.1145/3293611.3331633
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2018 | Journal Article | IST-REx-ID: 536 | OA
Alistarh, D.-A., Aspnes, J., King, V., & Saia, J. (2018). Communication-efficient randomized consensus. Distributed Computing. Springer. https://doi.org/10.1007/s00446-017-0315-1
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2018 | Conference Paper | IST-REx-ID: 7116 | OA
Grubic, D., Tam, L., Alistarh, D.-A., & Zhang, C. (2018). Synchronous multi-GPU training for deep learning with low-precision communications: An empirical study. In Proceedings of the 21st International Conference on Extending Database Technology (pp. 145–156). Vienna, Austria: OpenProceedings. https://doi.org/10.5441/002/EDBT.2018.14
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2018 | Journal Article | IST-REx-ID: 6001
Alistarh, D.-A., Leiserson, W., Matveev, A., & Shavit, N. (2018). ThreadScan: Automatic and scalable memory reclamation. ACM Transactions on Parallel Computing. Association for Computing Machinery. https://doi.org/10.1145/3201897
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2018 | Conference Paper | IST-REx-ID: 7812 | OA
Polino, A., Pascanu, R., & Alistarh, D.-A. (2018). Model compression via distillation and quantization. In 6th International Conference on Learning Representations. Vancouver, Canada.
[Published Version] View | Files available | arXiv
 

2018 | Conference Paper | IST-REx-ID: 397
Arbel Raviv, M., & Brown, T. A. (2018). Harnessing epoch-based reclamation for efficient range queries (Vol. 53, pp. 14–27). Presented at the PPoPP: Principles and Practice of Parallel Programming, Vienna, Austria: ACM. https://doi.org/10.1145/3178487.3178489
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2018 | Journal Article | IST-REx-ID: 43 | OA
Rybicki, J., Kisdi, E., & Anttila, J. (2018). Model of bacterial toxin-dependent pathogenesis explains infective dose. PNAS. National Academy of Sciences. https://doi.org/10.1073/pnas.1721061115
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2018 | Journal Article | IST-REx-ID: 76 | OA
Lenzen, C., & Rybicki, J. (2018). Near-optimal self-stabilising counting and firing squads. Distributed Computing. Springer. https://doi.org/10.1007/s00446-018-0342-6
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2018 | Conference Paper | IST-REx-ID: 85 | OA
Gilad, E., Brown, T. A., Oskin, M., & Etsion, Y. (2018). Snapshot based synchronization: A fast replacement for Hand-over-Hand locking (Vol. 11014, pp. 465–479). Presented at the Euro-Par: European Conference on Parallel Processing, Turin, Italy: Springer. https://doi.org/10.1007/978-3-319-96983-1_33
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2018 | Conference Paper | IST-REx-ID: 5962 | OA
Alistarh, D.-A., De Sa, C., & Konstantinov, N. H. (2018). The convergence of stochastic gradient descent in asynchronous shared memory. In Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18 (pp. 169–178). Egham, United Kingdom: ACM Press. https://doi.org/10.1145/3212734.3212763
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2018 | Conference Paper | IST-REx-ID: 5961
Alistarh, D.-A. (2018). A brief tutorial on distributed and concurrent machine learning. In Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18 (pp. 487–488). Egham, United Kingdom: ACM Press. https://doi.org/10.1145/3212734.3212798
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2018 | Conference Paper | IST-REx-ID: 5963 | OA
Alistarh, D.-A., Brown, T. A., Kopinsky, J., & Nadiradze, G. (2018). Relaxed schedulers can efficiently parallelize iterative algorithms. In Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18 (pp. 377–386). Egham, United Kingdom: ACM Press. https://doi.org/10.1145/3212734.3212756
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2018 | Conference Paper | IST-REx-ID: 5965 | OA
Alistarh, D.-A., Brown, T. A., Kopinsky, J., Li, J. Z., & Nadiradze, G. (2018). Distributionally linearizable data structures. In Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18 (pp. 133–142). Vienna, Austria: ACM Press. https://doi.org/10.1145/3210377.3210411
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2018 | Conference Paper | IST-REx-ID: 5966 | OA
Alistarh, D.-A., Haider, S. K., Kübler, R., & Nadiradze, G. (2018). The transactional conflict problem. In Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18 (pp. 383–392). Vienna, Austria: ACM Press. https://doi.org/10.1145/3210377.3210406
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2018 | Conference Paper | IST-REx-ID: 5964 | OA
Aksenov, V., Alistarh, D.-A., & Kuznetsov, P. (2018). Brief Announcement: Performance prediction for coarse-grained locking. In Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18 (pp. 411–413). Egham, United Kingdom: ACM Press. https://doi.org/10.1145/3212734.3212785
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2018 | Conference Paper | IST-REx-ID: 6031
Stojanov, A., Smith, T. M., Alistarh, D.-A., & Puschel, M. (2018). Fast quantized arithmetic on x86: Trading compute for data movement. In 2018 IEEE International Workshop on Signal Processing Systems (Vol. 2018–October). Cape Town, South Africa: IEEE. https://doi.org/10.1109/SiPS.2018.8598402
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2018 | Conference Paper | IST-REx-ID: 7123 | OA
Alistarh, D.-A., Aspnes, J., & Gelashvili, R. (2018). Space-optimal majority in population protocols. In Proceedings of the 29th Annual ACM-SIAM Symposium on Discrete Algorithms (pp. 2221–2239). New Orleans, LA, United States: ACM. https://doi.org/10.1137/1.9781611975031.144
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2018 | Conference Paper | IST-REx-ID: 6558 | OA
Alistarh, D.-A., Allen-Zhu, Z., & Li, J. (2018). Byzantine stochastic gradient descent. In Advances in Neural Information Processing Systems (Vol. 2018, pp. 4613–4623). Montreal, Canada: Neural Information Processing Systems Foundation.
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2018 | Conference Paper | IST-REx-ID: 6589 | OA
Alistarh, D.-A., Hoefler, T., Johansson, M., Konstantinov, N. H., Khirirat, S., & Renggli, C. (2018). The convergence of sparsified gradient methods. In Advances in Neural Information Processing Systems 31 (Vol. Volume 2018, pp. 5973–5983). Montreal, Canada: Neural Information Processing Systems Foundation.
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2017 | Conference Paper | IST-REx-ID: 487
Baig, G., Radunovic, B., Alistarh, D.-A., Balkwill, M., Karagiannis, T., & Qiu, L. (2017). Towards unlicensed cellular networks in TV white spaces. In Proceedings of the 2017 13th International Conference on emerging Networking EXperiments and Technologies (pp. 2–14). Incheon, South Korea: ACM. https://doi.org/10.1145/3143361.3143367
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2017 | Conference Paper | IST-REx-ID: 791 | OA
Alistarh, D.-A., Kopinsky, J., Li, J., & Nadiradze, G. (2017). The power of choice in priority scheduling. In Proceedings of the ACM Symposium on Principles of Distributed Computing (Vol. Part F129314, pp. 283–292). Washington, WA, USA: ACM. https://doi.org/10.1145/3087801.3087810
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2017 | Conference Paper | IST-REx-ID: 431 | OA
Alistarh, D.-A., Grubic, D., Li, J., Tomioka, R., & Vojnović, M. (2017). QSGD: Communication-efficient SGD via gradient quantization and encoding (Vol. 2017, pp. 1710–1721). Presented at the NIPS: Neural Information Processing System, Long Beach, CA, United States: Neural Information Processing Systems Foundation.
[Submitted Version] View | Download Submitted Version (ext.) | arXiv
 

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