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117 Publications
2021 |
Published |
Conference Paper |
IST-REx-ID: 10432 |
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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| arXiv
2021 |
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Conference Paper |
IST-REx-ID: 10435 |
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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| arXiv
2021 |
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Journal Article |
IST-REx-ID: 9541 |
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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| arXiv
2021 |
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Conference Paper |
IST-REx-ID: 9543 |
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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| arXiv
2021 |
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IST-REx-ID: 9571 |
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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| arXiv
2021 |
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IST-REx-ID: 9620 |
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 |
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IST-REx-ID: 9678 |
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 |
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IST-REx-ID: 9823 |
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 |
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IST-REx-ID: 9827 |
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 |
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IST-REx-ID: 9933 |
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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| arXiv
2021 |
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IST-REx-ID: 9935 |
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 |
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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 |
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Conference Paper |
IST-REx-ID: 10853 |
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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| arXiv
2021 |
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IST-REx-ID: 10854 |
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 |
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IST-REx-ID: 10855 |
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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| arXiv
2021 |
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Conference Paper |
IST-REx-ID: 11436 |
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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| arXiv
2021 |
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IST-REx-ID: 11452 |
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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| arXiv
2021 |
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IST-REx-ID: 11458 |
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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| arXiv
2021 |
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Conference Paper |
IST-REx-ID: 11463 |
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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| arXiv
2021 |
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Conference Paper |
IST-REx-ID: 11464 |
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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