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121 Publications
2022 | Journal Article | IST-REx-ID: 11420 | 
A. Shevchenko, V. Kungurtsev, and M. Mondelli, “Mean-field analysis of piecewise linear solutions for wide ReLU networks,” Journal of Machine Learning Research, vol. 23, no. 130. Journal of Machine Learning Research, pp. 1–55, 2022.
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| arXiv
2022 | Conference Paper | IST-REx-ID: 12182 |
M. Pacut, M. Parham, J. Rybicki, S. Schmid, J. Suomela, and A. Tereshchenko, “Brief announcement: Temporal locality in online algorithms,” in 36th International Symposium on Distributed Computing, Augusta, GA, United States, 2022, vol. 246.
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2022 | Conference Paper | IST-REx-ID: 12780 |
I. Markov, H. Ramezanikebrya, and D.-A. Alistarh, “CGX: Adaptive system support for communication-efficient deep learning,” in Proceedings of the 23rd ACM/IFIP International Middleware Conference, Quebec, QC, Canada, 2022, pp. 241–254.
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| arXiv
2022 | Conference Paper | IST-REx-ID: 11844 |
D.-A. Alistarh, J. Rybicki, and S. Voitovych, “Near-optimal leader election in population protocols on graphs,” in Proceedings of the Annual ACM Symposium on Principles of Distributed Computing, Salerno, Italy, 2022, pp. 246–256.
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| arXiv
2022 | Conference Paper | IST-REx-ID: 11181 |
T. A. Brown, W. Sigouin, and D.-A. Alistarh, “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, Seoul, Republic of Korea, 2022, pp. 385–399.
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2022 | Conference Paper | IST-REx-ID: 11180 |
A. Postnikova, N. Koval, G. Nadiradze, and D.-A. Alistarh, “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, Seoul, Republic of Korea, 2022, pp. 353–367.
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2022 | Research Data Reference | IST-REx-ID: 13076 |
A. Postnikova, N. Koval, G. Nadiradze, and D.-A. Alistarh, “Multi-queues can be state-of-the-art priority schedulers.” Zenodo, 2022.
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2022 | Conference Paper | IST-REx-ID: 11707 |
A. Balliu, J. Hirvonen, D. Melnyk, D. Olivetti, J. Rybicki, and J. Suomela, “Local mending,” in International Colloquium on Structural Information and Communication Complexity, Paderborn, Germany, 2022, vol. 13298, pp. 1–20.
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2022 | Conference Paper | IST-REx-ID: 12299 |
E. B. Iofinova, E.-A. Peste, M. Kurtz, and D.-A. Alistarh, “How well do sparse ImageNet models transfer?,” in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, United States, 2022, pp. 12256–12266.
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| arXiv
2021 | Journal Article | IST-REx-ID: 10180 |
T. Hoefler, D.-A. Alistarh, T. Ben-Nun, N. Dryden, and E.-A. Peste, “Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks,” Journal of Machine Learning Research, vol. 22, no. 241. Journal of Machine Learning Research, pp. 1–124, 2021.
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| arXiv
2021 | Conference Paper | IST-REx-ID: 10218 |
D.-A. Alistarh, R. Gelashvili, and J. Rybicki, “Brief announcement: Fast graphical population protocols,” in 35th International Symposium on Distributed Computing, Freiburg, Germany, 2021, vol. 209.
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| arXiv
2021 | Conference Paper | IST-REx-ID: 10217 |
D.-A. Alistarh, R. Gelashvili, and G. Nadiradze, “Lower bounds for shared-memory leader election under bounded write contention,” in 35th International Symposium on Distributed Computing, Freiburg, Germany, 2021, vol. 209.
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2021 | Conference Paper | IST-REx-ID: 10216 |
B. Chatterjee, S. Peri, and M. Sa, “Brief announcement: Non-blocking dynamic unbounded graphs with worst-case amortized bounds,” in 35th International Symposium on Distributed Computing, Freiburg, Germany, 2021, vol. 209.
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| arXiv
2021 | Conference Paper | IST-REx-ID: 10219 |
J. Korhonen, A. Paz, J. Rybicki, S. Schmid, and J. Suomela, “Brief announcement: Sinkless orientation is hard also in the supported LOCAL model,” in 35th International Symposium on Distributed Computing, Freiburg, Germany, 2021, vol. 209.
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| arXiv
2021 | Conference Paper | IST-REx-ID: 10853 |
A. Fedorov, N. Koval, and D.-A. Alistarh, “A scalable concurrent algorithm for dynamic connectivity,” in Proceedings of the 33rd ACM Symposium on Parallelism in Algorithms and Architectures, Virtual, Online, 2021, pp. 208–220.
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2021 | Conference Paper | IST-REx-ID: 11436 |
V. Kungurtsev, M. Egan, B. Chatterjee, and D.-A. Alistarh, “Asynchronous optimization methods for efficient training of deep neural networks with guarantees,” in 35th AAAI Conference on Artificial Intelligence, AAAI 2021, Virtual, Online, 2021, vol. 35, no. 9B, pp. 8209–8216.
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| arXiv
2021 | Conference Paper | IST-REx-ID: 11452 |
F. Alimisis, P. Davies, B. Vandereycken, and D.-A. Alistarh, “Distributed principal component analysis with limited communication,” in Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems, Virtual, Online, 2021, vol. 4, pp. 2823–2834.
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| arXiv
2021 | Conference Paper | IST-REx-ID: 11463 |
E. Frantar, E. Kurtic, and D.-A. Alistarh, “M-FAC: Efficient matrix-free approximations of second-order information,” in 35th Conference on Neural Information Processing Systems, Virtual, Online, 2021, vol. 34, pp. 14873–14886.
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| arXiv
2021 | Conference Paper | IST-REx-ID: 11464 |
D.-A. Alistarh and J. Korhonen, “Towards tight communication lower bounds for distributed optimisation,” in 35th Conference on Neural Information Processing Systems, Virtual, Online, 2021, vol. 34, pp. 7254–7266.
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| arXiv
2021 | Conference Paper | IST-REx-ID: 9543 |
P. Davies, V. Gurunanthan, N. Moshrefi, S. Ashkboos, and D.-A. Alistarh, “New bounds for distributed mean estimation and variance reduction,” in 9th International Conference on Learning Representations, Virtual, 2021.
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| arXiv
2021 | Conference Paper | IST-REx-ID: 9620 |
D.-A. Alistarh and P. Davies, “Collecting coupons is faster with friends,” in Structural Information and Communication Complexity, Wrocław, Poland, 2021, vol. 12810, pp. 3–12.
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2021 | Conference Paper | IST-REx-ID: 9823 |
D.-A. Alistarh, F. Ellen, and J. Rybicki, “Wait-free approximate agreement on graphs,” in Structural Information and Communication Complexity, Wrocław, Poland, 2021, vol. 12810, pp. 87–105.
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| arXiv
2021 | Conference Paper | IST-REx-ID: 11458 |
E.-A. Peste, E. B. Iofinova, A. Vladu, and D.-A. Alistarh, “AC/DC: Alternating Compressed/DeCompressed training of deep neural networks,” in 35th Conference on Neural Information Processing Systems, Virtual, Online, 2021, vol. 34, pp. 8557–8570.
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| arXiv
2021 | Conference Paper | IST-REx-ID: 13147 |
F. Alimisis, P. Davies, and D.-A. Alistarh, “Communication-efficient distributed optimization with quantized preconditioners,” in Proceedings of the 38th International Conference on Machine Learning, Virtual, 2021, vol. 139, pp. 196–206.
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| arXiv
2021 | Journal Article | IST-REx-ID: 8723 |
S. Li et al., “Breaking (global) barriers in parallel stochastic optimization with wait-avoiding group averaging,” IEEE Transactions on Parallel and Distributed Systems, vol. 32, no. 7. IEEE, 2021.
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| arXiv
2021 | Journal Article | IST-REx-ID: 9827 |
B. Chatterjee, I. Walulya, and P. Tsigas, “Concurrent linearizable nearest neighbour search in LockFree-kD-tree,” Theoretical Computer Science, vol. 886. Elsevier, pp. 27–48, 2021.
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2021 | Conference Paper | IST-REx-ID: 9935 |
A. Czumaj, P. Davies, and M. Parter, “Improved deterministic (Δ+1) coloring in low-space MPC,” in Proceedings of the 2021 ACM Symposium on Principles of Distributed Computing, Virtual, Italy, 2021, pp. 469–479.
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2021 | Conference Paper | IST-REx-ID: 9933 |
A. Czumaj, P. Davies, and M. Parter, “Component stability in low-space massively parallel computation,” in Proceedings of the 2021 ACM Symposium on Principles of Distributed Computing, Virtual, Italy, 2021, pp. 481–491.
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| arXiv
2021 | Conference Paper | IST-REx-ID: 10432 |
G. Nadiradze, I. Markov, B. Chatterjee, V. Kungurtsev, and D.-A. Alistarh, “Elastic consistency: A practical consistency model for distributed stochastic gradient descent,” in Proceedings of the AAAI Conference on Artificial Intelligence, Virtual, 2021, vol. 35, no. 10, pp. 9037–9045.
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2021 | Conference Paper | IST-REx-ID: 10049 |
K. Klein et al., “Keep the dirt: tainted TreeKEM, adaptively and actively secure continuous group key agreement,” in 2021 IEEE Symposium on Security and Privacy , San Francisco, CA, United States, 2021, pp. 268–284.
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2021 | Conference Paper | IST-REx-ID: 10854 |
K.-T. Foerster, J. Korhonen, A. Paz, J. Rybicki, and S. Schmid, “Input-dynamic distributed algorithms for communication networks,” in Abstract Proceedings of the 2021 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems, Virtual, Online, 2021, pp. 71–72.
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2021 | Journal Article | IST-REx-ID: 10855 |
K.-T. Foerster, J. Korhonen, A. Paz, J. Rybicki, and S. Schmid, “Input-dynamic distributed algorithms for communication networks,” Proceedings of the ACM on Measurement and Analysis of Computing Systems, vol. 5, no. 1. Association for Computing Machinery, pp. 1–33, 2021.
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2021 | Thesis | IST-REx-ID: 10429 |
G. Nadiradze, “On achieving scalability through relaxation,” Institute of Science and Technology Austria, 2021.
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2021 | Conference Paper | IST-REx-ID: 10435 |
G. Nadiradze, A. Sabour, P. Davies, S. Li, and D.-A. Alistarh, “Asynchronous decentralized SGD with quantized and local updates,” in 35th Conference on Neural Information Processing Systems, Sydney, Australia, 2021.
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2021 | Journal Article | IST-REx-ID: 9541 |
A. Czumaj, P. Davies, and M. Parter, “Graph sparsification for derandomizing massively parallel computation with low space,” ACM Transactions on Algorithms, vol. 17, no. 2. Association for Computing Machinery, 2021.
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| arXiv
2021 | Conference Paper | IST-REx-ID: 9678 |
S. Brandt, B. Keller, J. Rybicki, J. Suomela, and J. Uitto, “Efficient load-balancing through distributed token dropping,” in Annual ACM Symposium on Parallelism in Algorithms and Architectures, Virtual Event, United States, 2021, pp. 129–139.
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2021 | Journal Article | IST-REx-ID: 8286 |
D.-A. Alistarh, G. Nadiradze, and A. Sabour, “Dynamic averaging load balancing on cycles,” Algorithmica. Springer Nature, 2021.
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| arXiv
2021 | Journal Article | IST-REx-ID: 9571 |
A. Ramezani-Kebrya, F. Faghri, I. Markov, V. Aksenov, D.-A. Alistarh, and D. M. Roy, “NUQSGD: Provably communication-efficient data-parallel SGD via nonuniform quantization,” Journal of Machine Learning Research, vol. 22, no. 114. Journal of Machine Learning Research, p. 1−43, 2021.
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| arXiv
2021 | Journal Article | IST-REx-ID: 7939 |
K. Censor-Hillel, M. Dory, J. Korhonen, and D. Leitersdorf, “Fast approximate shortest paths in the congested clique,” Distributed Computing, vol. 34. Springer Nature, pp. 463–487, 2021.
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| arXiv
2021 | Journal Article | IST-REx-ID: 15267 |
A. Czumaj and P. Davies, “Exploiting spontaneous transmissions for broadcasting and leader election in radio networks,” Journal of the ACM, vol. 68, no. 2. Association for Computing Machinery, 2021.
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2021 | Conference Paper | IST-REx-ID: 15263 |
F. Alimisis, A. Orvieto, G. Becigneul, and A. Lucchi, “Momentum improves optimization on Riemannian manifolds,” in Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, San Diego, CA, United States; Virtual, 2021, vol. 130, pp. 1351–1359.
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| arXiv
2020 | Conference Paper | IST-REx-ID: 7272 |
M. Arbel-Raviv, T. A. Brown, and A. Morrison, “Getting to the root of concurrent binary search tree performance,” in Proceedings of the 2018 USENIX Annual Technical Conference, Boston, MA, United States, 2020, pp. 295–306.
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2020 | Conference Paper | IST-REx-ID: 7605 |
D.-A. Alistarh, A. Fedorov, and N. Koval, “In search of the fastest concurrent union-find algorithm,” in 23rd International Conference on Principles of Distributed Systems, Neuchatal, Switzerland, 2020, vol. 153, p. 15:1-15:16.
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| arXiv
2020 | Conference Paper | IST-REx-ID: 7803 |
A. Czumaj, P. Davies, and M. Parter, “Simple, deterministic, constant-round coloring in the congested clique,” in Proceedings of the 2020 ACM Symposium on Principles of Distributed Computing, Salerno, Italy, 2020, pp. 309–318.
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| arXiv
2020 | Conference Paper | IST-REx-ID: 8725 |
V. Aksenov, D.-A. Alistarh, A. Drozdova, and A. Mohtashami, “The splay-list: A distribution-adaptive concurrent skip-list,” in 34th International Symposium on Distributed Computing, Freiburg, Germany, 2020, vol. 179, p. 3:1-3:18.
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2020 | Conference Paper | IST-REx-ID: 9632 |
S. P. Singh and D.-A. Alistarh, “WoodFisher: Efficient second-order approximation for neural network compression,” in Advances in Neural Information Processing Systems, Vancouver, Canada, 2020, vol. 33, pp. 18098–18109.
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2020 | Conference Paper | IST-REx-ID: 9631 |
V. Aksenov, D.-A. Alistarh, and J. Korhonen, “Scalable belief propagation via relaxed scheduling,” in Advances in Neural Information Processing Systems, Vancouver, Canada, 2020, vol. 33, pp. 22361–22372.
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2020 | Conference Paper | IST-REx-ID: 9415 |
M. Kurtz et al., “Inducing and exploiting activation sparsity for fast neural network inference,” in 37th International Conference on Machine Learning, ICML 2020, Online, 2020, vol. 119, pp. 5533–5543.
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