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156 Publications
2021 | Published | 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 | Published | 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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| arXiv
2021 | Published | 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 | Published | Conference Paper | IST-REx-ID: 11458 |
A. Krumes, 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 | Published | 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 | Published | 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 | Published | 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 | Published | 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 | Published | 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 | Published | Thesis | IST-REx-ID: 10429 |
G. Nadiradze, “On achieving scalability through relaxation,” Institute of Science and Technology Austria, 2021.
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2021 | Published | Journal Article | IST-REx-ID: 10180 |
T. Hoefler, D.-A. Alistarh, T. Ben-Nun, N. Dryden, and A. Krumes, “Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks,” Journal of Machine Learning Research, vol. 22, no. 241. ML Research Press, pp. 1–124, 2021.
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| arXiv
2021 | Published | 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 | Published | 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 | Published | 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 | Published | 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 | Published | 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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2020 | Published | Conference Paper | IST-REx-ID: 8191
D.-A. Alistarh, T. A. Brown, and N. Singhal, “Memory tagging: Minimalist synchronization for scalable concurrent data structures,” in Annual ACM Symposium on Parallelism in Algorithms and Architectures, Virtual Event, United States, 2020, no. 7, pp. 37–49.
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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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