7 Publications

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[7]
2024 | Published | Conference Paper | IST-REx-ID: 17456 | OA
L-GreCo: Layerwise-adaptive gradient compression for efficient data-parallel deep learning
I. Markov, K. Alimohammadi, E. Frantar, D.-A. Alistarh, in:, P. Gibbons, G. Pekhimenko, C. De Sa (Eds.), Proceedings of Machine Learning and Systems , Association for Computing Machinery, 2024.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[6]
2024 | Published | Thesis | IST-REx-ID: 17490 | OA
Communication-efficient distributed training of deep neural networks: An algorithms and systems perspective
I. Markov, Communication-Efficient Distributed Training of Deep Neural Networks: An Algorithms and Systems Perspective, Institute of Science and Technology Austria, 2024.
[Published Version] View | Files available | DOI
 
[5]
2023 | Published | Conference Paper | IST-REx-ID: 14461 | OA
Quantized distributed training of large models with convergence guarantees
I. Markov, A. Vladu, Q. Guo, D.-A. Alistarh, in:, Proceedings of the 40th International Conference on Machine Learning, ML Research Press, 2023, pp. 24020–24044.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[4]
2022 | Published | Conference Paper | IST-REx-ID: 12780 | OA
CGX: Adaptive system support for communication-efficient deep learning
I. Markov, H. Ramezanikebrya, D.-A. Alistarh, in:, Proceedings of the 23rd ACM/IFIP International Middleware Conference, Association for Computing Machinery, 2022, pp. 241–254.
[Published Version] View | Files available | DOI | arXiv
 
[3]
2021 | Published | Conference Paper | IST-REx-ID: 10049 | OA
Keep the dirt: tainted TreeKEM, adaptively and actively secure continuous group key agreement
K. Klein, G. Pascual Perez, M. Walter, C. Kamath Hosdurg, M. Capretto, M. Cueto Noval, I. Markov, M.X. Yeo, J.F. Alwen, K.Z. Pietrzak, in:, 2021 IEEE Symposium on Security and Privacy , IEEE, 2021, pp. 268–284.
[Preprint] View | Files available | DOI | Download Preprint (ext.)
 
[2]
2021 | Published | Conference Paper | IST-REx-ID: 10432 | OA
Elastic consistency: A practical consistency model for distributed stochastic gradient descent
G. Nadiradze, I. Markov, B. Chatterjee, V. Kungurtsev, D.-A. Alistarh, in:, Proceedings of the AAAI Conference on Artificial Intelligence, 2021, pp. 9037–9045.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[1]
2020 | Published | Conference Paper | IST-REx-ID: 15086 | OA
Adaptive gradient quantization for data-parallel SGD
F. Faghri, I. Tabrizian, I. Markov, D.-A. Alistarh, D. Roy, A. Ramezani-Kebrya, in:, Advances in Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2020.
[Preprint] View | Download Preprint (ext.) | arXiv
 

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7 Publications

Mark all

[7]
2024 | Published | Conference Paper | IST-REx-ID: 17456 | OA
L-GreCo: Layerwise-adaptive gradient compression for efficient data-parallel deep learning
I. Markov, K. Alimohammadi, E. Frantar, D.-A. Alistarh, in:, P. Gibbons, G. Pekhimenko, C. De Sa (Eds.), Proceedings of Machine Learning and Systems , Association for Computing Machinery, 2024.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[6]
2024 | Published | Thesis | IST-REx-ID: 17490 | OA
Communication-efficient distributed training of deep neural networks: An algorithms and systems perspective
I. Markov, Communication-Efficient Distributed Training of Deep Neural Networks: An Algorithms and Systems Perspective, Institute of Science and Technology Austria, 2024.
[Published Version] View | Files available | DOI
 
[5]
2023 | Published | Conference Paper | IST-REx-ID: 14461 | OA
Quantized distributed training of large models with convergence guarantees
I. Markov, A. Vladu, Q. Guo, D.-A. Alistarh, in:, Proceedings of the 40th International Conference on Machine Learning, ML Research Press, 2023, pp. 24020–24044.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[4]
2022 | Published | Conference Paper | IST-REx-ID: 12780 | OA
CGX: Adaptive system support for communication-efficient deep learning
I. Markov, H. Ramezanikebrya, D.-A. Alistarh, in:, Proceedings of the 23rd ACM/IFIP International Middleware Conference, Association for Computing Machinery, 2022, pp. 241–254.
[Published Version] View | Files available | DOI | arXiv
 
[3]
2021 | Published | Conference Paper | IST-REx-ID: 10049 | OA
Keep the dirt: tainted TreeKEM, adaptively and actively secure continuous group key agreement
K. Klein, G. Pascual Perez, M. Walter, C. Kamath Hosdurg, M. Capretto, M. Cueto Noval, I. Markov, M.X. Yeo, J.F. Alwen, K.Z. Pietrzak, in:, 2021 IEEE Symposium on Security and Privacy , IEEE, 2021, pp. 268–284.
[Preprint] View | Files available | DOI | Download Preprint (ext.)
 
[2]
2021 | Published | Conference Paper | IST-REx-ID: 10432 | OA
Elastic consistency: A practical consistency model for distributed stochastic gradient descent
G. Nadiradze, I. Markov, B. Chatterjee, V. Kungurtsev, D.-A. Alistarh, in:, Proceedings of the AAAI Conference on Artificial Intelligence, 2021, pp. 9037–9045.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[1]
2020 | Published | Conference Paper | IST-REx-ID: 15086 | OA
Adaptive gradient quantization for data-parallel SGD
F. Faghri, I. Tabrizian, I. Markov, D.-A. Alistarh, D. Roy, A. Ramezani-Kebrya, in:, Advances in Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2020.
[Preprint] View | Download Preprint (ext.) | arXiv
 

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