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156 Publications
2024 | Published | Conference Paper | IST-REx-ID: 18121 | 
A. S. Moakhar, E. B. Iofinova, E. Frantar, and D.-A. Alistarh, “SPADE: Sparsity-guided debugging for deep neural networks,” in Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 45955–45987.
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| arXiv
2024 | Published | Thesis | IST-REx-ID: 17490 |
I. Markov, “Communication-efficient distributed training of deep neural networks : An algorithms and systems perspective,” Institute of Science and Technology Austria, 2024.
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2024 | Published | Conference Paper | IST-REx-ID: 17456 |
I. Markov, K. Alimohammadi, E. Frantar, and D.-A. Alistarh, “L-GreCo: Layerwise-adaptive gradient compression for efficient data-parallel deep learning,” in Proceedings of Machine Learning and Systems , Athens, Greece, 2024, vol. 6.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 19518 |
D. Wu, I.-V. Modoranu, M. Safaryan, D. Kuznedelev, and D.-A. Alistarh, “The iterative optimal brain surgeon: Faster sparse recovery by leveraging second-order information,” in 38th Conference on Neural Information Processing Systems, Vancouver, Canada, 2024, vol. 37.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 19510 |
I.-V. Modoranu et al., “MICROADAM: Accurate adaptive optimization with low space overhead and provable convergence,” in 38th Conference on Neural Information Processing Systems, 2024, vol. 37.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 19511 |
S. Ashkboos et al., “QuaRot: Outlier-free 4-bit inference in rotated LLMs,” in 38th Conference on Neural Information Processing Systems, Vancouver, Canada, 2024, vol. 37.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 19519 |
V. Malinovskii et al., “PV-tuning: Beyond straight-through estimation for extreme LLM compression,” in 38th Conference on Neural Information Processing Systems, Vancouver, Canada, 2024, vol. 37.
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| arXiv
2024 | Research Data Reference | IST-REx-ID: 19884 |
E. Frantar, R. Castro, J. Chen, T. Hoefler, and D.-A. Alistarh, “MARLIN: Mixed-precision auto-regressive parallel inference on Large Language Models.” Zenodo, 2024.
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2024 | Published | Thesis | IST-REx-ID: 17465 |
A. Shevchenko, “High-dimensional limits in artificial neural networks,” Institute of Science and Technology Austria, 2024.
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2024 | Published | Conference Paper | IST-REx-ID: 17469 |
K. Kögler, A. Shevchenko, H. Hassani, and M. Mondelli, “Compression of structured data with autoencoders: Provable benefit of nonlinearities and depth,” in Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 24964–25015.
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| arXiv
2023 | Published | Journal Article | IST-REx-ID: 13179 |
N. Koval, D. Khalanskiy, and D.-A. Alistarh, “CQS: A formally-verified framework for fair and abortable synchronization,” Proceedings of the ACM on Programming Languages, vol. 7. Association for Computing Machinery , 2023.
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2023 | Published | Conference Paper | IST-REx-ID: 13262 |
A. Fedorov, D. Hashemi, G. Nadiradze, and D.-A. Alistarh, “Provably-efficient and internally-deterministic parallel Union-Find,” in Proceedings of the 35th ACM Symposium on Parallelism in Algorithms and Architectures, Orlando, FL, United States, 2023, pp. 261–271.
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| arXiv
2023 | Published | Conference Paper | IST-REx-ID: 14260 |
N. Koval, A. Fedorov, M. Sokolova, D. Tsitelov, and D.-A. Alistarh, “Lincheck: A practical framework for testing concurrent data structures on JVM,” in 35th International Conference on Computer Aided Verification , Paris, France, 2023, vol. 13964, pp. 156–169.
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2023 | Published | Journal Article | IST-REx-ID: 12330 |
V. Aksenov, D.-A. Alistarh, A. Drozdova, and A. Mohtashami, “The splay-list: A distribution-adaptive concurrent skip-list,” Distributed Computing, vol. 36. Springer Nature, pp. 395–418, 2023.
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| arXiv
2023 | Published | Conference Paper | IST-REx-ID: 12735 |
N. Koval, D.-A. Alistarh, and R. Elizarov, “Fast and scalable channels in Kotlin Coroutines,” in Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, Montreal, QC, Canada, 2023, pp. 107–118.
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| arXiv
2023 | Published | Conference Poster | IST-REx-ID: 12736 |
V. Aksenov, T. A. Brown, A. Fedorov, and I. Kokorin, Unexpected scaling in path copying trees. Association for Computing Machinery, 2023, pp. 438–440.
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2023 | Published | Journal Article | IST-REx-ID: 14815 |
A. Beznosikov, S. Horvath, P. Richtarik, and M. Safaryan, “On biased compression for distributed learning,” Journal of Machine Learning Research, vol. 24. Journal of Machine Learning Research, pp. 1–50, 2023.
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| arXiv
2023 | Research Data Reference | IST-REx-ID: 14995 |
N. Koval, A. Fedorov, M. Sokolova, D. Tsitelov, and D.-A. Alistarh, “Lincheck: A practical framework for testing concurrent data structures on JVM.” Zenodo, 2023.
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2023 | Published | Conference Paper | IST-REx-ID: 14460 |
M. Nikdan, T. Pegolotti, E. B. Iofinova, E. Kurtic, and D.-A. Alistarh, “SparseProp: Efficient sparse backpropagation for faster training of neural networks at the edge,” in Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, HI, United States, 2023, vol. 202, pp. 26215–26227.
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| arXiv
2023 | Published | Conference Paper | IST-REx-ID: 17378 |
E. Frantar, S. Ashkboos, T. Hoefler, and D.-A. Alistarh, “OPTQ: Accurate post-training quantization for generative pre-trained transformers,” in 11th International Conference on Learning Representations , Kigali, Rwanda, 2023.
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