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152 Publications
2024 | Published | Conference Paper | IST-REx-ID: 18977 | 
T. Dettmers et al., “SpQR: A sparse-quantized representation for near-lossless LLM weight compression,” in 12th International Conference on Learning Representations, Vienna, Austria, 2024.
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2024 | Published | Thesis | IST-REx-ID: 17485 |
E. Frantar, “Compressing large neural networks : Algorithms, systems and scaling laws,” Institute of Science and Technology Austria, 2024.
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2024 | Published | Conference Paper | IST-REx-ID: 18061 |
E. Frantar and D.-A. Alistarh, “QMoE: Sub-1-bit compression of trillion parameter models,” in Proceedings of Machine Learning and Systems, Santa Clara, CA, USA, 2024, vol. 6.
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2024 | Published | Conference Paper | IST-REx-ID: 18062 |
E. Frantar, C. R. Ruiz, N. Houlsby, D.-A. Alistarh, and U. Evci, “Scaling laws for sparsely-connected foundation models,” in The Twelfth International Conference on Learning Representations, Vienna, Austria, 2024.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 17329 |
D.-A. Alistarh, K. Chatterjee, M. Karrabi, and J. M. Lazarsfeld, “Game dynamics and equilibrium computation in the population protocol model,” in Proceedings of the 43rd Annual ACM Symposium on Principles of Distributed Computing, Nantes, France, 2024, pp. 40–49.
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2024 | Published | Conference Paper | IST-REx-ID: 18976 |
R. Islamov, M. Safaryan, and D.-A. Alistarh, “AsGrad: A sharp unified analysis of asynchronous-SGD algorithms,” in Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, Valencia, Spain, 2024, vol. 238, pp. 649–657.
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| arXiv
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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