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148 Publications
2024 | Published | Conference Paper | IST-REx-ID: 18062 | 
Frantar, Elias, Carlos Riquelme Ruiz, Neil Houlsby, Dan-Adrian Alistarh, and Utku Evci. “Scaling Laws for Sparsely-Connected Foundation Models.” In The Twelfth International Conference on Learning Representations, 2024.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 17329 |
Alistarh, Dan-Adrian, Krishnendu Chatterjee, Mehrdad Karrabi, and John 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, 40–49. Association for Computing Machinery, 2024. https://doi.org/10.1145/3662158.3662768.
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2024 | Published | Conference Paper | IST-REx-ID: 18976 |
Islamov, Rustem, Mher Safaryan, and Dan-Adrian Alistarh. “AsGrad: A Sharp Unified Analysis of Asynchronous-SGD Algorithms.” In Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, 238:649–57. ML Research Press, 2024.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18121 |
Moakhar, Arshia Soltani, Eugenia B Iofinova, Elias Frantar, and Dan-Adrian Alistarh. “SPADE: Sparsity-Guided Debugging for Deep Neural Networks.” In Proceedings of the 41st International Conference on Machine Learning, 235:45955–87. ML Research Press, 2024.
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| arXiv
2024 | Published | Thesis | IST-REx-ID: 17490 |
Markov, Ilia. “Communication-Efficient Distributed Training of Deep Neural Networks : An Algorithms and Systems Perspective.” Institute of Science and Technology Austria, 2024. https://doi.org/10.15479/at:ista:17490.
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2024 | Published | Conference Paper | IST-REx-ID: 17456 |
Markov, Ilia, Kaveh Alimohammadi, Elias Frantar, and Dan-Adrian Alistarh. “L-GreCo: Layerwise-Adaptive Gradient Compression for Efficient Data-Parallel Deep Learning.” In Proceedings of Machine Learning and Systems , edited by P. Gibbons, G. Pekhimenko, and C. De Sa, Vol. 6. Association for Computing Machinery, 2024.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 19518 |
Wu, Diyuan, Ionut-Vlad Modoranu, Mher Safaryan, Denis Kuznedelev, and Dan-Adrian Alistarh. “The Iterative Optimal Brain Surgeon: Faster Sparse Recovery by Leveraging Second-Order Information.” In 38th Conference on Neural Information Processing Systems, Vol. 37. Neural Information Processing Systems Foundation, 2024.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 19510 |
Modoranu, Ionut-Vlad, Mher Safaryan, Grigory Malinovsky, Eldar Kurtic, Thomas Robert, Peter Richtárik, and Dan-Adrian Alistarh. “MICROADAM: Accurate Adaptive Optimization with Low Space Overhead and Provable Convergence.” In 38th Conference on Neural Information Processing Systems, Vol. 37. Neural Information Processing Systems Foundation, 2024.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 19511 |
Ashkboos, Saleh, Amirkeivan Mohtashami, Maximilian L. Croci, Bo Li, Pashmina Cameron, Martin Jaggi, Dan-Adrian Alistarh, Torsten Hoefler, and James Hensman. “QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs.” In 38th Conference on Neural Information Processing Systems, Vol. 37. Neural Information Processing Systems Foundation, 2024.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 19519 |
Malinovskii, Vladimir, Denis Mazur, Ivan Ilin, Denis Kuznedelev, Konstantin Burlachenko, Kai Yi, Dan-Adrian Alistarh, and Peter Richtarik. “PV-Tuning: Beyond Straight-through Estimation for Extreme LLM Compression.” In 38th Conference on Neural Information Processing Systems, Vol. 37. Neural Information Processing Systems Foundation, 2024.
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2024 | Published | Thesis | IST-REx-ID: 17465 |
Shevchenko, Alexander. “High-Dimensional Limits in Artificial Neural Networks.” Institute of Science and Technology Austria, 2024. https://doi.org/10.15479/at:ista:17465.
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2024 | Published | Conference Paper | IST-REx-ID: 17469 |
Kögler, Kevin, Alexander Shevchenko, Hamed Hassani, and Marco Mondelli. “Compression of Structured Data with Autoencoders: Provable Benefit of Nonlinearities and Depth.” In Proceedings of the 41st International Conference on Machine Learning, 235:24964–15. ML Research Press, 2024.
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| arXiv
2023 | Published | Journal Article | IST-REx-ID: 13179 |
Koval, Nikita, Dmitry Khalanskiy, and Dan-Adrian Alistarh. “CQS: A Formally-Verified Framework for Fair and Abortable Synchronization.” Proceedings of the ACM on Programming Languages. Association for Computing Machinery , 2023. https://doi.org/10.1145/3591230.
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2023 | Published | Conference Paper | IST-REx-ID: 13262 |
Fedorov, Alexander, Diba Hashemi, Giorgi Nadiradze, and Dan-Adrian Alistarh. “Provably-Efficient and Internally-Deterministic Parallel Union-Find.” In Proceedings of the 35th ACM Symposium on Parallelism in Algorithms and Architectures, 261–71. Association for Computing Machinery, 2023. https://doi.org/10.1145/3558481.3591082.
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| arXiv
2023 | Published | Conference Paper | IST-REx-ID: 14260 |
Koval, Nikita, Alexander Fedorov, Maria Sokolova, Dmitry Tsitelov, and Dan-Adrian Alistarh. “Lincheck: A Practical Framework for Testing Concurrent Data Structures on JVM.” In 35th International Conference on Computer Aided Verification , 13964:156–69. Springer Nature, 2023. https://doi.org/10.1007/978-3-031-37706-8_8.
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2023 | Published | Journal Article | IST-REx-ID: 12330 |
Aksenov, Vitalii, Dan-Adrian Alistarh, Alexandra Drozdova, and Amirkeivan Mohtashami. “The Splay-List: A Distribution-Adaptive Concurrent Skip-List.” Distributed Computing. Springer Nature, 2023. https://doi.org/10.1007/s00446-022-00441-x.
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| arXiv
2023 | Published | Conference Paper | IST-REx-ID: 12735 |
Koval, Nikita, Dan-Adrian Alistarh, and Roman Elizarov. “Fast and Scalable Channels in Kotlin Coroutines.” In Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 107–18. Association for Computing Machinery, 2023. https://doi.org/10.1145/3572848.3577481.
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| arXiv
2023 | Published | Conference Poster | IST-REx-ID: 12736 |
Aksenov, Vitaly, Trevor A Brown, Alexander Fedorov, and Ilya Kokorin. Unexpected Scaling in Path Copying Trees. Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. Association for Computing Machinery, 2023. https://doi.org/10.1145/3572848.3577512.
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2023 | Published | Journal Article | IST-REx-ID: 14815 |
Beznosikov, Aleksandr, Samuel Horvath, Peter Richtarik, and Mher Safaryan. “On Biased Compression for Distributed Learning.” Journal of Machine Learning Research. Journal of Machine Learning Research, 2023.
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| arXiv
2023 | Research Data Reference | IST-REx-ID: 14995 |
Koval, Nikita, Alexander Fedorov, Maria Sokolova, Dmitry Tsitelov, and Dan-Adrian Alistarh. “Lincheck: A Practical Framework for Testing Concurrent Data Structures on JVM.” Zenodo, 2023. https://doi.org/10.5281/ZENODO.7877757.
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