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174 Publications
2025 |
Published |
Journal Article |
IST-REx-ID: 20704
Tuo P, Zeng Z, Chen J, Cheng B. Scalable multitemperature free energy sampling of classical Ising spin states. Journal of Chemical Theory and Computation. 2025;21(22):11427-11435. doi:10.1021/acs.jctc.5c01248
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| PubMed | Europe PMC
2025 |
Published |
Conference Paper |
IST-REx-ID: 22826 |
Rodionov G, Garipov R, Shutova A, et al. Hogwild! Inference: Parallel LLM generation via concurrent attention. In: 39th Conference on Neural Information Processing Systems. Vol 38. Neural Information Processing Systems Foundation; 2025:52014-52055. doi:10.52202/085713-1551
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2025 |
Published |
Conference Paper |
IST-REx-ID: 22830 |
Garipov R, Velikonivtsev F, Ermakov I, Svirschevski R, Egiazarian V, Ryabinin M. AutoJudge: Judge decoding without manual annotation. In: 39th Annual Conference on Neural Information Processing Systems. Vol 38. Neural Information Processing Systems Foundation; 2025:104904-104941. doi:10.52202/085713-3163
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| arXiv
2025 |
Published |
Conference Paper |
IST-REx-ID: 22827 |
Ashkboos S, Nikdan M, Tabesh S, Lopez Castro R, Hoefler T, Alistarh D-A. HALO: Hadamard-assisted lower-precision optimization for LLMs. In: 39th Conference on Neural Information Processing Systems. Vol 38. Neural Information Processing Systems Foundation; 2025:131755-131780. doi:10.52202/085713-3966
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| arXiv
2024 |
Published |
Conference Paper |
IST-REx-ID: 19518 |
Wu D, Modoranu I-V, Safaryan M, Kuznedelev D, Alistarh D-A. 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: 19519 |
Malinovskii V, Mazur D, Ilin I, et al. 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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| arXiv
2024 |
Research Data Reference |
IST-REx-ID: 19884 |
Frantar E, Castro R, Chen J, Hoefler T, Alistarh D-A. MARLIN: Mixed-precision auto-regressive parallel inference on Large Language Models. 2024. doi:10.5281/ZENODO.14213091
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2024 |
Published |
Conference Paper |
IST-REx-ID: 17332 |
Kokorin I, Yudov V, Aksenov V, Alistarh D-A. Wait-free trees with asymptotically-efficient range queries. In: 2024 IEEE International Parallel and Distributed Processing Symposium. IEEE; 2024:169-179. doi:10.1109/IPDPS57955.2024.00023
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| arXiv
2024 |
Published |
Conference Paper |
IST-REx-ID: 15011 |
Kurtic E, Hoefler T, Alistarh D-A. How to prune your language model: Recovering accuracy on the “Sparsity May Cry” benchmark. In: Proceedings of Machine Learning Research. Vol 234. ML Research Press; 2024:542-553.
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| arXiv
2024 |
Published |
Conference Paper |
IST-REx-ID: 18070
Chatterjee B, Kungurtsev V, Alistarh D-A. Federated SGD with local asynchrony. In: Proceedings of the 44th International Conference on Distributed Computing Systems. IEEE; 2024:857-868. doi:10.1109/ICDCS60910.2024.00084
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2024 |
Published |
Conference Paper |
IST-REx-ID: 17329 |
Alistarh D-A, Chatterjee K, Karrabi M, Lazarsfeld JM. Game dynamics and equilibrium computation in the population protocol model. In: Proceedings of the 43rd Annual ACM Symposium on Principles of Distributed Computing. Association for Computing Machinery; 2024:40-49. doi:10.1145/3662158.3662768
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2024 |
Published |
Conference Paper |
IST-REx-ID: 18121 |
Moakhar AS, Iofinova EB, Frantar E, Alistarh D-A. SPADE: Sparsity-guided debugging for deep neural networks. In: Proceedings of the 41st International Conference on Machine Learning. Vol 235. ML Research Press; 2024:45955-45987.
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| arXiv
2024 |
Published |
Conference Paper |
IST-REx-ID: 18976 |
Islamov R, Safaryan M, Alistarh D-A. AsGrad: A sharp unified analysis of asynchronous-SGD algorithms. In: Proceedings of The 27th International Conference on Artificial Intelligence and Statistics. Vol 238. ML Research Press; 2024:649-657.
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| arXiv
2024 |
Published |
Conference Paper |
IST-REx-ID: 18113 |
Egiazarian V, Panferov A, Kuznedelev D, Frantar E, Babenko A, Alistarh D-A. Extreme compression of large language models via additive quantization. In: Proceedings of the 41st International Conference on Machine Learning. Vol 235. ML Research Press; 2024:12284-12303.
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| arXiv
2024 |
Published |
Conference Paper |
IST-REx-ID: 18975 |
Modoranu I-V, Kalinov A, Kurtic E, Frantar E, Alistarh D-A. Error feedback can accurately compress preconditioners. In: 41st International Conference on Machine Learning. Vol 235. ML Research Press; 2024:35910-35933.
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| arXiv
2024 |
Published |
Conference Paper |
IST-REx-ID: 18977 |
Dettmers T, Svirschevski RA, Egiazarian V, et al. SpQR: A sparse-quantized representation for near-lossless LLM weight compression. In: 12th International Conference on Learning Representations. OpenReview; 2024.
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| arXiv
2024 |
Published |
Conference Paper |
IST-REx-ID: 17093 |
Zakerinia H, Talaei S, Nadiradze G, Alistarh D-A. Communication-efficient federated learning with data and client heterogeneity. In: Proceedings of the 27th International Conference on Artificial Intelligence and Statistics. Vol 238. ML Research Press; 2024:3448-3456.
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| arXiv
2024 |
Published |
Conference Paper |
IST-REx-ID: 18117 |
Nikdan M, Tabesh S, Crncevic E, Alistarh D-A. RoSA: Accurate parameter-efficient fine-tuning via robust adaptation. In: Proceedings of the 41st International Conference on Machine Learning. Vol 235. ML Research Press; 2024:38187-38206.
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| arXiv
2024 |
Published |
Thesis | PhD |
IST-REx-ID: 17485 |
Frantar E. Compressing large neural networks: Algorithms, systems and scaling laws. 2024. doi:10.15479/at:ista:17485
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2024 |
Published |
Conference Paper |
IST-REx-ID: 18062 |
Frantar E, Ruiz CR, Houlsby N, Alistarh D-A, Evci U. Scaling laws for sparsely-connected foundation models. In: The Twelfth International Conference on Learning Representations. ; 2024.
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| arXiv