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174 Publications
2025 |
Published |
Journal Article |
IST-REx-ID: 20704
Tuo, Ping, Zezhu Zeng, Jiale Chen, and Bingqing Cheng. “Scalable Multitemperature Free Energy Sampling of Classical Ising Spin States.” Journal of Chemical Theory and Computation. American Chemical Society, 2025. https://doi.org/10.1021/acs.jctc.5c01248.
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| PubMed | Europe PMC
2025 |
Published |
Conference Paper |
IST-REx-ID: 22826 |
Rodionov, Gleb, Roman Garipov, Alina Shutova, George Yakushev, Erik Schultheis, Vage Egiazarian, Anton Sinitsin, Denis Kuznedelev, and Dan-Adrian Alistarh. “Hogwild! Inference: Parallel LLM Generation via Concurrent Attention.” In 39th Conference on Neural Information Processing Systems, 38:52014–55. Neural Information Processing Systems Foundation, 2025. https://doi.org/10.52202/085713-1551.
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2025 |
Published |
Conference Paper |
IST-REx-ID: 22830 |
Garipov, Roman, Fedor Velikonivtsev, Ivan Ermakov, Ruslan Svirschevski, Vage Egiazarian, and Max Ryabinin. “AutoJudge: Judge Decoding without Manual Annotation.” In 39th Annual Conference on Neural Information Processing Systems, 38:104904–41. Neural Information Processing Systems Foundation, 2025. https://doi.org/10.52202/085713-3163.
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| arXiv
2025 |
Published |
Conference Paper |
IST-REx-ID: 22827 |
Ashkboos, Saleh, Mahdi Nikdan, Soroush Tabesh, Roberto Lopez Castro, Torsten Hoefler, and Dan-Adrian Alistarh. “HALO: Hadamard-Assisted Lower-Precision Optimization for LLMs.” In 39th Conference on Neural Information Processing Systems, 38:131755–80. Neural Information Processing Systems Foundation, 2025. https://doi.org/10.52202/085713-3966.
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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: 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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| arXiv
2024 |
Research Data Reference |
IST-REx-ID: 19884 |
Frantar, Elias, Roberto Castro, Jiale Chen, Torsten Hoefler, and Dan-Adrian Alistarh. “MARLIN: Mixed-Precision Auto-Regressive Parallel Inference on Large Language Models.” Zenodo, 2024. https://doi.org/10.5281/ZENODO.14213091.
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2024 |
Published |
Conference Paper |
IST-REx-ID: 17332 |
Kokorin, Ilya, Victor Yudov, Vitaly Aksenov, and Dan-Adrian Alistarh. “Wait-Free Trees with Asymptotically-Efficient Range Queries.” In 2024 IEEE International Parallel and Distributed Processing Symposium, 169–79. IEEE, 2024. https://doi.org/10.1109/IPDPS57955.2024.00023.
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| arXiv
2024 |
Published |
Conference Paper |
IST-REx-ID: 15011 |
Kurtic, Eldar, Torsten Hoefler, and Dan-Adrian Alistarh. “How to Prune Your Language Model: Recovering Accuracy on the ‘Sparsity May Cry’ Benchmark.” In Proceedings of Machine Learning Research, 234:542–53. ML Research Press, 2024.
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| arXiv
2024 |
Published |
Conference Paper |
IST-REx-ID: 18070
Chatterjee, Bapi, Vyacheslav Kungurtsev, and Dan-Adrian Alistarh. “Federated SGD with Local Asynchrony.” In Proceedings of the 44th International Conference on Distributed Computing Systems, 857–68. IEEE, 2024. https://doi.org/10.1109/ICDCS60910.2024.00084.
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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: 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 |
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: 18113 |
Egiazarian, Vage, Andrei Panferov, Denis Kuznedelev, Elias Frantar, Artem Babenko, and Dan-Adrian Alistarh. “Extreme Compression of Large Language Models via Additive Quantization.” In Proceedings of the 41st International Conference on Machine Learning, 235:12284–303. ML Research Press, 2024.
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| arXiv
2024 |
Published |
Conference Paper |
IST-REx-ID: 18975 |
Modoranu, Ionut-Vlad, Aleksei Kalinov, Eldar Kurtic, Elias Frantar, and Dan-Adrian Alistarh. “Error Feedback Can Accurately Compress Preconditioners.” In 41st International Conference on Machine Learning, 235:35910–33. ML Research Press, 2024.
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| arXiv
2024 |
Published |
Conference Paper |
IST-REx-ID: 18977 |
Dettmers, Tim, Ruslan A. Svirschevski, Vage Egiazarian, Denis Kuznedelev, Elias Frantar, Saleh Ashkboos, Alexander Borzunov, Torsten Hoefler, and Dan-Adrian Alistarh. “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, Hossein, Shayan Talaei, Giorgi Nadiradze, and Dan-Adrian Alistarh. “Communication-Efficient Federated Learning with Data and Client Heterogeneity.” In Proceedings of the 27th International Conference on Artificial Intelligence and Statistics, 238:3448–56. ML Research Press, 2024.
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| arXiv
2024 |
Published |
Conference Paper |
IST-REx-ID: 18117 |
Nikdan, Mahdi, Soroush Tabesh, Elvir Crncevic, and Dan-Adrian Alistarh. “RoSA: Accurate Parameter-Efficient Fine-Tuning via Robust Adaptation.” In Proceedings of the 41st International Conference on Machine Learning, 235:38187–206. ML Research Press, 2024.
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| arXiv
2024 |
Published |
Thesis | PhD |
IST-REx-ID: 17485 |
Frantar, Elias. “Compressing Large Neural Networks: Algorithms, Systems and Scaling Laws.” Institute of Science and Technology Austria, 2024. https://doi.org/10.15479/at:ista:17485.
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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