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160 Publications
2025 | Published | Journal Article | IST-REx-ID: 19713 | 
Talaei, Shayan, Matin Ansaripour, Giorgi Nadiradze, and Dan-Adrian Alistarh. “Hybrid Decentralized Optimization: Leveraging Both First- and Zeroth-Order Optimizers for Faster Convergence.” Proceedings of The39th AAAI Conference on Artificial Intelligence. Association for the Advancement of Artificial Intelligence, 2025. https://doi.org/10.1609/aaai.v39i19.34290.
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2025 | Published | Conference Paper | IST-REx-ID: 20038 |
Jin, Tian, Ahmed Imtiaz Humayun, Utku Evci, Suvinay Subramanian, Amir Yazdanbakhsh, Dan-Adrian Alistarh, and Gintare Karolina Dziugaite. “The Journey Matters: Average Parameter Count over Pre-Training Unifies Sparse and Dense Scaling Laws.” In 13th International Conference on Learning Representations, 85165–81. ICLR, 2025.
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2025 | Published | Conference Paper | IST-REx-ID: 20037 |
Sawmya, Shashata, Linghao Kong, Ilia Markov, Dan-Adrian Alistarh, and Nir Shavit. “Wasserstein Distances, Neuronal Entanglement, and Sparsity.” In 13th International Conference on Learning Representations, 26244–74. ICLR, 2025.
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2025 | Published | Conference Paper | IST-REx-ID: 20032 |
Chen, Jiale, Dingling Yao, Adeel A Pervez, Dan-Adrian Alistarh, and Francesco Locatello. “Scalable Mechanistic Neural Networks.” In 13th International Conference on Learning Representations, 63716–37. ICLR, 2025.
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2025 | Published | Conference Paper | IST-REx-ID: 20034 |
Robert, Thomas, Mher Safaryan, Ionut-Vlad Modoranu, and Dan-Adrian Alistarh. “LDAdam: Adaptive Optimization from Low-Dimensional Gradient Statistics.” In 13th International Conference on Learning Representations, 101877–913. ICLR, 2025.
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2025 | Published | Conference Paper | IST-REx-ID: 19877 |
Frantar, Elias, Roberto L. Castro, Jiale Chen, Torsten Hoefler, and Dan-Adrian Alistarh. “MARLIN: Mixed-Precision Auto-Regressive Parallel Inference on Large Language Models.” In Proceedings of the 30th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming, 239–51. Association for Computing Machinery, 2025. https://doi.org/10.1145/3710848.3710871.
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2025 | Epub ahead of print | Journal Article | IST-REx-ID: 19969 |
Alistarh, Dan-Adrian, Joel Rybicki, and Sasha Voitovych. “Near-Optimal Leader Election in Population Protocols on Graphs.” Distributed Computing. Springer Nature, 2025. https://doi.org/10.1007/s00446-025-00487-7.
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2025 | Published | Conference Paper | IST-REx-ID: 20684 |
Kurtic, Eldar, Alexandre Marques, Shubhra Pandit, Mark Kurtz, and Dan-Adrian Alistarh. “‘Give Me BF16 or Give Me Death’? Accuracy-Performance Trade-Offs in LLM Quantization.” In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics, 26872–86. Association for Computational Linguistics, 2025.
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2025 | Published | Conference Paper | IST-REx-ID: 20224 |
Martynov, Pavel, Maxim Buzdalov, Sergei Pankratov, Vitaliy Aksenov, and Stefan Schmid. “In the Search of Optimal Tree Networks: Hardness and Heuristics.” In Proceedings of the 2025 Genetic and Evolutionary Computation Conference, 249–57. Association for Computing Machinery, 2025. https://doi.org/10.1145/3712256.3726425.
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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
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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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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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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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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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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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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2024 | Published | Thesis | 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: 18061 |
Frantar, Elias, and Dan-Adrian Alistarh. “QMoE: Sub-1-Bit Compression of Trillion Parameter Models.” In Proceedings of Machine Learning and Systems, edited by P. Gibbons, G. Pekhimenko, and C. De Sa, Vol. 6, 2024.
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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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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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