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164 Publications
2025 |
Published |
Conference Paper |
IST-REx-ID: 19877 |
Frantar E, Castro RL, Chen J, Hoefler T, Alistarh D-A. 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. Association for Computing Machinery; 2025:239-251. doi:10.1145/3710848.3710871
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| arXiv
2025 |
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Journal Article |
IST-REx-ID: 19969 |
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Alistarh D-A, Rybicki J, Voitovych S. Near-optimal leader election in population protocols on graphs. Distributed Computing. 2025;38:207-245. doi:10.1007/s00446-025-00487-7
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| arXiv
2025 |
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Conference Paper |
IST-REx-ID: 20032 |
Chen J, Yao D, Pervez AA, Alistarh D-A, Locatello F. Scalable mechanistic neural networks. In: 13th International Conference on Learning Representations. ICLR; 2025:63716-63737.
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| arXiv
2025 |
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Conference Paper |
IST-REx-ID: 20034 |
Robert T, Safaryan M, Modoranu I-V, Alistarh D-A. LDAdam: Adaptive optimization from low-dimensional gradient statistics. In: 13th International Conference on Learning Representations. ICLR; 2025:101877-101913.
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| arXiv
2025 |
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Conference Paper |
IST-REx-ID: 20037 |
Sawmya S, Kong L, Markov I, Alistarh D-A, Shavit N. Wasserstein distances, neuronal entanglement, and sparsity. In: 13th International Conference on Learning Representations. ICLR; 2025:26244-26274.
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| arXiv
2025 |
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Conference Paper |
IST-REx-ID: 20038 |
Jin T, Humayun AI, Evci U, et al. The journey matters: Average parameter count over pre-training unifies sparse and dense scaling laws. In: 13th International Conference on Learning Representations. ICLR; 2025:85165-85181.
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| arXiv
2025 |
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Conference Paper |
IST-REx-ID: 20224 |
Martynov P, Buzdalov M, Pankratov S, Aksenov V, Schmid S. In the search of optimal tree networks: Hardness and heuristics. In: Proceedings of the 2025 Genetic and Evolutionary Computation Conference. Association for Computing Machinery; 2025:249-257. doi:10.1145/3712256.3726425
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2025 |
Published |
Conference Paper |
IST-REx-ID: 20684 |
Kurtic E, Marques A, Pandit S, Kurtz M, Alistarh D-A. “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. Association for Computational Linguistics; 2025:26872-26886.
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| arXiv
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 |
Journal Article |
IST-REx-ID: 19713 |
Talaei S, Ansaripour M, Nadiradze G, Alistarh D-A. Hybrid decentralized optimization: Leveraging both first- and zeroth-order optimizers for faster convergence. Proceedings of the 39th AAAI Conference on Artificial Intelligence. 2025;39(19):20778-20786. doi:10.1609/aaai.v39i19.34290
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| arXiv
2025 |
Published |
Conference Paper |
IST-REx-ID: 20820 |
Sieberling O, Kuznedelev D, Kurtic E, Alistarh D-A. EvoPress: Accurate dynamic model compression via evolutionary search. In: 42nd International Conference on Machine Learning. Vol 267. ML Research Press; 2025:55556-55590.
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| arXiv
2025 |
Published |
Conference Paper |
IST-REx-ID: 20821 |
Nguyen AD, Markov I, Wu FZ, et al. Layer-wise quantization for quantized optimistic dual averaging. In: 42nd International Conference on Machine Learning. Vol 267. ML Research Press; 2025:46026-46072.
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| arXiv
2025 |
Published |
Conference Paper |
IST-REx-ID: 21250 |
Alistarh D-A, Ellen F, Fedorov A. An almost-logarithmic lower bound for leader election with bounded value contention. In: 39th International Symposium on Distributed Computing. Vol 356. Schloss Dagstuhl - Leibniz-Zentrum für Informatik; 2025:3:1-3:16. doi:10.4230/LIPIcs.DISC.2025.3
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2025 |
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Book Chapter |
IST-REx-ID: 21257 |
Kurtic E, Kuznedelev D, Frantar E, et al. Sparse Fine-Tuning for Inference Acceleration of Large Language Models. In: Passban P, Way A, Rezagholizadeh M, eds. Enhancing LLM Performance. Efficacy, Fine-Tuning, and Inference Techniques. Springer Nature; 2025:83-97. doi:10.1007/978-3-031-85747-8_6
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| arXiv
2024 |
Published |
Conference Paper |
IST-REx-ID: 18061 |
Frantar E, Alistarh D-A. QMoE: Sub-1-bit compression of trillion parameter models. In: Gibbons P, Pekhimenko G, De Sa C, eds. Proceedings of Machine Learning and Systems. Vol 6. ; 2024.
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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
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: 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: 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 |
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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