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Ionut-Vlad Modoranu

Andrei Panferov

Sergei Pankratov

Dan Alistarh

Mahdi Nikdan

Eldar Kurtic

Soroush Tabesh

Alexander Fedorov

Vage Egiazarian

Jiale Chen

Alexandra Volkova

Erik Schultheis

Elias Frantar

Mher Safaryan

Trevor Brown

Sokolova, Mariia

Joel Rybicki

Peter Davies

Sidak Singh

Martin Töpfer

Elvir Crncevic

Alexandra Krumes

Vitalii Aksenov

Roberto Lopez Castro

Janne Korhonen

Bapi Chatterjee

Sharareh Alipour

Ilia Markov

Giorgi Nadiradze



164 Publications

2025 | Published | Conference Paper | IST-REx-ID: 19877 | OA
E. Frantar, R. L. Castro, J. Chen, T. Hoefler, and D.-A. 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, Las Vegas, NV, United States, 2025, pp. 239–251.
[Published Version] View | Files available | DOI | WoS | arXiv
 
2025 | Published | Journal Article | IST-REx-ID: 19969 | OA | PlanS
D.-A. Alistarh, J. Rybicki, and S. Voitovych, “Near-optimal leader election in population protocols on graphs,” Distributed Computing, vol. 38. Springer Nature, pp. 207–245, 2025.
[Published Version] View | Files available | DOI | WoS | arXiv
 
2025 | Published | Conference Paper | IST-REx-ID: 20032 | OA
J. Chen, D. Yao, A. A. Pervez, D.-A. Alistarh, and F. Locatello, “Scalable mechanistic neural networks,” in 13th International Conference on Learning Representations, Singapore, Singapore, 2025, pp. 63716–63737.
[Published Version] View | Files available | arXiv
 
2025 | Published | Conference Paper | IST-REx-ID: 20034 | OA
T. Robert, M. Safaryan, I.-V. Modoranu, and D.-A. Alistarh, “LDAdam: Adaptive optimization from low-dimensional gradient statistics,” in 13th International Conference on Learning Representations, Singapore, Singapore, 2025, pp. 101877–101913.
[Published Version] View | Files available | arXiv
 
2025 | Published | Conference Paper | IST-REx-ID: 20037 | OA
S. Sawmya, L. Kong, I. Markov, D.-A. Alistarh, and N. Shavit, “Wasserstein distances, neuronal entanglement, and sparsity,” in 13th International Conference on Learning Representations, Singapore, Singapore, 2025, pp. 26244–26274.
[Published Version] View | Files available | arXiv
 
2025 | Published | Conference Paper | IST-REx-ID: 20038 | OA
T. Jin et al., “The journey matters: Average parameter count over pre-training unifies sparse and dense scaling laws,” in 13th International Conference on Learning Representations, Singapore, Singapore, 2025, pp. 85165–85181.
[Published Version] View | Files available | arXiv
 
2025 | Published | Conference Paper | IST-REx-ID: 20224 | OA
P. Martynov, M. Buzdalov, S. Pankratov, V. Aksenov, and S. Schmid, “In the search of optimal tree networks: Hardness and heuristics,” in Proceedings of the 2025 Genetic and Evolutionary Computation Conference, Malaga, Spain, 2025, pp. 249–257.
[Published Version] View | Files available | DOI | WoS
 
2025 | Published | Conference Paper | IST-REx-ID: 20684 | OA
E. Kurtic, A. Marques, S. Pandit, M. Kurtz, and D.-A. 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, Vienna, Austria, 2025, pp. 26872–26886.
[Published Version] View | Files available | arXiv
 
2025 | Published | Journal Article | IST-REx-ID: 20704
P. Tuo, Z. Zeng, J. Chen, and B. Cheng, “Scalable multitemperature free energy sampling of classical Ising spin states,” Journal of Chemical Theory and Computation, vol. 21, no. 22. American Chemical Society, pp. 11427–11435, 2025.
View | Files available | DOI | WoS | PubMed | Europe PMC
 
2025 | Published | Journal Article | IST-REx-ID: 19713 | OA
S. Talaei, M. Ansaripour, G. Nadiradze, and D.-A. Alistarh, “Hybrid decentralized optimization: Leveraging both first- and zeroth-order optimizers for faster convergence,” Proceedings of the 39th AAAI Conference on Artificial Intelligence, vol. 39, no. 19. Association for the Advancement of Artificial Intelligence, pp. 20778–20786, 2025.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
2025 | Published | Conference Paper | IST-REx-ID: 20820 | OA
O. Sieberling, D. Kuznedelev, E. Kurtic, and D.-A. Alistarh, “EvoPress: Accurate dynamic model compression via evolutionary search,” in 42nd International Conference on Machine Learning, Vancouver, Canada, 2025, vol. 267, pp. 55556–55590.
[Published Version] View | Files available | arXiv
 
2025 | Published | Conference Paper | IST-REx-ID: 20821 | OA
A. D. Nguyen et al., “Layer-wise quantization for quantized optimistic dual averaging,” in 42nd International Conference on Machine Learning, Vancouver, Canada, 2025, vol. 267, pp. 46026–46072.
[Published Version] View | Files available | arXiv
 
2025 | Published | Conference Paper | IST-REx-ID: 21250 | OA
D.-A. Alistarh, F. Ellen, and A. Fedorov, “An almost-logarithmic lower bound for leader election with bounded value contention,” in 39th International Symposium on Distributed Computing, Berlin, Germany, 2025, vol. 356, p. 3:1-3:16.
[Published Version] View | Files available | DOI
 
2025 | Published | Book Chapter | IST-REx-ID: 21257 | OA
E. Kurtic et al., “Sparse Fine-Tuning for Inference Acceleration of Large Language Models,” in Enhancing LLM Performance. Efficacy, Fine-Tuning, and Inference Techniques, P. Passban, A. Way, and M. Rezagholizadeh, Eds. Springer Nature, 2025, pp. 83–97.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
2024 | Published | Conference Paper | IST-REx-ID: 18061 | OA
E. Frantar and D.-A. Alistarh, “QMoE: Sub-1-bit compression of trillion parameter models,” in Proceedings of Machine Learning and Systems, Santa Clara, CA, USA, 2024, vol. 6.
[Published Version] View | Files available | Download Published Version (ext.)
 
2024 | Published | Conference Paper | IST-REx-ID: 18062 | OA
E. Frantar, C. R. Ruiz, N. Houlsby, D.-A. Alistarh, and U. Evci, “Scaling laws for sparsely-connected foundation models,” in The Twelfth International Conference on Learning Representations, Vienna, Austria, 2024.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
2024 | Published | Conference Paper | IST-REx-ID: 18070
B. Chatterjee, V. Kungurtsev, and D.-A. Alistarh, “Federated SGD with local asynchrony,” in Proceedings of the 44th International Conference on Distributed Computing Systems, Jersey City, NJ, United States, 2024, pp. 857–868.
View | DOI | WoS
 
2024 | Published | Conference Paper | IST-REx-ID: 18113 | OA
V. Egiazarian, A. Panferov, D. Kuznedelev, E. Frantar, A. Babenko, and D.-A. Alistarh, “Extreme compression of large language models via additive quantization,” in Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 12284–12303.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2024 | Published | Conference Paper | IST-REx-ID: 18117 | OA
M. Nikdan, S. Tabesh, E. Crncevic, and D.-A. Alistarh, “RoSA: Accurate parameter-efficient fine-tuning via robust adaptation,” in Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 38187–38206.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
2024 | Published | Conference Paper | IST-REx-ID: 18121 | OA
A. S. Moakhar, E. B. Iofinova, E. Frantar, and D.-A. Alistarh, “SPADE: Sparsity-guided debugging for deep neural networks,” in Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 45955–45987.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 

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