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160 Publications


2025 | Published | Journal Article | IST-REx-ID: 19713 | OA
Talaei, Shayan, et al. “Hybrid Decentralized Optimization: Leveraging Both First- and Zeroth-Order Optimizers for Faster Convergence.” Proceedings of The39th AAAI Conference on Artificial Intelligence, vol. 39, no. 19, Association for the Advancement of Artificial Intelligence, 2025, pp. 20778–86, doi:10.1609/aaai.v39i19.34290.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 

2025 | Published | Conference Paper | IST-REx-ID: 20038 | OA
Jin, Tian, et al. “The Journey Matters: Average Parameter Count over Pre-Training Unifies Sparse and Dense Scaling Laws.” 13th International Conference on Learning Representations, ICLR, 2025, pp. 85165–81.
[Published Version] View | Files available | arXiv
 

2025 | Published | Conference Paper | IST-REx-ID: 20037 | OA
Sawmya, Shashata, et al. “Wasserstein Distances, Neuronal Entanglement, and Sparsity.” 13th International Conference on Learning Representations, ICLR, 2025, pp. 26244–74.
[Published Version] View | Files available | arXiv
 

2025 | Published | Conference Paper | IST-REx-ID: 20032 | OA
Chen, Jiale, et al. “Scalable Mechanistic Neural Networks.” 13th International Conference on Learning Representations, ICLR, 2025, pp. 63716–37.
[Published Version] View | Files available | arXiv
 

2025 | Published | Conference Paper | IST-REx-ID: 20034 | OA
Robert, Thomas, et al. “LDAdam: Adaptive Optimization from Low-Dimensional Gradient Statistics.” 13th International Conference on Learning Representations, ICLR, 2025, pp. 101877–913.
[Published Version] View | Files available | arXiv
 

2025 | Published | Conference Paper | IST-REx-ID: 19877 | OA
Frantar, Elias, et al. “MARLIN: Mixed-Precision Auto-Regressive Parallel Inference on Large Language Models.” Proceedings of the 30th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming, Association for Computing Machinery, 2025, pp. 239–51, doi:10.1145/3710848.3710871.
[Published Version] View | Files available | DOI | WoS | arXiv
 

2025 | Epub ahead of print | Journal Article | IST-REx-ID: 19969 | OA
Alistarh, Dan-Adrian, et al. “Near-Optimal Leader Election in Population Protocols on Graphs.” Distributed Computing, Springer Nature, 2025, doi:10.1007/s00446-025-00487-7.
[Published Version] View | Files available | DOI | Download Published Version (ext.) | WoS | arXiv
 

2025 | Published | Conference Paper | IST-REx-ID: 20684 | OA
Kurtic, Eldar, et al. “‘Give Me BF16 or Give Me Death’? Accuracy-Performance Trade-Offs in LLM Quantization.” Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, 2025, pp. 26872–86.
[Published Version] View | Files available | arXiv
 

2025 | Published | Conference Paper | IST-REx-ID: 20224 | OA
Martynov, Pavel, et al. “In the Search of Optimal Tree Networks: Hardness and Heuristics.” Proceedings of the 2025 Genetic and Evolutionary Computation Conference, Association for Computing Machinery, 2025, pp. 249–57, doi:10.1145/3712256.3726425.
[Published Version] View | Files available | DOI | WoS
 

2025 | Published | Journal Article | IST-REx-ID: 20704
Tuo, Ping, et al. “Scalable Multitemperature Free Energy Sampling of Classical Ising Spin States.” Journal of Chemical Theory and Computation, vol. 21, no. 22, American Chemical Society, 2025, pp. 11427–35, doi:10.1021/acs.jctc.5c01248.
View | Files available | DOI | WoS | PubMed | Europe PMC
 

2024 | Published | Conference Paper | IST-REx-ID: 17093 | OA
Zakerinia, Hossein, et al. “Communication-Efficient Federated Learning with Data and Client Heterogeneity.” Proceedings of the 27th International Conference on Artificial Intelligence and Statistics, vol. 238, ML Research Press, 2024, pp. 3448–56.
[Preprint] View | Download Preprint (ext.) | arXiv
 

2024 | Published | Conference Paper | IST-REx-ID: 15011 | OA
Kurtic, Eldar, et al. “How to Prune Your Language Model: Recovering Accuracy on the ‘Sparsity May Cry’ Benchmark.” Proceedings of Machine Learning Research, vol. 234, ML Research Press, 2024, pp. 542–53.
[Preprint] View | Download Preprint (ext.) | arXiv
 

2024 | Published | Conference Paper | IST-REx-ID: 18113 | OA
Egiazarian, Vage, et al. “Extreme Compression of Large Language Models via Additive Quantization.” Proceedings of the 41st International Conference on Machine Learning, vol. 235, ML Research Press, 2024, pp. 12284–303.
[Preprint] View | Download Preprint (ext.) | arXiv
 

2024 | Published | Conference Paper | IST-REx-ID: 18117 | OA
Nikdan, Mahdi, et al. “RoSA: Accurate Parameter-Efficient Fine-Tuning via Robust Adaptation.” Proceedings of the 41st International Conference on Machine Learning, vol. 235, ML Research Press, 2024, pp. 38187–206.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 

2024 | Published | Conference Paper | IST-REx-ID: 18975 | OA
Modoranu, Ionut-Vlad, et al. “Error Feedback Can Accurately Compress Preconditioners.” 41st International Conference on Machine Learning, vol. 235, ML Research Press, 2024, pp. 35910–33.
[Preprint] View | Download Preprint (ext.) | arXiv
 

2024 | Published | Conference Paper | IST-REx-ID: 18977 | OA
Dettmers, Tim, et al. “SpQR: A Sparse-Quantized Representation for near-Lossless LLM Weight Compression.” 12th International Conference on Learning Representations, OpenReview, 2024.
[Preprint] View | Download Preprint (ext.) | arXiv
 

2024 | Published | Thesis | IST-REx-ID: 17485 | OA
Frantar, Elias. Compressing Large Neural Networks : Algorithms, Systems and Scaling Laws. Institute of Science and Technology Austria, 2024, doi:10.15479/at:ista:17485.
[Published Version] View | Files available | DOI
 

2024 | Published | Conference Paper | IST-REx-ID: 18061 | OA
Frantar, Elias, and Dan-Adrian Alistarh. “QMoE: Sub-1-Bit Compression of Trillion Parameter Models.” Proceedings of Machine Learning and Systems, edited by P. Gibbons et al., vol. 6, 2024.
[Published Version] View | Files available | Download Published Version (ext.)
 

2024 | Published | Conference Paper | IST-REx-ID: 18062 | OA
Frantar, Elias, et al. “Scaling Laws for Sparsely-Connected Foundation Models.” The Twelfth International Conference on Learning Representations, 2024.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 

2024 | Published | Conference Paper | IST-REx-ID: 17329 | OA
Alistarh, Dan-Adrian, et al. “Game Dynamics and Equilibrium Computation in the Population Protocol Model.” Proceedings of the 43rd Annual ACM Symposium on Principles of Distributed Computing, Association for Computing Machinery, 2024, pp. 40–49, doi:10.1145/3662158.3662768.
[Published Version] View | Files available | DOI
 

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