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156 Publications

2025 | Published | Journal Article | IST-REx-ID: 19713 | OA
Hybrid decentralized optimization: Leveraging both first- and zeroth-order optimizers for faster convergence
S. Talaei, M. Ansaripour, G. Nadiradze, D.-A. Alistarh, Proceedings of The39th AAAI Conference on Artificial Intelligence 39 (2025) 20778–20786.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
2025 | Published | Conference Paper | IST-REx-ID: 19877 | OA
MARLIN: Mixed-precision auto-regressive parallel inference on Large Language Models
E. Frantar, R.L. Castro, J. Chen, T. Hoefler, D.-A. Alistarh, in:, Proceedings of the 30th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming, Association for Computing Machinery, 2025, pp. 239–251.
[Published Version] View | Files available | DOI | arXiv
 
2025 | Epub ahead of print | Journal Article | IST-REx-ID: 19969 | OA
Near-optimal leader election in population protocols on graphs
D.-A. Alistarh, J. Rybicki, S. Voitovych, Distributed Computing (2025).
[Published Version] View | Files available | DOI | Download Published Version (ext.) | arXiv
 
2025 | Published | Conference Paper | IST-REx-ID: 20038 | OA
The journey matters: Average parameter count over pre-training unifies sparse and dense scaling laws
T. Jin, A.I. Humayun, U. Evci, S. Subramanian, A. Yazdanbakhsh, D.-A. Alistarh, G.K. Dziugaite, in:, 13th International Conference on Learning Representations, ICLR, 2025, pp. 85165–85181.
[Published Version] View | Files available | arXiv
 
2025 | Published | Conference Paper | IST-REx-ID: 20037 | OA
Wasserstein distances, neuronal entanglement, and sparsity
S. Sawmya, L. Kong, I. Markov, D.-A. Alistarh, N. Shavit, in:, 13th International Conference on Learning Representations, ICLR, 2025, pp. 26244–26274.
[Published Version] View | Files available | arXiv
 
2025 | Published | Conference Paper | IST-REx-ID: 20032 | OA
Scalable mechanistic neural networks
J. Chen, D. Yao, A.A. Pervez, D.-A. Alistarh, F. Locatello, in:, 13th International Conference on Learning Representations, ICLR, 2025, pp. 63716–63737.
[Published Version] View | Files available | arXiv
 
2025 | Published | Conference Paper | IST-REx-ID: 20034 | OA
LDAdam: Adaptive optimization from low-dimensional gradient statistics
T. Robert, M. Safaryan, I.-V. Modoranu, D.-A. Alistarh, in:, 13th International Conference on Learning Representations, ICLR, 2025, pp. 101877–101913.
[Published Version] View | Files available | arXiv
 
2024 | Published | Conference Paper | IST-REx-ID: 17093 | OA
Communication-efficient federated learning with data and client heterogeneity
H. Zakerinia, S. Talaei, G. Nadiradze, D.-A. Alistarh, in:, Proceedings of the 27th International Conference on Artificial Intelligence and Statistics, ML Research Press, 2024, pp. 3448–3456.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2024 | Published | Conference Paper | IST-REx-ID: 17332 | OA
Wait-free trees with asymptotically-efficient range queries
I. Kokorin, V. Yudov, V. Aksenov, D.-A. Alistarh, in:, 2024 IEEE International Parallel and Distributed Processing Symposium, IEEE, 2024, pp. 169–179.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
2024 | Published | Conference Paper | IST-REx-ID: 15011 | OA
How to prune your language model: Recovering accuracy on the "Sparsity May Cry" benchmark
E. Kurtic, T. Hoefler, D.-A. Alistarh, in:, Proceedings of Machine Learning Research, ML Research Press, 2024, pp. 542–553.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2024 | Published | Conference Paper | IST-REx-ID: 18070
Federated SGD with local asynchrony
B. Chatterjee, V. Kungurtsev, D.-A. Alistarh, in:, Proceedings of the 44th International Conference on Distributed Computing Systems, IEEE, 2024, pp. 857–868.
View | DOI
 
2024 | Published | Conference Paper | IST-REx-ID: 18113 | OA
Extreme compression of large language models via additive quantization
V. Egiazarian, A. Panferov, D. Kuznedelev, E. Frantar, A. Babenko, D.-A. Alistarh, in:, Proceedings of the 41st International Conference on Machine Learning, ML Research Press, 2024, pp. 12284–12303.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2024 | Published | Conference Paper | IST-REx-ID: 18117 | OA
RoSA: Accurate parameter-efficient fine-tuning via robust adaptation
M. Nikdan, S. Tabesh, E. Crncevic, D.-A. Alistarh, in:, Proceedings of the 41st International Conference on Machine Learning, ML Research Press, 2024, pp. 38187–38206.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
2024 | Published | Conference Paper | IST-REx-ID: 18975 | OA
Error feedback can accurately compress preconditioners
I.-V. Modoranu, A. Kalinov, E. Kurtic, E. Frantar, D.-A. Alistarh, in:, 41st International Conference on Machine Learning, ML Research Press, 2024, pp. 35910–35933.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2024 | Published | Conference Paper | IST-REx-ID: 18977 | OA
SpQR: A sparse-quantized representation for near-lossless LLM weight compression
T. Dettmers, R.A. Svirschevski, V. Egiazarian, D. Kuznedelev, E. Frantar, S. Ashkboos, A. Borzunov, T. Hoefler, D.-A. Alistarh, in:, 12th International Conference on Learning Representations, OpenReview, 2024.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2024 | Published | Thesis | IST-REx-ID: 17485 | OA
Compressing large neural networks : Algorithms, systems and scaling laws
E. Frantar, Compressing Large Neural Networks : Algorithms, Systems and Scaling Laws, Institute of Science and Technology Austria, 2024.
[Published Version] View | Files available | DOI
 
2024 | Published | Conference Paper | IST-REx-ID: 18061 | OA
QMoE: Sub-1-bit compression of trillion parameter models
E. Frantar, D.-A. Alistarh, in:, P. Gibbons, G. Pekhimenko, C. De Sa (Eds.), Proceedings of Machine Learning and Systems, 2024.
[Published Version] View | Files available | Download Published Version (ext.)
 
2024 | Published | Conference Paper | IST-REx-ID: 18062 | OA
Scaling laws for sparsely-connected foundation models
E. Frantar, C.R. Ruiz, N. Houlsby, D.-A. Alistarh, U. Evci, in:, 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
Game dynamics and equilibrium computation in the population protocol model
D.-A. Alistarh, K. Chatterjee, M. Karrabi, J.M. Lazarsfeld, in:, Proceedings of the 43rd Annual ACM Symposium on Principles of Distributed Computing, Association for Computing Machinery, 2024, pp. 40–49.
[Published Version] View | Files available | DOI
 
2024 | Published | Conference Paper | IST-REx-ID: 18976 | OA
AsGrad: A sharp unified analysis of asynchronous-SGD algorithms
R. Islamov, M. Safaryan, D.-A. Alistarh, in:, Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, ML Research Press, 2024, pp. 649–657.
[Preprint] View | Download Preprint (ext.) | arXiv
 

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