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Alexander Fedorov

Andrei Panferov

Dan Alistarh

Sergei Pankratov

Vage Egiazarian

Erik Schultheis

Mahdi Nikdan

Soroush Tabesh

Jiale Chen

Alexandra Volkova

Eldar Kurtic

Ionut-Vlad Modoranu

Janne Korhonen

Vitalii Aksenov

Martin Töpfer

Giorgi Nadiradze

Bapi Chatterjee

Alexandra Krumes

Peter Davies

Sidak Singh

Trevor Brown

Elvir Crncevic

Sokolova, Mariia

Elias Frantar

Sharareh Alipour

Joel Rybicki

Ilia Markov

Roberto Lopez Castro

Mher Safaryan



164 Publications

2024 | Research Data Reference | IST-REx-ID: 19884 | OA
MARLIN: Mixed-precision auto-regressive parallel inference on Large Language Models
E. Frantar, R. Castro, J. Chen, T. Hoefler, D.-A. Alistarh, (2024).
[Published Version] View | Files available | DOI | Download Published Version (ext.)
 
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: 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
 
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 | Conference Paper | IST-REx-ID: 19510 | OA
MICROADAM: Accurate adaptive optimization with low space overhead and provable convergence
I.-V. Modoranu, M. Safaryan, G. Malinovsky, E. Kurtic, T. Robert, P. Richtárik, D.-A. Alistarh, in:, 38th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2024.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
2024 | Published | Conference Paper | IST-REx-ID: 19511 | OA
QuaRot: Outlier-free 4-bit inference in rotated LLMs
S. Ashkboos, A. Mohtashami, M.L. Croci, B. Li, P. Cameron, M. Jaggi, D.-A. Alistarh, T. Hoefler, J. Hensman, in:, 38th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2024.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
2024 | Published | Conference Paper | IST-REx-ID: 19518 | OA
The iterative optimal brain surgeon: Faster sparse recovery by leveraging second-order information
D. Wu, I.-V. Modoranu, M. Safaryan, D. Kuznedelev, D.-A. Alistarh, in:, 38th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2024.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2024 | Published | Conference Paper | IST-REx-ID: 19519 | OA
PV-tuning: Beyond straight-through estimation for extreme LLM compression
V. Malinovskii, D. Mazur, I. Ilin, D. Kuznedelev, K. Burlachenko, K. Yi, D.-A. Alistarh, P. Richtarik, in:, 38th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2024.
[Published Version] View | Files available | 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: 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: 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: 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.) | WoS | arXiv
 
2024 | Published | Conference Paper | IST-REx-ID: 17456 | OA
L-GreCo: Layerwise-adaptive gradient compression for efficient data-parallel deep learning
I. Markov, K. Alimohammadi, E. Frantar, D.-A. Alistarh, in:, P. Gibbons, G. Pekhimenko, C. De Sa (Eds.), Proceedings of Machine Learning and Systems , Association for Computing Machinery, 2024.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
2024 | Published | Thesis | PhD | 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 | Thesis | PhD | IST-REx-ID: 17490 | OA
Communication-efficient distributed training of deep neural networks : An algorithms and systems perspective
I. Markov, Communication-Efficient Distributed Training of Deep Neural Networks : An Algorithms and Systems Perspective, Institute of Science and Technology Austria, 2024.
[Published Version] View | Files available | DOI
 
2024 | Published | Thesis | PhD | IST-REx-ID: 17465 | OA
High-dimensional limits in artificial neural networks
A. Shevchenko, High-Dimensional Limits in Artificial Neural Networks, Institute of Science and Technology Austria, 2024.
[Published Version] View | Files available | DOI
 
2024 | Published | Conference Paper | IST-REx-ID: 17469 | OA
Compression of structured data with autoencoders: Provable benefit of nonlinearities and depth
K. Kögler, A. Shevchenko, H. Hassani, M. Mondelli, in:, Proceedings of the 41st International Conference on Machine Learning, ML Research Press, 2024, pp. 24964–25015.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
2023 | Published | Conference Paper | IST-REx-ID: 14260 | OA
Lincheck: A practical framework for testing concurrent data structures on JVM
N. Koval, A. Fedorov, M. Sokolova, D. Tsitelov, D.-A. Alistarh, in:, 35th International Conference on Computer Aided Verification , Springer Nature, 2023, pp. 156–169.
[Published Version] View | Files available | DOI | WoS
 
2023 | Published | Journal Article | IST-REx-ID: 14364 | OA
Why extension-based proofs fail
D.-A. Alistarh, J. Aspnes, F. Ellen, R. Gelashvili, L. Zhu, SIAM Journal on Computing 52 (2023) 913–944.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
2023 | Published | Conference Paper | IST-REx-ID: 14458 | OA
SparseGPT: Massive language models can be accurately pruned in one-shot
E. Frantar, D.-A. Alistarh, in:, Proceedings of the 40th International Conference on Machine Learning, ML Research Press, 2023, pp. 10323–10337.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 

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