Eldar Kurtic
10 Publications
2025 |
Published |
Conference Paper |
IST-REx-ID: 20684 |
Kurtic E, Marques A, Pandit S, Kurtz M, Alistarh D-A. 2025. “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. ACL: Meeting of the Association for Computational Linguistics, 26872–26886.
[Published Version]
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| arXiv
2025 |
Published |
Conference Paper |
IST-REx-ID: 20820 |
Sieberling O, Kuznedelev D, Kurtic E, Alistarh D-A. 2025. EvoPress: Accurate dynamic model compression via evolutionary search. 42nd International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 267, 55556–55590.
[Published Version]
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| arXiv
2025 |
Published |
Book Chapter |
IST-REx-ID: 21257 |
Kurtic E, Kuznedelev D, Frantar E, Goinv M, Pandit S, Agarwalla A, Nguyen T, Marques A, Kurtz M, Alistarh D-A. 2025.Sparse Fine-Tuning for Inference Acceleration of Large Language Models. In: Enhancing LLM Performance. Efficacy, Fine-Tuning, and Inference Techniques. Machine Translation: Technologies and Applications, , 83–97.
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| arXiv
2024 |
Published |
Conference Paper |
IST-REx-ID: 18975 |
Modoranu I-V, Kalinov A, Kurtic E, Frantar E, Alistarh D-A. 2024. Error feedback can accurately compress preconditioners. 41st International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 235, 35910–35933.
[Preprint]
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| arXiv
2024 |
Published |
Conference Paper |
IST-REx-ID: 19510 |
Modoranu I-V, Safaryan M, Malinovsky G, Kurtic E, Robert T, Richtárik P, Alistarh D-A. 2024. MICROADAM: Accurate adaptive optimization with low space overhead and provable convergence. 38th Conference on Neural Information Processing Systems. , Advances in Neural Information Processing Systems, vol. 37.
[Preprint]
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| arXiv
2024 |
Published |
Conference Paper |
IST-REx-ID: 15011 |
Kurtic E, Hoefler T, Alistarh D-A. 2024. How to prune your language model: Recovering accuracy on the ‘Sparsity May Cry’ benchmark. Proceedings of Machine Learning Research. CPAL: Conference on Parsimony and Learning, PMLR, vol. 234, 542–553.
[Preprint]
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| arXiv
2023 |
Published |
Conference Paper |
IST-REx-ID: 14460 |
Nikdan M, Pegolotti T, Iofinova EB, Kurtic E, Alistarh D-A. 2023. SparseProp: Efficient sparse backpropagation for faster training of neural networks at the edge. Proceedings of the 40th International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 202, 26215–26227.
[Preprint]
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| arXiv
2023 |
Published |
Conference Paper |
IST-REx-ID: 13053 |
Krumes A, Vladu A, Kurtic E, Lampert C, Alistarh D-A. 2023. CrAM: A Compression-Aware Minimizer. 11th International Conference on Learning Representations . ICLR: International Conference on Learning Representations.
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| arXiv
2022 |
Published |
Conference Paper |
IST-REx-ID: 17088 |
Kurtic E, Campos D, Nguyen T, Frantar E, Kurtz M, Fineran B, Goin M, Alistarh D-A. 2022. The optimal BERT surgeon: Scalable and accurate second-order pruning for large language models. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. EMNLP: Conference on Empirical Methods in Natural Language Processing, 4163–4181.
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| DOI
| arXiv
2021 |
Published |
Conference Paper |
IST-REx-ID: 11463 |
Frantar E, Kurtic E, Alistarh D-A. 2021. M-FAC: Efficient matrix-free approximations of second-order information. 35th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 34, 14873–14886.
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10 Publications
2025 |
Published |
Conference Paper |
IST-REx-ID: 20684 |
Kurtic E, Marques A, Pandit S, Kurtz M, Alistarh D-A. 2025. “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. ACL: Meeting of the Association for Computational Linguistics, 26872–26886.
[Published Version]
View
| Files available
| arXiv
2025 |
Published |
Conference Paper |
IST-REx-ID: 20820 |
Sieberling O, Kuznedelev D, Kurtic E, Alistarh D-A. 2025. EvoPress: Accurate dynamic model compression via evolutionary search. 42nd International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 267, 55556–55590.
[Published Version]
View
| Files available
| arXiv
2025 |
Published |
Book Chapter |
IST-REx-ID: 21257 |
Kurtic E, Kuznedelev D, Frantar E, Goinv M, Pandit S, Agarwalla A, Nguyen T, Marques A, Kurtz M, Alistarh D-A. 2025.Sparse Fine-Tuning for Inference Acceleration of Large Language Models. In: Enhancing LLM Performance. Efficacy, Fine-Tuning, and Inference Techniques. Machine Translation: Technologies and Applications, , 83–97.
[Preprint]
View
| DOI
| Download Preprint (ext.)
| arXiv
2024 |
Published |
Conference Paper |
IST-REx-ID: 18975 |
Modoranu I-V, Kalinov A, Kurtic E, Frantar E, Alistarh D-A. 2024. Error feedback can accurately compress preconditioners. 41st International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 235, 35910–35933.
[Preprint]
View
| Download Preprint (ext.)
| arXiv
2024 |
Published |
Conference Paper |
IST-REx-ID: 19510 |
Modoranu I-V, Safaryan M, Malinovsky G, Kurtic E, Robert T, Richtárik P, Alistarh D-A. 2024. MICROADAM: Accurate adaptive optimization with low space overhead and provable convergence. 38th Conference on Neural Information Processing Systems. , Advances in Neural Information Processing Systems, vol. 37.
[Preprint]
View
| Files available
| Download Preprint (ext.)
| arXiv
2024 |
Published |
Conference Paper |
IST-REx-ID: 15011 |
Kurtic E, Hoefler T, Alistarh D-A. 2024. How to prune your language model: Recovering accuracy on the ‘Sparsity May Cry’ benchmark. Proceedings of Machine Learning Research. CPAL: Conference on Parsimony and Learning, PMLR, vol. 234, 542–553.
[Preprint]
View
| Download Preprint (ext.)
| arXiv
2023 |
Published |
Conference Paper |
IST-REx-ID: 14460 |
Nikdan M, Pegolotti T, Iofinova EB, Kurtic E, Alistarh D-A. 2023. SparseProp: Efficient sparse backpropagation for faster training of neural networks at the edge. Proceedings of the 40th International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 202, 26215–26227.
[Preprint]
View
| Download Preprint (ext.)
| arXiv
2023 |
Published |
Conference Paper |
IST-REx-ID: 13053 |
Krumes A, Vladu A, Kurtic E, Lampert C, Alistarh D-A. 2023. CrAM: A Compression-Aware Minimizer. 11th International Conference on Learning Representations . ICLR: International Conference on Learning Representations.
[Published Version]
View
| Files available
| Download Published Version (ext.)
| arXiv
2022 |
Published |
Conference Paper |
IST-REx-ID: 17088 |
Kurtic E, Campos D, Nguyen T, Frantar E, Kurtz M, Fineran B, Goin M, Alistarh D-A. 2022. The optimal BERT surgeon: Scalable and accurate second-order pruning for large language models. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. EMNLP: Conference on Empirical Methods in Natural Language Processing, 4163–4181.
[Published Version]
View
| Files available
| DOI
| arXiv
2021 |
Published |
Conference Paper |
IST-REx-ID: 11463 |
Frantar E, Kurtic E, Alistarh D-A. 2021. M-FAC: Efficient matrix-free approximations of second-order information. 35th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 34, 14873–14886.
[Published Version]
View
| Download Published Version (ext.)
| arXiv