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25 Publications
2024 | Published | Conference Paper | IST-REx-ID: 18973 | 
Bombari, S., & Mondelli, M. (2024). Towards understanding the word sensitivity of attention layers: A study via random features. In 41st International Conference on Machine Learning (Vol. 235, pp. 4300–4328). Vienna, Austria: ML Research Press.
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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. In 41st International Conference on Machine Learning (Vol. 235, pp. 35910–35933). Vienna, Austria: ML Research Press.
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2024 | Published | Conference Paper | IST-REx-ID: 18972 |
Bombari, S., & Mondelli, M. (2024). How spurious features are memorized: Precise analysis for random and NTK features. In 41st International Conference on Machine Learning (Vol. 235, pp. 4267–4299). Vienna, Austria: ML Research Press.
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2024 | Published | Conference Paper | IST-REx-ID: 18971 |
Arefin, R., Zhang, Y., Baratin, A., Locatello, F., Rish, I., Liu, D., & Kawaguchi, K. (2024). Unsupervised concept discovery mitigates spurious correlations. In Proceedings of the 41st International Conference on Machine Learning (Vol. 235, pp. 1672–1688). Vienna, Austria: ML Research Press.
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2024 | Published | Conference Paper | IST-REx-ID: 18976 |
Islamov, R., Safaryan, M., & Alistarh, D.-A. (2024). AsGrad: A sharp unified analysis of asynchronous-SGD algorithms. In Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (Vol. 238, pp. 649–657). Valencia, Spain: ML Research Press.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 17093 |
Zakerinia, H., Talaei, S., Nadiradze, G., & Alistarh, D.-A. (2024). Communication-efficient federated learning with data and client heterogeneity. In Proceedings of the 27th International Conference on Artificial Intelligence and Statistics (Vol. 238, pp. 3448–3456). Valencia, Spain: ML Research Press.
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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. In Proceedings of Machine Learning Research (Vol. 234, pp. 542–553). Hongkong, China: ML Research Press.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18113 |
Egiazarian, V., Panferov, A., Kuznedelev, D., Frantar, E., Babenko, A., & Alistarh, D.-A. (2024). Extreme compression of large language models via additive quantization. In Proceedings of the 41st International Conference on Machine Learning (Vol. 235, pp. 12284–12303). Vienna, Austria: ML Research Press.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18114 |
Pervez, A. A., Locatello, F., & Gavves, E. (2024). Mechanistic neural networks for scientific machine learning. In Proceedings of the 41st International Conference on Machine Learning (Vol. 235, pp. 40484–40501). Vienna, Austria: ML Research Press.
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2024 | Published | Conference Paper | IST-REx-ID: 18115 |
Axiotis, K., Cohen-Addad, V., Henzinger, M., Jerome, S., Mirrokni, V., Saulpic, D., … Wunder, M. (2024). Data-efficient learning via clustering-based sensitivity sampling: Foundation models and beyond. In Proceedings of the 41st International Conference on Machine Learning (Vol. 235, pp. 2086–2107). Vienna, Austria: ML Research Press.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18116 |
La Tour, M. D., Henzinger, M., & Saulpic, D. (2024). Making old things new: A unified algorithm for differentially private clustering. In Proceedings of the 41st International Conference on Machine Learning (Vol. 235, pp. 12046–12086). Vienna, Austria: ML Research Press.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18117 |
Nikdan, M., Tabesh, S., Crncevic, E., & Alistarh, D.-A. (2024). RoSA: Accurate parameter-efficient fine-tuning via robust adaptation. In Proceedings of the 41st International Conference on Machine Learning (Vol. 235, pp. 38187–38206). Vienna, Austria: ML Research Press.
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2024 | Published | Conference Paper | IST-REx-ID: 18118 |
Zakerinia, H., Behjati, A., & Lampert, C. (2024). More flexible PAC-Bayesian meta-learning by learning learning algorithms. In Proceedings of the 41st International Conference on Machine Learning (Vol. 235, pp. 58122–58139). Vienna, Austria: ML Research Press.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18120 |
Scott, J. A., & Cahill, Á. (2024). Improved modelling of federated datasets using mixtures-of-Dirichlet-multinomials. In Proceedings of the 41st International Conference on Machine Learning (Vol. 235, pp. 44012–44037). Vienna, Austria: ML Research Press.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18121 |
Moakhar, A. S., Iofinova, E. B., Frantar, E., & Alistarh, D.-A. (2024). SPADE: Sparsity-guided debugging for deep neural networks. In Proceedings of the 41st International Conference on Machine Learning (Vol. 235, pp. 45955–45987). Vienna, Austria: ML Research Press.
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| arXiv
2023 | Published | Conference Paper | IST-REx-ID: 14458 |
Frantar, E., & Alistarh, D.-A. (2023). SparseGPT: Massive language models can be accurately pruned in one-shot. In Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 10323–10337). Honolulu, Hawaii, HI, United States: ML Research Press.
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| arXiv
2023 | Published | Conference Paper | IST-REx-ID: 14460 |
Nikdan, M., Pegolotti, T., Iofinova, E. B., Kurtic, E., & Alistarh, D.-A. (2023). SparseProp: Efficient sparse backpropagation for faster training of neural networks at the edge. In Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 26215–26227). Honolulu, Hawaii, HI, United States: ML Research Press.
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| arXiv
2023 | Published | Conference Paper | IST-REx-ID: 14461 |
Markov, I., Vladu, A., Guo, Q., & Alistarh, D.-A. (2023). Quantized distributed training of large models with convergence guarantees. In Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 24020–24044). Honolulu, Hawaii, HI, United States: ML Research Press.
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2023 | Published | Conference Paper | IST-REx-ID: 14462 |
Fichtenberger, H., Henzinger, M., & Upadhyay, J. (2023). Constant matters: Fine-grained error bound on differentially private continual observation. In Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 10072–10092). Honolulu, Hawaii, HI, United States: ML Research Press.
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2023 | Published | Conference Paper | IST-REx-ID: 14459 |
Shevchenko, A., Kögler, K., Hassani, H., & Mondelli, M. (2023). Fundamental limits of two-layer autoencoders, and achieving them with gradient methods. In Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 31151–31209). Honolulu, Hawaii, HI, United States: ML Research Press.
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| arXiv