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34 Publications
2024 | Published | Conference Paper | IST-REx-ID: 18972 | 
S. Bombari and M. Mondelli, “How spurious features are memorized: Precise analysis for random and NTK features,” in 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 4267–4299.
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2024 | Published | Conference Paper | IST-REx-ID: 18115 |
K. Axiotis et al., “Data-efficient learning via clustering-based sensitivity sampling: Foundation models and beyond,” in Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 2086–2107.
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2024 | Published | Conference Paper | IST-REx-ID: 18116 |
M. D. La Tour, M. Henzinger, and D. Saulpic, “Making old things new: A unified algorithm for differentially private clustering,” in Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 12046–12086.
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2024 | Published | Conference Paper | IST-REx-ID: 18121 |
A. S. Moakhar, E. B. Iofinova, E. Frantar, and D.-A. Alistarh, “SPADE: Sparsity-guided debugging for deep neural networks,” in Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 45955–45987.
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2023 | Published | Conference Paper | IST-REx-ID: 14460 |
M. Nikdan, T. Pegolotti, E. B. Iofinova, E. Kurtic, and D.-A. Alistarh, “SparseProp: Efficient sparse backpropagation for faster training of neural networks at the edge,” in Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, HI, United States, 2023, vol. 202, pp. 26215–26227.
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2023 | Published | Conference Paper | IST-REx-ID: 14458 |
E. Frantar and D.-A. Alistarh, “SparseGPT: Massive language models can be accurately pruned in one-shot,” in Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, HI, United States, 2023, vol. 202, pp. 10323–10337.
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2023 | Published | Conference Paper | IST-REx-ID: 14461 |
I. Markov, A. Vladu, Q. Guo, and D.-A. Alistarh, “Quantized distributed training of large models with convergence guarantees,” in Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, HI, United States, 2023, vol. 202, pp. 24020–24044.
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2023 | Published | Conference Paper | IST-REx-ID: 14462 |
H. Fichtenberger, M. Henzinger, and J. Upadhyay, “Constant matters: Fine-grained error bound on differentially private continual observation,” in Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, HI, United States, 2023, vol. 202, pp. 10072–10092.
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2023 | Published | Conference Paper | IST-REx-ID: 14459 |
A. Shevchenko, K. Kögler, H. Hassani, and M. Mondelli, “Fundamental limits of two-layer autoencoders, and achieving them with gradient methods,” in Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, HI, United States, 2023, vol. 202, pp. 31151–31209.
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2022 | Published | Conference Paper | IST-REx-ID: 13241 |
N. H. Konstantinov and C. Lampert, “On the impossibility of fairness-aware learning from corrupted data,” in Proceedings of Machine Learning Research, 2022, vol. 171, pp. 59–83.
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2022 | Published | Conference Paper | IST-REx-ID: 13239 |
T. L. Van Der Plas, T. P. Vogels, and S. G. Manohar, “Predictive learning enables neural networks to learn complex working memory tasks,” in Proceedings of Machine Learning Research, 2022, vol. 199, pp. 518–531.
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2021 | Published | Conference Paper | IST-REx-ID: 13147 |
F. Alimisis, P. Davies, and D.-A. Alistarh, “Communication-efficient distributed optimization with quantized preconditioners,” in Proceedings of the 38th International Conference on Machine Learning, Virtual, 2021, vol. 139, pp. 196–206.
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2021 | Published | Conference Paper | IST-REx-ID: 13146 |
Q. Nguyen, M. Mondelli, and G. Montufar, “Tight bounds on the smallest Eigenvalue of the neural tangent kernel for deep ReLU networks,” in Proceedings of the 38th International Conference on Machine Learning, Virtual, 2021, vol. 139, pp. 8119–8129.
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2017 | Published | Conference Paper | IST-REx-ID: 11651 |
D. Wang, K. Fountoulakis, M. Henzinger, M. W. Mahoney, and Satish Rao , “Capacity releasing diffusion for speed and locality,” in Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia, 2017, vol. 70, pp. 3598–3607.
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| arXiv