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82 Publications
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2024 |
Published |
Conference Paper |
IST-REx-ID: 18891 |
Súkeník, P., Lampert, C., & Mondelli, M. (2024). Neural collapse versus low-rank bias: Is deep neural collapse really optimal? In 38th Annual Conference on Neural Information Processing Systems (Vol. 37). Vancouver, Canada: Neural Information Processing Systems Foundation.
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| arXiv
2024 |
Published |
Conference Paper |
IST-REx-ID: 18897 |
Pedrotti, F., Maas, J., & Mondelli, M. (2024). Improved convergence of score-based diffusion models via prediction-correction. In Transactions on Machine Learning Research.
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| arXiv
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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| arXiv
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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| arXiv
2024 |
Published |
Conference Paper |
IST-REx-ID: 19518 |
Wu, D., Modoranu, I.-V., Safaryan, M., Kuznedelev, D., & Alistarh, D.-A. (2024). The iterative optimal brain surgeon: Faster sparse recovery by leveraging second-order information. In 38th Conference on Neural Information Processing Systems (Vol. 37). Vancouver, Canada: Neural Information Processing Systems Foundation.
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| arXiv
2024 |
Published |
Journal Article |
IST-REx-ID: 15172 |
Esposito, A. R., & Mondelli, M. (2024). Concentration without independence via information measures. IEEE Transactions on Information Theory. IEEE. https://doi.org/10.1109/TIT.2024.3367767
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| WoS
| arXiv
2024 |
Published |
Conference Paper |
IST-REx-ID: 17147 |
Depope, A., Mondelli, M., & Robinson, M. R. (2024). Inference of genetic effects via approximate message passing. In 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing (pp. 13151–13155). Seoul, Korea: IEEE. https://doi.org/10.1109/ICASSP48485.2024.10447198
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| WoS
2024 |
Published |
Journal Article |
IST-REx-ID: 17330 |
Resch, N., Yuan, C., & Zhang, Y. (2024). Zero-rate thresholds and new capacity bounds for list-decoding and list-recovery. IEEE Transactions on Information Theory. IEEE. https://doi.org/10.1109/TIT.2024.3430842
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| arXiv
2024 |
Draft |
Preprint |
IST-REx-ID: 17350 |
Pedrotti, F., Maas, J., & Mondelli, M. (n.d.). Improved convergence of score-based diffusion models via prediction-correction. arXiv. https://doi.org/10.48550/arXiv.2305.14164
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| arXiv
2024 |
Published |
Thesis | PhD |
IST-REx-ID: 17465 |
Shevchenko, A. (2024). High-dimensional limits in artificial neural networks. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:17465
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2024 |
Published |
Conference Paper |
IST-REx-ID: 17469 |
Kögler, K., Shevchenko, A., Hassani, H., & Mondelli, M. (2024). Compression of structured data with autoencoders: Provable benefit of nonlinearities and depth. In Proceedings of the 41st International Conference on Machine Learning (Vol. 235, pp. 24964–25015). Vienna, Austria: ML Research Press.
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| arXiv
2023 |
Published |
Conference Paper |
IST-REx-ID: 14083 |
Resch, N., Yuan, C., & Zhang, Y. (2023). Zero-rate thresholds and new capacity bounds for list-decoding and list-recovery. In 50th International Colloquium on Automata, Languages, and Programming (Vol. 261). Paderborn, Germany: Schloss Dagstuhl - Leibniz-Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.ICALP.2023.99
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| arXiv
2023 |
Published |
Journal Article |
IST-REx-ID: 12838 |
Zhang, Y., & Vatedka, S. (2023). Multiple packing: Lower bounds via infinite constellations. IEEE Transactions on Information Theory. IEEE. https://doi.org/10.1109/TIT.2023.3260950
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| arXiv
2023 |
Published |
Conference Paper |
IST-REx-ID: 12859 |
Bombari, S., Kiyani, S., & Mondelli, M. (2023). Beyond the universal law of robustness: Sharper laws for random features and neural tangent kernels. In Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 2738–2776). Honolulu, HI, United States: ML Research Press.
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| arXiv
2023 |
Published |
Journal Article |
IST-REx-ID: 13269 |
Polyanskii, N., & Zhang, Y. (2023). Codes for the Z-channel. IEEE Transactions on Information Theory. Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/TIT.2023.3292219
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| arXiv
2023 |
Published |
Journal Article |
IST-REx-ID: 13315 |
Barbier, J., Camilli, F., Mondelli, M., & Sáenz, M. (2023). Fundamental limits in structured principal component analysis and how to reach them. Proceedings of the National Academy of Sciences of the United States of America. National Academy of Sciences. https://doi.org/10.1073/pnas.2302028120
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| PubMed | Europe PMC
2023 |
Published |
Conference Paper |
IST-REx-ID: 13321 |
Xu, Y., Hou, T. Q., Liang, S. S., & Mondelli, M. (2023). Approximate message passing for multi-layer estimation in rotationally invariant models. In 2023 IEEE Information Theory Workshop (pp. 294–298). Saint-Malo, France: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ITW55543.2023.10160238
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| arXiv
2023 |
Published |
Journal Article |
IST-REx-ID: 14751 |
Zhang, Y. (2023). Zero-error communication over adversarial MACs. IEEE Transactions on Information Theory. Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/tit.2023.3257239
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| arXiv
2023 |
Published |
Conference Paper |
IST-REx-ID: 14921 |
Súkeník, P., Mondelli, M., & Lampert, C. (2023). Deep neural collapse is provably optimal for the deep unconstrained features model. In 37th Annual Conference on Neural Information Processing Systems. New Orleans, LA, United States.
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| arXiv
2023 |
Published |
Conference Paper |
IST-REx-ID: 14922 |
Esposito, A. R., & Mondelli, M. (2023). Concentration without independence via information measures. In Proceedings of 2023 IEEE International Symposium on Information Theory (pp. 400–405). Taipei, Taiwan: IEEE. https://doi.org/10.1109/isit54713.2023.10206899
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| arXiv
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