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50 Publications
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 | 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 | 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
2023 | Epub ahead of print | Journal Article | IST-REx-ID: 14665 |
Zhang, Y., & Vatedka, S. (2023). Multiple packing: Lower bounds via error exponents. IEEE Transactions on Information Theory. IEEE. https://doi.org/10.1109/TIT.2023.3334032
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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 | In Press | Conference Paper | IST-REx-ID: 14921 |
Súkeník, P., Mondelli, M., & Lampert, C. (n.d.). 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 | In Press | Conference Paper | IST-REx-ID: 14922 |
Esposito, A. R., & Mondelli, M. (n.d.). Concentration without independence via information measures. In Proceedings of 2023 IEEE International Symposium on Information Theory. Taipei, Taiwan: IEEE. https://doi.org/10.1109/isit54713.2023.10206899
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| arXiv
2023 | In Press | Conference Paper | IST-REx-ID: 14923 |
Fu, T., Liu, Y., Barbier, J., Mondelli, M., Liang, S., & Hou, T. (n.d.). Mismatched estimation of non-symmetric rank-one matrices corrupted by structured noise. In Proceedings of 2023 IEEE International Symposium on Information Theory. Taipei, Taiwan: IEEE. https://doi.org/10.1109/isit54713.2023.10206671
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2023 | Published | Conference Paper | IST-REx-ID: 14924 |
Wu, D., Kungurtsev, V., & Mondelli, M. (2023). Mean-field analysis for heavy ball methods: Dropout-stability, connectivity, and global convergence. In Transactions on Machine Learning Research. ML Research Press.
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| arXiv
2022 | Published | Journal Article | IST-REx-ID: 12273 |
Zhang, Y., Vatedka, S., Jaggi, S., & Sarwate, A. D. (2022). Quadratically constrained myopic adversarial channels. IEEE Transactions on Information Theory. Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/tit.2022.3167554
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| arXiv
2022 | Published | Journal Article | IST-REx-ID: 12480 |
Mondelli, M., & Venkataramanan, R. (2022). Approximate message passing with spectral initialization for generalized linear models. Journal of Statistical Mechanics: Theory and Experiment. IOP Publishing. https://doi.org/10.1088/1742-5468/ac9828
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2022 | Accepted | Preprint | IST-REx-ID: 12536 |
Barbier, J., Hou, T., Mondelli, M., & Saenz, M. (n.d.). The price of ignorance: How much does it cost to forget noise structure in low-rank matrix estimation? arXiv. https://doi.org/10.48550/arXiv.2205.10009
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2022 | Published | Conference Paper | IST-REx-ID: 12537 |
Bombari, S., Amani, M. H., & Mondelli, M. (2022). Memorization and optimization in deep neural networks with minimum over-parameterization. In 36th Conference on Neural Information Processing Systems (Vol. 35, pp. 7628–7640). Curran Associates.
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| arXiv
2022 | Published | Journal Article | IST-REx-ID: 12538 |
Amani, M. H., Bombari, S., Mondelli, M., Pukdee, R., & Rini, S. (2022). Sharp asymptotics on the compression of two-layer neural networks. IEEE Information Theory Workshop. Mumbai, India: IEEE. https://doi.org/10.1109/ITW54588.2022.9965870
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| arXiv
2022 | Published | Conference Paper | IST-REx-ID: 12540 |
Venkataramanan, R., Kögler, K., & Mondelli, M. (2022). Estimation in rotationally invariant generalized linear models via approximate message passing. In Proceedings of the 39th International Conference on Machine Learning (Vol. 162). Baltimore, MD, United States: ML Research Press.
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2022 | Submitted | Preprint | IST-REx-ID: 12860 |
Bombari, S., Achille, A., Wang, Z., Wang, Y.-X., Xie, Y., Singh, K. Y., … Soatto, S. (n.d.). Towards differential relational privacy and its use in question answering. arXiv. https://doi.org/10.48550/arXiv.2203.16701
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