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52 Publications

2024 | Journal Article | IST-REx-ID: 15172
A. R. Esposito and M. Mondelli, “Concentration without independence via information measures,” IEEE Transactions on Information Theory. IEEE.
View | Files available | DOI | arXiv
 
2023 | Conference Paper | IST-REx-ID: 14083 | OA
N. Resch, C. Yuan, and Y. Zhang, “Zero-rate thresholds and new capacity bounds for list-decoding and list-recovery,” in 50th International Colloquium on Automata, Languages, and Programming, Paderborn, Germany, 2023, vol. 261.
[Published Version] View | Files available | DOI | arXiv
 
2023 | Journal Article | IST-REx-ID: 13315 | OA
J. Barbier, F. Camilli, M. Mondelli, and M. Sáenz, “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, vol. 120, no. 30. National Academy of Sciences, 2023.
[Published Version] View | Files available | DOI | PubMed | Europe PMC
 
2023 | Conference Paper | IST-REx-ID: 14459 | OA
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.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2023 | Journal Article | IST-REx-ID: 12838 | OA
Y. Zhang and S. Vatedka, “Multiple packing: Lower bounds via infinite constellations,” IEEE Transactions on Information Theory, vol. 69, no. 7. IEEE, pp. 4513–4527, 2023.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
2023 | Conference Paper | IST-REx-ID: 13321 | OA
Y. Xu, T. Q. Hou, S. S. Liang, and M. Mondelli, “Approximate message passing for multi-layer estimation in rotationally invariant models,” in 2023 IEEE Information Theory Workshop, Saint-Malo, France, 2023, pp. 294–298.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
2023 | Journal Article | IST-REx-ID: 14665 | OA
Y. Zhang and S. Vatedka, “Multiple packing: Lower bounds via error exponents,” IEEE Transactions on Information Theory. IEEE, 2023.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
2023 | Journal Article | IST-REx-ID: 14751 | OA
Y. Zhang, “Zero-error communication over adversarial MACs,” IEEE Transactions on Information Theory, vol. 69, no. 7. Institute of Electrical and Electronics Engineers, pp. 4093–4127, 2023.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
2023 | Journal Article | IST-REx-ID: 13269 | OA
N. Polyanskii and Y. Zhang, “Codes for the Z-channel,” IEEE Transactions on Information Theory, vol. 69, no. 10. Institute of Electrical and Electronics Engineers, pp. 6340–6357, 2023.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
2023 | Conference Paper | IST-REx-ID: 12859 | OA
S. Bombari, S. Kiyani, and M. Mondelli, “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, Honolulu, HI, United States, 2023, vol. 202, pp. 2738–2776.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
2023 | Conference Paper | IST-REx-ID: 14921 | OA
P. Súkeník, M. Mondelli, and C. Lampert, “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.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2023 | Conference Paper | IST-REx-ID: 14924 | OA
D. Wu, V. Kungurtsev, and M. Mondelli, “Mean-field analysis for heavy ball methods: Dropout-stability, connectivity, and global convergence,” in Transactions on Machine Learning Research, 2023.
[Published Version] View | Download Published Version (ext.) | arXiv
 
2023 | Conference Paper | IST-REx-ID: 14923 | OA
T. Fu, Y. Liu, J. Barbier, M. Mondelli, S. Liang, and T. Hou, “Mismatched estimation of non-symmetric rank-one matrices corrupted by structured noise,” in Proceedings of 2023 IEEE International Symposium on Information Theory, Taipei, Taiwan.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
2023 | Conference Paper | IST-REx-ID: 14922 | OA
A. R. Esposito and M. Mondelli, “Concentration without independence via information measures,” in Proceedings of 2023 IEEE International Symposium on Information Theory, Taipei, Taiwan, 2023, pp. 400–405.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
2022 | Journal Article | IST-REx-ID: 11420 | OA
A. Shevchenko, V. Kungurtsev, and M. Mondelli, “Mean-field analysis of piecewise linear solutions for wide ReLU networks,” Journal of Machine Learning Research, vol. 23, no. 130. Journal of Machine Learning Research, pp. 1–55, 2022.
[Published Version] View | Files available | arXiv
 
2022 | Conference Paper | IST-REx-ID: 12011 | OA
Y. Zhang, S. Jaggi, M. Langberg, and A. D. Sarwate, “The capacity of causal adversarial channels,” in 2022 IEEE International Symposium on Information Theory, Espoo, Finland, 2022, vol. 2022, pp. 2523–2528.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
2022 | Conference Paper | IST-REx-ID: 12017
A. K. Yadav, M. Alimohammadi, Y. Zhang, A. J. Budkuley, and S. Jaggi, “New results on AVCs with omniscient and myopic adversaries,” in 2022 IEEE International Symposium on Information Theory, Espoo, Finland, 2022, vol. 2022, pp. 2535–2540.
View | DOI
 
2022 | Conference Paper | IST-REx-ID: 12013
P. Joshi, A. Purkayastha, Y. Zhang, A. J. Budkuley, and S. Jaggi, “On the capacity of additive AVCs with feedback,” in 2022 IEEE International Symposium on Information Theory, Espoo, Finland, 2022, vol. 2022, pp. 504–509.
View | DOI
 
2022 | Conference Paper | IST-REx-ID: 12016 | OA
D. Fathollahi and M. Mondelli, “Polar coded computing: The role of the scaling exponent,” in 2022 IEEE International Symposium on Information Theory, Espoo, Finland, 2022, vol. 2022, pp. 2154–2159.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
2022 | Conference Paper | IST-REx-ID: 12012 | OA
S. Torkamani, J. B. Ebrahimi, P. Sadeghi, R. G. L. D’Oliveira, and M. Médard, “Heterogeneous differential privacy via graphs,” in 2022 IEEE International Symposium on Information Theory, Espoo, Finland, 2022, vol. 2022, pp. 1623–1628.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 

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