Please note that LibreCat no longer supports Internet Explorer versions 8 or 9 (or earlier).
We recommend upgrading to the latest Internet Explorer, Google Chrome, or Firefox.
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 | 
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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: 12016 |
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 |
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
2022 | Conference Paper | IST-REx-ID: 12540 |
R. Venkataramanan, K. Kögler, and M. Mondelli, “Estimation in rotationally invariant generalized linear models via approximate message passing,” in Proceedings of the 39th International Conference on Machine Learning, Baltimore, MD, United States, 2022, vol. 162.
[Published Version]
View
| Files available
2022 | Preprint | IST-REx-ID: 12536 |
J. Barbier, T. Hou, M. Mondelli, and M. Saenz, “The price of ignorance: How much does it cost to forget noise structure in low-rank matrix estimation?,” arXiv. .
[Preprint]
View
| DOI
| Download Preprint (ext.)
| arXiv
2022 | Preprint | IST-REx-ID: 12860 |
S. Bombari et al., “Towards differential relational privacy and its use in question answering,” arXiv. .
[Preprint]
View
| DOI
| Download Preprint (ext.)
| arXiv
2022 | Journal Article | IST-REx-ID: 11639 |
Y. Zhang and S. Vatedka, “List decoding random Euclidean codes and Infinite constellations,” IEEE Transactions on Information Theory, vol. 68, no. 12. IEEE, pp. 7753–7786, 2022.
[Preprint]
View
| DOI
| Download Preprint (ext.)
| WoS
| arXiv
2022 | Journal Article | IST-REx-ID: 12233 |
N. Doan, S. A. Hashemi, M. Mondelli, and W. J. Gross, “Decoding Reed-Muller codes with successive codeword permutations,” IEEE Transactions on Communications, vol. 70, no. 11. Institute of Electrical and Electronics Engineers, pp. 7134–7145, 2022.
[Preprint]
View
| DOI
| Download Preprint (ext.)
| WoS
| arXiv
2022 | Journal Article | IST-REx-ID: 12273 |
Y. Zhang, S. Vatedka, S. Jaggi, and A. D. Sarwate, “Quadratically constrained myopic adversarial channels,” IEEE Transactions on Information Theory, vol. 68, no. 8. Institute of Electrical and Electronics Engineers, pp. 4901–4948, 2022.
[Preprint]
View
| DOI
| Download Preprint (ext.)
| WoS
| arXiv
2022 | Journal Article | IST-REx-ID: 10364 |
S. A. Hashemi, M. Mondelli, A. Fazeli, A. Vardy, J. Cioffi, and A. Goldsmith, “Parallelism versus latency in simplified successive-cancellation decoding of polar codes,” IEEE Transactions on Wireless Communications, vol. 21, no. 6. Institute of Electrical and Electronics Engineers, pp. 3909–3920, 2022.
[Preprint]
View
| Files available
| DOI
| Download Preprint (ext.)
| WoS
| arXiv
2022 | Journal Article | IST-REx-ID: 12538 |
M. H. Amani, S. Bombari, M. Mondelli, R. Pukdee, and S. Rini, “Sharp asymptotics on the compression of two-layer neural networks,” IEEE Information Theory Workshop. IEEE, pp. 588–593, 2022.
[Preprint]
View
| DOI
| Download Preprint (ext.)
| arXiv
2022 | Conference Paper | IST-REx-ID: 12537 |
S. Bombari, M. H. Amani, and M. Mondelli, “Memorization and optimization in deep neural networks with minimum over-parameterization,” in 36th Conference on Neural Information Processing Systems, 2022, vol. 35, pp. 7628–7640.
[Preprint]
View
| Download Preprint (ext.)
| arXiv
2022 | Journal Article | IST-REx-ID: 12480 |
M. Mondelli and R. Venkataramanan, “Approximate message passing with spectral initialization for generalized linear models,” Journal of Statistical Mechanics: Theory and Experiment, vol. 2022, no. 11. IOP Publishing, 2022.
[Published Version]
View
| Files available
| DOI
| WoS
2021 | Conference Paper | IST-REx-ID: 10595 |
Q. Nguyen, M. Mondelli, and G. F. 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.
[Published Version]
View
| Download Published Version (ext.)
| arXiv
2021 | Conference Paper | IST-REx-ID: 10599 |
S. A. Hashemi, M. Mondelli, J. Cioffi, and A. Goldsmith, “Successive syndrome-check decoding of polar codes,” in Proceedings of the 55th Asilomar Conference on Signals, Systems, and Computers, Virtual, Pacific Grove, CA, United States, 2021, vol. 2021–October, pp. 943–947.
[Preprint]
View
| DOI
| Download Preprint (ext.)
| arXiv
2021 | 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.
[Published Version]
View
| Files available
| arXiv
2021 | Journal Article | IST-REx-ID: 9047 |
M. Mondelli, S. A. Hashemi, J. M. Cioffi, and A. Goldsmith, “Sublinear latency for simplified successive cancellation decoding of polar codes,” IEEE Transactions on Wireless Communications, vol. 20, no. 1. IEEE, pp. 18–27, 2021.
[Preprint]
View
| Files available
| DOI
| Download Preprint (ext.)
| WoS
| arXiv
2021 | Conference Paper | IST-REx-ID: 10053 |
S. A. Hashemi, M. Mondelli, A. Fazeli, A. Vardy, J. Cioffi, and A. Goldsmith, “Parallelism versus latency in simplified successive-cancellation decoding of polar codes,” in 2021 IEEE International Symposium on Information Theory, Melbourne, Australia, 2021, pp. 2369–2374.
[Preprint]
View
| Files available
| DOI
| Download Preprint (ext.)
| WoS
| arXiv
2021 | Conference Paper | IST-REx-ID: 10597 |
D. Fathollahi, N. Farsad, S. A. Hashemi, and M. Mondelli, “Sparse multi-decoder recursive projection aggregation for Reed-Muller codes,” in 2021 IEEE International Symposium on Information Theory, Virtual, Melbourne, Australia, 2021, pp. 1082–1087.
[Preprint]
View
| DOI
| Download Preprint (ext.)
| WoS
| arXiv
2021 | Journal Article | IST-REx-ID: 10211 |
M. Mondelli, C. Thrampoulidis, and R. Venkataramanan, “Optimal combination of linear and spectral estimators for generalized linear models,” Foundations of Computational Mathematics. Springer, 2021.
[Published Version]
View
| Files available
| DOI
| WoS
| arXiv
2021 | Conference Paper | IST-REx-ID: 10593 |
M. Mondelli and R. Venkataramanan, “PCA initialization for approximate message passing in rotationally invariant models,” in 35th Conference on Neural Information Processing Systems, Virtual, 2021, vol. 35, pp. 29616–29629.
[Preprint]
View
| Download Preprint (ext.)
| arXiv
2021 | Conference Paper | IST-REx-ID: 10594 |
Q. Nguyen, P. Bréchet, and M. Mondelli, “When are solutions connected in deep networks?,” in 35th Conference on Neural Information Processing Systems, Virtual, 2021, vol. 35.
[Preprint]
View
| Download Preprint (ext.)
| arXiv
2021 | Conference Paper | IST-REx-ID: 10598 |
M. Mondelli and R. Venkataramanan, “Approximate message passing with spectral initialization for generalized linear models,” in Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, Virtual, San Diego, CA, United States, 2021, vol. 130, pp. 397–405.
[Preprint]
View
| Files available
| Download Preprint (ext.)
| arXiv
2021 | Journal Article | IST-REx-ID: 9002
A. Fazeli, H. Hassani, M. Mondelli, and A. Vardy, “Binary linear codes with optimal scaling: Polar codes with large kernels,” IEEE Transactions on Information Theory, vol. 67, no. 9. IEEE, pp. 5693–5710, 2021.
[Preprint]
View
| Files available
| DOI
| arXiv
2021 | Journal Article | IST-REx-ID: 15254 |
S. Li, R. Bitar, S. Jaggi, and Y. Zhang, “Network coding with myopic adversaries,” IEEE Journal on Selected Areas in Information Theory, vol. 2, no. 4. IEEE, pp. 1108–1119, 2021.
[Preprint]
View
| DOI
| Download Preprint (ext.)
| arXiv
2020 | Conference Paper | IST-REx-ID: 9221 |
Q. Nguyen and M. Mondelli, “Global convergence of deep networks with one wide layer followed by pyramidal topology,” in 34th Conference on Neural Information Processing Systems, Vancouver, Canada, 2020, vol. 33, pp. 11961–11972.
[Preprint]
View
| Download Preprint (ext.)
| arXiv
2020 | Conference Paper | IST-REx-ID: 8536 |
M. Mondelli, S. A. Hashemi, J. Cioffi, and A. Goldsmith, “Simplified successive cancellation decoding of polar codes has sublinear latency,” in IEEE International Symposium on Information Theory - Proceedings, Los Angeles, CA, United States, 2020, vol. 2020–June.
[Preprint]
View
| Files available
| DOI
| Download Preprint (ext.)
| arXiv
2020 | Conference Paper | IST-REx-ID: 9198 |
A. Shevchenko and M. Mondelli, “Landscape connectivity and dropout stability of SGD solutions for over-parameterized neural networks,” in Proceedings of the 37th International Conference on Machine Learning, 2020, vol. 119, pp. 8773–8784.
[Published Version]
View
| Files available
| arXiv
2020 | Journal Article | IST-REx-ID: 6748 |
A. Javanmard, M. Mondelli, and A. Montanari, “Analysis of a two-layer neural network via displacement convexity,” Annals of Statistics, vol. 48, no. 6. Institute of Mathematical Statistics, pp. 3619–3642, 2020.
[Preprint]
View
| DOI
| Download Preprint (ext.)
| WoS
| arXiv