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52 Publications

2022 | Journal Article | IST-REx-ID: 10364 | OA
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 | OA
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 | OA
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 | OA
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 | OA
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 | OA
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 | OA
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 | OA
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 | OA
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 | OA
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 | OA
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 | OA
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 | OA
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 | OA
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 | OA
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 | OA
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 | OA
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 | OA
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 | OA
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
 

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