Amani MH, Bombari S, Mondelli M, Pukdee R, Rini S. Sharp asymptotics on the compression of two-layer neural networks. IEEE Information Theory Workshop. 2022:588-593. doi:10.1109/ITW54588.2022.9965870

Bombari S, Amani MH, Mondelli M. Memorization and optimization in deep neural networks with minimum over-parameterization. In: 36th Conference on Neural Information Processing Systems. Vol 35. Curran Associates; 2022:7628-7640.

Mondelli M, Venkataramanan R. Approximate message passing with spectral initialization for generalized linear models. Journal of Statistical Mechanics: Theory and Experiment. 2022;2022(11). doi:10.1088/1742-5468/ac9828

Mondelli M, Thrampoulidis C, Venkataramanan R. Optimal combination of linear and spectral estimators for generalized linear models. Foundations of Computational Mathematics. 2022;22(5):1513-1566. doi:10.1007/s10208-021-09531-x

Barbier J, Hou T, Mondelli M, Saenz M. The price of ignorance: How much does it cost to forget noise structure in low-rank matrix estimation? In: 36th Annual Conference on Neural Information Processing Systems. Vol 35. ; 2022.

Nguyen Q, Mondelli M, Montufar GF. Tight bounds on the smallest eigenvalue of the neural tangent kernel for deep ReLU networks. In: Meila M, Zhang T, eds. Proceedings of the 38th International Conference on Machine Learning. Vol 139. ML Research Press; 2021:8119-8129.

Hashemi SA, Mondelli M, Cioffi J, Goldsmith A. Successive syndrome-check decoding of polar codes. In: Proceedings of the 55th Asilomar Conference on Signals, Systems, and Computers. Vol 2021-October. Institute of Electrical and Electronics Engineers; 2021:943-947. doi:10.1109/IEEECONF53345.2021.9723394

Nguyen Q, Mondelli M, Montufar G. 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. Vol 139. ML Research Press; 2021:8119-8129.

Mondelli M, Hashemi SA, Cioffi JM, Goldsmith A. Sublinear latency for simplified successive cancellation decoding of polar codes. IEEE Transactions on Wireless Communications. 2021;20(1):18-27. doi:10.1109/TWC.2020.3022922

Hashemi SA, Mondelli M, Fazeli A, Vardy A, Cioffi J, Goldsmith A. Parallelism versus latency in simplified successive-cancellation decoding of polar codes. In: 2021 IEEE International Symposium on Information Theory. Institute of Electrical and Electronics Engineers; 2021:2369-2374. doi:10.1109/ISIT45174.2021.9518153

Fathollahi D, Farsad N, Hashemi SA, Mondelli M. Sparse multi-decoder recursive projection aggregation for Reed-Muller codes. In: 2021 IEEE International Symposium on Information Theory. Institute of Electrical and Electronics Engineers; 2021:1082-1087. doi:10.1109/isit45174.2021.9517887

Mondelli M, Venkataramanan R. PCA initialization for approximate message passing in rotationally invariant models. In: 35th Conference on Neural Information Processing Systems. Vol 35. Neural Information Processing Systems Foundation; 2021:29616-29629.

Nguyen Q, Bréchet P, Mondelli M. When are solutions connected in deep networks? In: 35th Conference on Neural Information Processing Systems. Vol 35. Neural Information Processing Systems Foundation; 2021.

Mondelli M, Venkataramanan R. Approximate message passing with spectral initialization for generalized linear models. In: Banerjee A, Fukumizu K, eds. Proceedings of The 24th International Conference on Artificial Intelligence and Statistics. Vol 130. ML Research Press; 2021:397-405.

Fazeli A, Hassani H, Mondelli M, Vardy A. Binary linear codes with optimal scaling: Polar codes with large kernels. IEEE Transactions on Information Theory. 2021;67(9):5693-5710. doi:10.1109/TIT.2020.3038806

Li S, Bitar R, Jaggi S, Zhang Y. Network coding with myopic adversaries. IEEE Journal on Selected Areas in Information Theory. 2021;2(4):1108-1119. doi:10.1109/JSAIT.2021.3126474

Nguyen Q, Mondelli M. Global convergence of deep networks with one wide layer followed by pyramidal topology. In: 34th Conference on Neural Information Processing Systems. Vol 33. Curran Associates; 2020:11961–11972.

Mondelli M, Hashemi SA, Cioffi J, Goldsmith A. Simplified successive cancellation decoding of polar codes has sublinear latency. In: IEEE International Symposium on Information Theory - Proceedings. Vol 2020-June. IEEE; 2020. doi:10.1109/ISIT44484.2020.9174141

Shevchenko A, Mondelli M. Landscape connectivity and dropout stability of SGD solutions for over-parameterized neural networks. In: Proceedings of the 37th International Conference on Machine Learning. Vol 119. ML Research Press; 2020:8773-8784.

Javanmard A, Mondelli M, Montanari A. Analysis of a two-layer neural network via displacement convexity. Annals of Statistics. 2020;48(6):3619-3642. doi:10.1214/20-AOS1945