Amani, Mohammad Hossein, Simone Bombari, Marco Mondelli, Rattana Pukdee, and Stefano Rini. “Sharp Asymptotics on the Compression of Two-Layer Neural Networks.”https://doi.org/10.1109/ITW54588.2022.9965870__IEEE Information Theory Workshop. IEEE, 2022.https://doi.org/10.1109/ITW54588.2022.9965870 .

Bombari, Simone, Mohammad Hossein Amani, and Marco Mondelli. “Memorization and Optimization in Deep Neural Networks with Minimum Over-Parameterization.” In 36th Conference on Neural Information Processing Systems, 35:7628–40. Curran Associates, 2022.

Mondelli, Marco, and Ramji Venkataramanan. “Approximate Message Passing with Spectral Initialization for Generalized Linear Models.”https://doi.org/10.1088/1742-5468/ac9828__Journal of Statistical Mechanics: Theory and Experiment. IOP Publishing, 2022.https://doi.org/10.1088/1742-5468/ac9828 .

Mondelli, Marco, Christos Thrampoulidis, and Ramji Venkataramanan. “Optimal Combination of Linear and Spectral Estimators for Generalized Linear Models.”https://doi.org/10.1007/s10208-021-09531-x__Foundations of Computational Mathematics. Springer, 2022.https://doi.org/10.1007/s10208-021-09531-x .

Barbier, Jean, TianQi Hou, Marco Mondelli, and Manuel Saenz. “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, Quynh, Marco Mondelli, and Guido 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, edited by Marina Meila and Tong Zhang, 139:8119–29. ML Research Press, 2021.

Hashemi, Seyyed Ali, Marco Mondelli, John Cioffi, and Andrea Goldsmith. “Successive Syndrome-Check Decoding of Polar Codes.”https://doi.org/10.1109/IEEECONF53345.2021.9723394__In Proceedings of the 55th Asilomar Conference on Signals, Systems, and Computers, 2021–October:943–47. Institute of Electrical and Electronics Engineers, 2021.https://doi.org/10.1109/IEEECONF53345.2021.9723394 .

Nguyen, Quynh, Marco Mondelli, and Guido 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, 139:8119–29. ML Research Press, 2021.

Mondelli, Marco, Seyyed Ali Hashemi, John M. Cioffi, and Andrea Goldsmith. “Sublinear Latency for Simplified Successive Cancellation Decoding of Polar Codes.”https://doi.org/10.1109/TWC.2020.3022922__IEEE Transactions on Wireless Communications. IEEE, 2021.https://doi.org/10.1109/TWC.2020.3022922 .

Hashemi, Seyyed Ali, Marco Mondelli, Arman Fazeli, Alexander Vardy, John Cioffi, and Andrea Goldsmith. “Parallelism versus Latency in Simplified Successive-Cancellation Decoding of Polar Codes.”https://doi.org/10.1109/ISIT45174.2021.9518153__In 2021 IEEE International Symposium on Information Theory, 2369–74. Institute of Electrical and Electronics Engineers, 2021.https://doi.org/10.1109/ISIT45174.2021.9518153 .

Fathollahi, Dorsa, Nariman Farsad, Seyyed Ali Hashemi, and Marco Mondelli. “Sparse Multi-Decoder Recursive Projection Aggregation for Reed-Muller Codes.”https://doi.org/10.1109/isit45174.2021.9517887__In 2021 IEEE International Symposium on Information Theory, 1082–87. Institute of Electrical and Electronics Engineers, 2021.https://doi.org/10.1109/isit45174.2021.9517887 .

Mondelli, Marco, and Ramji Venkataramanan. “PCA Initialization for Approximate Message Passing in Rotationally Invariant Models.” In 35th Conference on Neural Information Processing Systems, 35:29616–29. Neural Information Processing Systems Foundation, 2021.

Nguyen, Quynh, Pierre Bréchet, and Marco Mondelli. “When Are Solutions Connected in Deep Networks?” In 35th Conference on Neural Information Processing Systems, Vol. 35. Neural Information Processing Systems Foundation, 2021.

Mondelli, Marco, and Ramji Venkataramanan. “Approximate Message Passing with Spectral Initialization for Generalized Linear Models.” In Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, edited by Arindam Banerjee and Kenji Fukumizu, 130:397–405. ML Research Press, 2021.

Fazeli, Arman, Hamed Hassani, Marco Mondelli, and Alexander Vardy. “Binary Linear Codes with Optimal Scaling: Polar Codes with Large Kernels.”https://doi.org/10.1109/TIT.2020.3038806__IEEE Transactions on Information Theory. IEEE, 2021.https://doi.org/10.1109/TIT.2020.3038806 .

Li, Sijie, Rawad Bitar, Sidharth Jaggi, and Yihan Zhang. “Network Coding with Myopic Adversaries.”https://doi.org/10.1109/JSAIT.2021.3126474__IEEE Journal on Selected Areas in Information Theory. IEEE, 2021.https://doi.org/10.1109/JSAIT.2021.3126474 .

Nguyen, Quynh, and Marco Mondelli. “Global Convergence of Deep Networks with One Wide Layer Followed by Pyramidal Topology.” In 34th Conference on Neural Information Processing Systems, 33:11961–11972. Curran Associates, 2020.

Mondelli, Marco, Seyyed Ali Hashemi, John Cioffi, and Andrea Goldsmith. “Simplified Successive Cancellation Decoding of Polar Codes Has Sublinear Latency.”https://doi.org/10.1109/ISIT44484.2020.9174141__In IEEE International Symposium on Information Theory - Proceedings, Vol. 2020–June. IEEE, 2020.https://doi.org/10.1109/ISIT44484.2020.9174141 .

Shevchenko, Alexander, and Marco Mondelli. “Landscape Connectivity and Dropout Stability of SGD Solutions for Over-Parameterized Neural Networks.” In Proceedings of the 37th International Conference on Machine Learning, 119:8773–84. ML Research Press, 2020.

Javanmard, Adel, Marco Mondelli, and Andrea Montanari. “Analysis of a Two-Layer Neural Network via Displacement Convexity.”https://doi.org/10.1214/20-AOS1945__Annals of Statistics. Institute of Mathematical Statistics, 2020.https://doi.org/10.1214/20-AOS1945 .