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Zhang, Y., &; Vatedka, S. (2022). List decoding random Euclidean codes and Infinite constellations. IEEE Transactions on Information Theory. IEEE.https://doi.org/10.1109/TIT.2022.3189542 

Doan, N., Hashemi, S. A., Mondelli, M., &; Gross, W. J. (2022). Decoding Reed-Muller codes with successive codeword permutations. IEEE Transactions on Communications. Institute of Electrical and Electronics Engineers.https://doi.org/10.1109/tcomm.2022.3211101 

Zhang, Y., Vatedka, S., Jaggi, S., &; Sarwate, A. D. (2022). Quadratically constrained myopic adversarial channels. IEEE Transactions on Information Theory. Institute of Electrical and Electronics Engineers.https://doi.org/10.1109/tit.2022.3167554 

Hashemi, S. A., Mondelli, M., Fazeli, A., Vardy, A., Cioffi, J., &; Goldsmith, A. (2022). Parallelism versus latency in simplified successive-cancellation decoding of polar codes. IEEE Transactions on Wireless Communications. Institute of Electrical and Electronics Engineers.https://doi.org/10.1109/TWC.2021.3125626 

Amani, M. H., Bombari, S., Mondelli, M., Pukdee, R., &; Rini, S. (2022). Sharp asymptotics on the compression of two-layer neural networks. IEEE Information Theory Workshop. Mumbai, India: IEEE.https://doi.org/10.1109/ITW54588.2022.9965870 

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

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

Nguyen, Q., Mondelli, M., &; Montufar, G. F. (2021). Tight bounds on the smallest eigenvalue of the neural tangent kernel for deep ReLU networks. In M. Meila &; T. Zhang (Eds.), Proceedings of the 38th International Conference on Machine Learning (Vol. 139, pp. 8119–8129). Virtual: ML Research Press.

Hashemi, S. A., Mondelli, M., Cioffi, J., &; Goldsmith, A. (2021). Successive syndrome-check decoding of polar codes. In Proceedings of the 55th Asilomar Conference on Signals, Systems, and Computers (Vol. 2021–October, pp. 943–947). Virtual, Pacific Grove, CA, United States: Institute of Electrical and Electronics Engineers.https://doi.org/10.1109/IEEECONF53345.2021.9723394 

Nguyen, Q., Mondelli, M., &; Montufar, G. (2021). 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, pp. 8119–8129). Virtual: ML Research Press.

Mondelli, M., Hashemi, S. A., Cioffi, J. M., &; Goldsmith, A. (2021). Sublinear latency for simplified successive cancellation decoding of polar codes. IEEE Transactions on Wireless Communications. IEEE.https://doi.org/10.1109/TWC.2020.3022922 

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

Fathollahi, D., Farsad, N., Hashemi, S. A., &; Mondelli, M. (2021). Sparse multi-decoder recursive projection aggregation for Reed-Muller codes. In 2021 IEEE International Symposium on Information Theory (pp. 1082–1087). Virtual, Melbourne, Australia: Institute of Electrical and Electronics Engineers.https://doi.org/10.1109/isit45174.2021.9517887 

Mondelli, M., Thrampoulidis, C., &; Venkataramanan, R. (2021). Optimal combination of linear and spectral estimators for generalized linear models. Foundations of Computational Mathematics. Springer.https://doi.org/10.1007/s10208-021-09531-x 

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

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

Mondelli, M., &; Venkataramanan, R. (2021). Approximate message passing with spectral initialization for generalized linear models. In A. Banerjee &; K. Fukumizu (Eds.), Proceedings of The 24th International Conference on Artificial Intelligence and Statistics (Vol. 130, pp. 397–405). Virtual, San Diego, CA, United States: ML Research Press.

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

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

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

Mondelli, M., Hashemi, S. A., Cioffi, J., &; Goldsmith, A. (2020). Simplified successive cancellation decoding of polar codes has sublinear latency. In IEEE International Symposium on Information Theory - Proceedings (Vol. 2020–June). Los Angeles, CA, United States: IEEE.https://doi.org/10.1109/ISIT44484.2020.9174141 

Shevchenko, A., &; Mondelli, M. (2020). 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, pp. 8773–8784). ML Research Press.

Javanmard, A., Mondelli, M., &; Montanari, A. (2020). Analysis of a two-layer neural network via displacement convexity. Annals of Statistics. Institute of Mathematical Statistics.https://doi.org/10.1214/20-AOS1945 

Hashemi, S. A., Condo, C., Mondelli, M., &; Gross, W. J. (2019). Rate-flexible fast polar decoders. IEEE Transactions on Signal Processing. IEEE.https://doi.org/10.1109/TSP.2019.2944738 

Mondelli, M., Hassani, S. H., &; Urbanke, R. (2019). A new coding paradigm for the primitive relay channel. Algorithms. MDPI.https://doi.org/10.3390/a12100218