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