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., &; 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