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.
LibreCat: https://research-explorer.ista.ac.at/record/10595
arXiv: 2012.11654

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 
LibreCat: https://research-explorer.ista.ac.at/record/10599
DOI: 10.1109/IEEECONF53345.2021.9723394
arXiv: 2112.00057

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.
LibreCat: https://research-explorer.ista.ac.at/record/13146
arXiv: 2012.11654

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 
LibreCat: https://research-explorer.ista.ac.at/record/9047
DOI: 10.1109/TWC.2020.3022922
WoS: 000607808800002
arXiv: 1909.04892

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 
LibreCat: https://research-explorer.ista.ac.at/record/10053
DOI: 10.1109/ISIT45174.2021.9518153
WoS: 000701502202078
arXiv: 2012.13378

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 
LibreCat: https://research-explorer.ista.ac.at/record/10597
DOI: 10.1109/isit45174.2021.9517887
WoS: 000701502201029
arXiv: 2011.12882

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.
LibreCat: https://research-explorer.ista.ac.at/record/10593
arXiv: 2106.02356

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.
LibreCat: https://research-explorer.ista.ac.at/record/10594
arXiv: 2102.09671

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.
LibreCat: https://research-explorer.ista.ac.at/record/10598
arXiv: 2010.03460

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 
LibreCat: https://research-explorer.ista.ac.at/record/9002
DOI: 10.1109/TIT.2020.3038806
arXiv: 1711.01339

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 
LibreCat: https://research-explorer.ista.ac.at/record/15254
DOI: 10.1109/JSAIT.2021.3126474
arXiv: 2102.09885

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.
LibreCat: https://research-explorer.ista.ac.at/record/9221
arXiv: 2002.07867

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 
LibreCat: https://research-explorer.ista.ac.at/record/8536
DOI: 10.1109/ISIT44484.2020.9174141
arXiv: 1909.04892

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.
LibreCat: https://research-explorer.ista.ac.at/record/9198
arXiv: 1912.10095

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 
LibreCat: https://research-explorer.ista.ac.at/record/6748
DOI: 10.1214/20-AOS1945
WoS: 000598369200021
arXiv: 1901.01375

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 
LibreCat: https://research-explorer.ista.ac.at/record/6750
DOI: 10.1109/TSP.2019.2944738
arXiv: 1903.09203

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 
LibreCat: https://research-explorer.ista.ac.at/record/7007
DOI: 10.3390/a12100218
arXiv: 1801.03153