Nguyen Q, Mondelli M, Montufar GF. Tight bounds on the smallest eigenvalue of the neural tangent kernel for deep ReLU networks. In: Meila M, Zhang T, eds. Proceedings of the 38th International Conference on Machine Learning. Vol 139. ML Research Press; 2021:8119-8129.
LibreCat: https://research-explorer.ista.ac.at/record/10595
arXiv: 2012.11654

Hashemi SA, Mondelli M, Cioffi J, Goldsmith A. Successive syndrome-check decoding of polar codes. In: Proceedings of the 55th Asilomar Conference on Signals, Systems, and Computers. Vol 2021-October. Institute of Electrical and Electronics Engineers; 2021:943-947. doi: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. 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. ML Research Press; 2021:8119-8129.
LibreCat: https://research-explorer.ista.ac.at/record/13146
arXiv: 2012.11654

Mondelli M, Hashemi SA, Cioffi JM, Goldsmith A. Sublinear latency for simplified successive cancellation decoding of polar codes. IEEE Transactions on Wireless Communications. 2021;20(1):18-27. doi: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 SA, Mondelli M, Fazeli A, Vardy A, Cioffi J, Goldsmith A. Parallelism versus latency in simplified successive-cancellation decoding of polar codes. In: 2021 IEEE International Symposium on Information Theory. Institute of Electrical and Electronics Engineers; 2021:2369-2374. doi: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 SA, Mondelli M. Sparse multi-decoder recursive projection aggregation for Reed-Muller codes. In: 2021 IEEE International Symposium on Information Theory. Institute of Electrical and Electronics Engineers; 2021:1082-1087. doi: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. PCA initialization for approximate message passing in rotationally invariant models. In: 35th Conference on Neural Information Processing Systems. Vol 35. Neural Information Processing Systems Foundation; 2021:29616-29629.
LibreCat: https://research-explorer.ista.ac.at/record/10593
arXiv: 2106.02356

Nguyen Q, Bréchet P, Mondelli M. When are solutions connected in deep networks? In: 35th Conference on Neural Information Processing Systems. Vol 35. Neural Information Processing Systems Foundation; 2021.
LibreCat: https://research-explorer.ista.ac.at/record/10594
arXiv: 2102.09671

Mondelli M, Venkataramanan R. Approximate message passing with spectral initialization for generalized linear models. In: Banerjee A, Fukumizu K, eds. Proceedings of The 24th International Conference on Artificial Intelligence and Statistics. Vol 130. ML Research Press; 2021:397-405.
LibreCat: https://research-explorer.ista.ac.at/record/10598
arXiv: 2010.03460

Fazeli A, Hassani H, Mondelli M, Vardy A. Binary linear codes with optimal scaling: Polar codes with large kernels. IEEE Transactions on Information Theory. 2021;67(9):5693-5710. doi: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. Network coding with myopic adversaries. IEEE Journal on Selected Areas in Information Theory. 2021;2(4):1108-1119. doi: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. Global convergence of deep networks with one wide layer followed by pyramidal topology. In: 34th Conference on Neural Information Processing Systems. Vol 33. Curran Associates; 2020:11961–11972.
LibreCat: https://research-explorer.ista.ac.at/record/9221
arXiv: 2002.07867

Mondelli M, Hashemi SA, Cioffi J, Goldsmith A. Simplified successive cancellation decoding of polar codes has sublinear latency. In: IEEE International Symposium on Information Theory - Proceedings. Vol 2020-June. IEEE; 2020. doi: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. 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. ML Research Press; 2020:8773-8784.
LibreCat: https://research-explorer.ista.ac.at/record/9198
arXiv: 1912.10095

Javanmard A, Mondelli M, Montanari A. Analysis of a two-layer neural network via displacement convexity. Annals of Statistics. 2020;48(6):3619-3642. doi:10.1214/20-AOS1945
LibreCat: https://research-explorer.ista.ac.at/record/6748
DOI: 10.1214/20-AOS1945
WoS: 000598369200021
arXiv: 1901.01375

Hashemi SA, Condo C, Mondelli M, Gross WJ. Rate-flexible fast polar decoders. IEEE Transactions on Signal Processing. 2019;67(22). doi: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 SH, Urbanke R. A new coding paradigm for the primitive relay channel. Algorithms. 2019;12(10). doi:10.3390/a12100218
LibreCat: https://research-explorer.ista.ac.at/record/7007
DOI: 10.3390/a12100218
arXiv: 1801.03153