D. Fathollahi, N. Farsad, S. A. Hashemi, and M. Mondelli, “Sparse multi-decoder recursive projection aggregation for Reed-Muller codes,” in 2021 IEEE International Symposium on Information Theory, Virtual, Melbourne, Australia, 2021, pp. 1082–1087.
LibreCat: https://research-explorer.ista.ac.at/record/10597
DOI: 10.1109/isit45174.2021.9517887
WoS: 000701502201029
arXiv: 2011.12882

M. Mondelli and R. Venkataramanan, “PCA initialization for approximate message passing in rotationally invariant models,” in 35th Conference on Neural Information Processing Systems, Virtual, 2021, vol. 35, pp. 29616–29629.
LibreCat: https://research-explorer.ista.ac.at/record/10593
arXiv: 2106.02356

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

M. Mondelli and R. Venkataramanan, “Approximate message passing with spectral initialization for generalized linear models,” in Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, Virtual, San Diego, CA, United States, 2021, vol. 130, pp. 397–405.
LibreCat: https://research-explorer.ista.ac.at/record/10598
arXiv: 2010.03460

A. Fazeli, H. Hassani, M. Mondelli, and A. Vardy, “Binary linear codes with optimal scaling: Polar codes with large kernels,” IEEE Transactions on Information Theory, vol. 67, no. 9. IEEE, pp. 5693–5710, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/9002
DOI: 10.1109/TIT.2020.3038806
arXiv: 1711.01339

S. Li, R. Bitar, S. Jaggi, and Y. Zhang, “Network coding with myopic adversaries,” IEEE Journal on Selected Areas in Information Theory, vol. 2, no. 4. IEEE, pp. 1108–1119, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/15254
DOI: 10.1109/JSAIT.2021.3126474
arXiv: 2102.09885

Q. Nguyen and M. Mondelli, “Global convergence of deep networks with one wide layer followed by pyramidal topology,” in 34th Conference on Neural Information Processing Systems, Vancouver, Canada, 2020, vol. 33, pp. 11961–11972.
LibreCat: https://research-explorer.ista.ac.at/record/9221
arXiv: 2002.07867

M. Mondelli, S. A. Hashemi, J. Cioffi, and A. Goldsmith, “Simplified successive cancellation decoding of polar codes has sublinear latency,” in IEEE International Symposium on Information Theory - Proceedings, Los Angeles, CA, United States, 2020, vol. 2020–June.
LibreCat: https://research-explorer.ista.ac.at/record/8536
DOI: 10.1109/ISIT44484.2020.9174141
arXiv: 1909.04892

A. Shevchenko and M. Mondelli, “Landscape connectivity and dropout stability of SGD solutions for over-parameterized neural networks,” in Proceedings of the 37th International Conference on Machine Learning, 2020, vol. 119, pp. 8773–8784.
LibreCat: https://research-explorer.ista.ac.at/record/9198
arXiv: 1912.10095

A. Javanmard, M. Mondelli, and A. Montanari, “Analysis of a two-layer neural network via displacement convexity,” Annals of Statistics, vol. 48, no. 6. Institute of Mathematical Statistics, pp. 3619–3642, 2020.
LibreCat: https://research-explorer.ista.ac.at/record/6748
DOI: 10.1214/20-AOS1945
WoS: 000598369200021
arXiv: 1901.01375

S. A. Hashemi, C. Condo, M. Mondelli, and W. J. Gross, “Rate-flexible fast polar decoders,” IEEE Transactions on Signal Processing, vol. 67, no. 22. IEEE, 2019.
LibreCat: https://research-explorer.ista.ac.at/record/6750
DOI: 10.1109/TSP.2019.2944738
arXiv: 1903.09203

M. Mondelli, S. H. Hassani, and R. Urbanke, “A new coding paradigm for the primitive relay channel,” Algorithms, vol. 12, no. 10. MDPI, 2019.
LibreCat: https://research-explorer.ista.ac.at/record/7007
DOI: 10.3390/a12100218
arXiv: 1801.03153