Please note that LibreCat no longer supports Internet Explorer versions 8 or 9 (or earlier).
We recommend upgrading to the latest Internet Explorer, Google Chrome, or Firefox.
61 Publications
2022 |Published| Conference Paper | IST-REx-ID: 12536 | 
Barbier, J., Hou, T., Mondelli, M., & Saenz, M. (2022). The price of ignorance: How much does it cost to forget noise structure in low-rank matrix estimation? In 36th Annual Conference on Neural Information Processing Systems (Vol. 35). New Orleans, LA, United States.
[Preprint]
View
| Download Preprint (ext.)
| arXiv
2022 |Published| Conference Paper | IST-REx-ID: 17086 |
Zhang, Y., & Weinberger, N. (2022). Mean estimation in high-dimensional binary Markov Gaussian mixture models. In 36th Conference on Neural Information Processing Systems (Vol. 35). New Orleans, LA, United States: ML Research Press.
[Published Version]
View
| Files available
| arXiv
2022 |Published| Journal Article | IST-REx-ID: 11420 |
Shevchenko, A., Kungurtsev, V., & Mondelli, M. (2022). Mean-field analysis of piecewise linear solutions for wide ReLU networks. Journal of Machine Learning Research. Journal of Machine Learning Research.
[Published Version]
View
| Files available
| arXiv
2022 |Published| Conference Paper | IST-REx-ID: 12018
Zhang, Y., & Vatedka, S. (2022). Lower bounds on list decoding capacity using error exponents. In 2022 IEEE International Symposium on Information Theory (Vol. 2022, pp. 1324–1329). Espoo, Finland: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ISIT50566.2022.9834815
View
| DOI
2021 |Published| Conference Paper | IST-REx-ID: 10595 |
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.
[Published Version]
View
| Download Published Version (ext.)
| arXiv
2021 |Published| Conference Paper | IST-REx-ID: 10599 |
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
[Preprint]
View
| DOI
| Download Preprint (ext.)
| arXiv
2021 |Published| Conference Paper | IST-REx-ID: 13146 |
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.
[Published Version]
View
| Files available
| arXiv
2021 |Published| Journal Article | IST-REx-ID: 9047 |
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
[Preprint]
View
| Files available
| DOI
| Download Preprint (ext.)
| WoS
| arXiv
2021 |Published| Conference Paper | IST-REx-ID: 10053 |
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
[Preprint]
View
| Files available
| DOI
| Download Preprint (ext.)
| WoS
| arXiv
2021 |Published| Conference Paper | IST-REx-ID: 10597 |
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
[Preprint]
View
| DOI
| Download Preprint (ext.)
| WoS
| arXiv
2021 |Published| Conference Paper | IST-REx-ID: 10593 |
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.
[Preprint]
View
| Download Preprint (ext.)
| arXiv
2021 |Published| Conference Paper | IST-REx-ID: 10594 |
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.
[Preprint]
View
| Download Preprint (ext.)
| arXiv
2021 |Published| Conference Paper | IST-REx-ID: 10598 |
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.
[Preprint]
View
| Files available
| Download Preprint (ext.)
| arXiv
2021 |Published| Journal Article | IST-REx-ID: 9002
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
[Preprint]
View
| Files available
| DOI
| arXiv
2021 |Published| Journal Article | IST-REx-ID: 15254 |
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
[Preprint]
View
| DOI
| Download Preprint (ext.)
| arXiv
2020 |Published| Conference Paper | IST-REx-ID: 9221 |
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.
[Preprint]
View
| Download Preprint (ext.)
| arXiv
2020 |Published| Conference Paper | IST-REx-ID: 8536 |
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
[Preprint]
View
| Files available
| DOI
| Download Preprint (ext.)
| arXiv
2020 |Published| Journal Article | IST-REx-ID: 6748 |
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
[Preprint]
View
| DOI
| Download Preprint (ext.)
| WoS
| arXiv
2020 |Published| Conference Paper | IST-REx-ID: 9198 |
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.
[Published Version]
View
| Files available
| arXiv
2019 |Published| Journal Article | IST-REx-ID: 6750 |
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
[Preprint]
View
| DOI
| Download Preprint (ext.)
| arXiv