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Barbier, J., Camilli, F., Mondelli, M., &; Sáenz, M. (2023). Fundamental limits in structured principal component analysis and how to reach them. Proceedings of the National Academy of Sciences of the United States of America. National Academy of Sciences.https://doi.org/10.1073/pnas.2302028120 

Shevchenko, A., Kögler, K., Hassani, H., &; Mondelli, M. (2023). Fundamental limits of two-layer autoencoders, and achieving them with gradient methods. In Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 31151–31209). Honolulu, Hawaii, HI, United States: ML Research Press.

Xu, Y., Hou, T. Q., Liang, S. S., &; Mondelli, M. (2023). Approximate message passing for multi-layer estimation in rotationally invariant models. In 2023 IEEE Information Theory Workshop (pp. 294–298). Saint-Malo, France: Institute of Electrical and Electronics Engineers.https://doi.org/10.1109/ITW55543.2023.10160238 

Bombari, S., Kiyani, S., &; Mondelli, M. (2023). Beyond the universal law of robustness: Sharper laws for random features and neural tangent kernels. In Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 2738–2776). Honolulu, HI, United States: ML Research Press.

Súkeník, P., Mondelli, M., &; Lampert, C. (n.d.). Deep neural collapse is provably optimal for the deep unconstrained features model. In 37th Annual Conference on Neural Information Processing Systems. New Orleans, LA, United States.

Wu, D., Kungurtsev, V., &; Mondelli, M. (2023). Mean-field analysis for heavy ball methods: Dropout-stability, connectivity, and global convergence. In Transactions on Machine Learning Research. ML Research Press.

Fu, T., Liu, Y., Barbier, J., Mondelli, M., Liang, S., &; Hou, T. (n.d.). Mismatched estimation of non-symmetric rank-one matrices corrupted by structured noise. In Proceedings of 2023 IEEE International Symposium on Information Theory. Taipei, Taiwan: IEEE.https://doi.org/10.1109/isit54713.2023.10206671 

Esposito, A. R., &; Mondelli, M. (2023). Concentration without independence via information measures. In Proceedings of 2023 IEEE International Symposium on Information Theory (pp. 400–405). Taipei, Taiwan: IEEE.https://doi.org/10.1109/isit54713.2023.10206899 

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.

Fathollahi, D., &; Mondelli, M. (2022). Polar coded computing: The role of the scaling exponent. In 2022 IEEE International Symposium on Information Theory (Vol. 2022, pp. 2154–2159). Espoo, Finland: IEEE.https://doi.org/10.1109/ISIT50566.2022.9834712 

Venkataramanan, R., Kögler, K., &; Mondelli, M. (2022). Estimation in rotationally invariant generalized linear models via approximate message passing. In Proceedings of the 39th International Conference on Machine Learning (Vol. 162). Baltimore, MD, United States: ML Research Press.

Barbier, J., Hou, T., Mondelli, M., &; Saenz, M. (n.d.). The price of ignorance: How much does it cost to forget noise structure in low-rank matrix estimation? arXiv.https://doi.org/10.48550/arXiv.2205.10009 

Doan, N., Hashemi, S. A., Mondelli, M., &; Gross, W. J. (2022). Decoding Reed-Muller codes with successive codeword permutations. IEEE Transactions on Communications. Institute of Electrical and Electronics Engineers.https://doi.org/10.1109/tcomm.2022.3211101 

Hashemi, S. A., Mondelli, M., Fazeli, A., Vardy, A., Cioffi, J., &; Goldsmith, A. (2022). Parallelism versus latency in simplified successive-cancellation decoding of polar codes. IEEE Transactions on Wireless Communications. Institute of Electrical and Electronics Engineers.https://doi.org/10.1109/TWC.2021.3125626 

Amani, M. H., Bombari, S., Mondelli, M., Pukdee, R., &; Rini, S. (2022). Sharp asymptotics on the compression of two-layer neural networks. IEEE Information Theory Workshop. Mumbai, India: IEEE.https://doi.org/10.1109/ITW54588.2022.9965870 

Bombari, S., Amani, M. H., &; Mondelli, M. (2022). Memorization and optimization in deep neural networks with minimum over-parameterization. In 36th Conference on Neural Information Processing Systems (Vol. 35, pp. 7628–7640). Curran Associates.

Mondelli, M., &; Venkataramanan, R. (2022). Approximate message passing with spectral initialization for generalized linear models. Journal of Statistical Mechanics: Theory and Experiment. IOP Publishing.https://doi.org/10.1088/1742-5468/ac9828 

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.

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