Beyond the universal law of robustness: Sharper laws for random features and neural tangent kernels
S. Bombari, S. Kiyani, M. Mondelli, in:, Proceedings of the 40th International Conference on Machine Learning, ML Research Press, 2023, pp. 2738–2776.

Fundamental limits in structured principal component analysis and how to reach them
J. Barbier, F. Camilli, M. Mondelli, M. Sáenz, Proceedings of the National Academy of Sciences of the United States of America 120 (2023).

Approximate message passing for multi-layer estimation in rotationally invariant models
Y. Xu, T.Q. Hou, S.S. Liang, M. Mondelli, in:, 2023 IEEE Information Theory Workshop, Institute of Electrical and Electronics Engineers, 2023, pp. 294–298.

Fundamental limits of two-layer autoencoders, and achieving them with gradient methods
A. Shevchenko, K. Kögler, H. Hassani, M. Mondelli, in:, Proceedings of the 40th International Conference on Machine Learning, ML Research Press, 2023, pp. 31151–31209.

Deep neural collapse is provably optimal for the deep unconstrained features model
P. Súkeník, M. Mondelli, C. Lampert, in:, 37th Annual Conference on Neural Information Processing Systems, n.d.

Concentration without independence via information measures
A.R. Esposito, M. Mondelli, in:, Proceedings of 2023 IEEE International Symposium on Information Theory, IEEE, n.d.

Mismatched estimation of non-symmetric rank-one matrices corrupted by structured noise
T. Fu, Y. Liu, J. Barbier, M. Mondelli, S. Liang, T. Hou, in:, Proceedings of 2023 IEEE International Symposium on Information Theory, IEEE, n.d.

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

Approximate message passing with spectral initialization for generalized linear models
M. Mondelli, R. Venkataramanan, Journal of Statistical Mechanics: Theory and Experiment 2022 (2022).

The price of ignorance: How much does it cost to forget noise structure in low-rank matrix estimation?
J. Barbier, T. Hou, M. Mondelli, M. Saenz, ArXiv (n.d.).

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

Sharp asymptotics on the compression of two-layer neural networks
M.H. Amani, S. Bombari, M. Mondelli, R. Pukdee, S. Rini, IEEE Information Theory Workshop (2022) 588–593.

Estimation in rotationally invariant generalized linear models via approximate message passing
R. Venkataramanan, K. Kögler, M. Mondelli, in:, Proceedings of the 39th International Conference on Machine Learning, ML Research Press, 2022.

Mean-field analysis of piecewise linear solutions for wide ReLU networks
A. Shevchenko, V. Kungurtsev, M. Mondelli, Journal of Machine Learning Research 23 (2022) 1–55.

Polar coded computing: The role of the scaling exponent
D. Fathollahi, M. Mondelli, in:, 2022 IEEE International Symposium on Information Theory, IEEE, 2022, pp. 2154–2159.

Decoding Reed-Muller codes with successive codeword permutations
N. Doan, S.A. Hashemi, M. Mondelli, W.J. Gross, IEEE Transactions on Communications 70 (2022) 7134–7145.

Parallelism versus latency in simplified successive-cancellation decoding of polar codes
S.A. Hashemi, M. Mondelli, A. Fazeli, A. Vardy, J. Cioffi, A. Goldsmith, IEEE Transactions on Wireless Communications 21 (2022) 3909–3920.

Binary linear codes with optimal scaling: Polar codes with large kernels
A. Fazeli, H. Hassani, M. Mondelli, A. Vardy, IEEE Transactions on Information Theory 67 (2021) 5693–5710.

Sublinear latency for simplified successive cancellation decoding of polar codes
M. Mondelli, S.A. Hashemi, J.M. Cioffi, A. Goldsmith, IEEE Transactions on Wireless Communications 20 (2021) 18–27.

Tight bounds on the smallest Eigenvalue of the neural tangent kernel for deep ReLU networks
Q. Nguyen, M. Mondelli, G. Montufar, in:, Proceedings of the 38th International Conference on Machine Learning, ML Research Press, 2021, pp. 8119–8129.