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

Successive syndrome-check decoding of polar codes
S.A. Hashemi, M. Mondelli, J. Cioffi, A. Goldsmith, in:, Proceedings of the 55th Asilomar Conference on Signals, Systems, and Computers, Institute of Electrical and Electronics Engineers, 2021, pp. 943–947.

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.

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.

Parallelism versus latency in simplified successive-cancellation decoding of polar codes
S.A. Hashemi, M. Mondelli, A. Fazeli, A. Vardy, J. Cioffi, A. Goldsmith, in:, 2021 IEEE International Symposium on Information Theory, Institute of Electrical and Electronics Engineers, 2021, pp. 2369–2374.

Sparse multi-decoder recursive projection aggregation for Reed-Muller codes
D. Fathollahi, N. Farsad, S.A. Hashemi, M. Mondelli, in:, 2021 IEEE International Symposium on Information Theory, Institute of Electrical and Electronics Engineers, 2021, pp. 1082–1087.

PCA initialization for approximate message passing in rotationally invariant models
M. Mondelli, R. Venkataramanan, in:, 35th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2021, pp. 29616–29629.

When are solutions connected in deep networks?
Q. Nguyen, P. Bréchet, M. Mondelli, in:, 35th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2021.

Approximate message passing with spectral initialization for generalized linear models
M. Mondelli, R. Venkataramanan, in:, A. Banerjee, K. Fukumizu (Eds.), Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, ML Research Press, 2021, pp. 397–405.

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.

Network coding with myopic adversaries
S. Li, R. Bitar, S. Jaggi, Y. Zhang, IEEE Journal on Selected Areas in Information Theory 2 (2021) 1108–1119.

Global convergence of deep networks with one wide layer followed by pyramidal topology
Q. Nguyen, M. Mondelli, in:, 34th Conference on Neural Information Processing Systems, Curran Associates, 2020, pp. 11961–11972.

Simplified successive cancellation decoding of polar codes has sublinear latency
M. Mondelli, S.A. Hashemi, J. Cioffi, A. Goldsmith, in:, IEEE International Symposium on Information Theory - Proceedings, IEEE, 2020.

Landscape connectivity and dropout stability of SGD solutions for over-parameterized neural networks
A. Shevchenko, M. Mondelli, in:, Proceedings of the 37th International Conference on Machine Learning, ML Research Press, 2020, pp. 8773–8784.

Analysis of a two-layer neural network via displacement convexity
A. Javanmard, M. Mondelli, A. Montanari, Annals of Statistics 48 (2020) 3619–3642.

Rate-flexible fast polar decoders
S.A. Hashemi, C. Condo, M. Mondelli, W.J. Gross, IEEE Transactions on Signal Processing 67 (2019).

A new coding paradigm for the primitive relay channel
M. Mondelli, S.H. Hassani, R. Urbanke, Algorithms 12 (2019).