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.
LibreCat: https://research-explorer.ista.ac.at/record/12538
DOI: 10.1109/ITW54588.2022.9965870
arXiv: 2205.08199

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.
LibreCat: https://research-explorer.ista.ac.at/record/12537
arXiv: 2205.10217

Approximate message passing with spectral initialization for generalized linear models
M. Mondelli, R. Venkataramanan, Journal of Statistical Mechanics: Theory and Experiment 2022 (2022).
LibreCat: https://research-explorer.ista.ac.at/record/12480
DOI: 10.1088/1742-5468/ac9828
WoS: 000889589900001

Optimal combination of linear and spectral estimators for generalized linear models
M. Mondelli, C. Thrampoulidis, R. Venkataramanan, Foundations of Computational Mathematics 22 (2022) 1513–1566.
LibreCat: https://research-explorer.ista.ac.at/record/10211
DOI: 10.1007/s10208-021-09531-x
WoS: 000685721000001
arXiv: 2008.03326

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, in:, 36th Annual Conference on Neural Information Processing Systems, 2022.
LibreCat: https://research-explorer.ista.ac.at/record/12536
arXiv: 2205.10009

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.
LibreCat: https://research-explorer.ista.ac.at/record/10595
arXiv: 2012.11654

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.
LibreCat: https://research-explorer.ista.ac.at/record/10599
DOI: 10.1109/IEEECONF53345.2021.9723394
arXiv: 2112.00057

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.
LibreCat: https://research-explorer.ista.ac.at/record/13146
arXiv: 2012.11654

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.
LibreCat: https://research-explorer.ista.ac.at/record/9047
DOI: 10.1109/TWC.2020.3022922
WoS: 000607808800002
arXiv: 1909.04892

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.
LibreCat: https://research-explorer.ista.ac.at/record/10053
DOI: 10.1109/ISIT45174.2021.9518153
WoS: 000701502202078
arXiv: 2012.13378

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.
LibreCat: https://research-explorer.ista.ac.at/record/10597
DOI: 10.1109/isit45174.2021.9517887
WoS: 000701502201029
arXiv: 2011.12882

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.
LibreCat: https://research-explorer.ista.ac.at/record/10593
arXiv: 2106.02356

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.
LibreCat: https://research-explorer.ista.ac.at/record/10594
arXiv: 2102.09671

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.
LibreCat: https://research-explorer.ista.ac.at/record/10598
arXiv: 2010.03460

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.
LibreCat: https://research-explorer.ista.ac.at/record/9002
DOI: 10.1109/TIT.2020.3038806
arXiv: 1711.01339

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.
LibreCat: https://research-explorer.ista.ac.at/record/15254
DOI: 10.1109/JSAIT.2021.3126474
arXiv: 2102.09885

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.
LibreCat: https://research-explorer.ista.ac.at/record/9221
arXiv: 2002.07867

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.
LibreCat: https://research-explorer.ista.ac.at/record/8536
DOI: 10.1109/ISIT44484.2020.9174141
arXiv: 1909.04892

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.
LibreCat: https://research-explorer.ista.ac.at/record/9198
arXiv: 1912.10095

Analysis of a two-layer neural network via displacement convexity
A. Javanmard, M. Mondelli, A. Montanari, Annals of Statistics 48 (2020) 3619–3642.
LibreCat: https://research-explorer.ista.ac.at/record/6748
DOI: 10.1214/20-AOS1945
WoS: 000598369200021
arXiv: 1901.01375