A. R. Esposito and M. Mondelli, “Concentration without independence via information measures,” IEEE Transactions on Information Theory. IEEE.

N. Resch, C. Yuan, and Y. Zhang, “Zero-rate thresholds and new capacity bounds for list-decoding and list-recovery,” in 50th International Colloquium on Automata, Languages, and Programming, Paderborn, Germany, 2023, vol. 261.

J. Barbier, F. Camilli, M. Mondelli, and M. Sáenz, “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, vol. 120, no. 30. National Academy of Sciences, 2023.

A. Shevchenko, K. Kögler, H. Hassani, and M. Mondelli, “Fundamental limits of two-layer autoencoders, and achieving them with gradient methods,” in Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, HI, United States, 2023, vol. 202, pp. 31151–31209.

Y. Zhang and S. Vatedka, “Multiple packing: Lower bounds via infinite constellations,” IEEE Transactions on Information Theory, vol. 69, no. 7. IEEE, pp. 4513–4527, 2023.

Y. Xu, T. Q. Hou, S. S. Liang, and M. Mondelli, “Approximate message passing for multi-layer estimation in rotationally invariant models,” in 2023 IEEE Information Theory Workshop, Saint-Malo, France, 2023, pp. 294–298.

Y. Zhang and S. Vatedka, “Multiple packing: Lower bounds via error exponents,” IEEE Transactions on Information Theory. IEEE, 2023.

Y. Zhang, “Zero-error communication over adversarial MACs,” IEEE Transactions on Information Theory, vol. 69, no. 7. Institute of Electrical and Electronics Engineers, pp. 4093–4127, 2023.

N. Polyanskii and Y. Zhang, “Codes for the Z-channel,” IEEE Transactions on Information Theory, vol. 69, no. 10. Institute of Electrical and Electronics Engineers, pp. 6340–6357, 2023.

S. Bombari, S. Kiyani, and M. Mondelli, “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, Honolulu, HI, United States, 2023, vol. 202, pp. 2738–2776.

P. Súkeník, M. Mondelli, and C. Lampert, “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.

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

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

A. R. Esposito and M. Mondelli, “Concentration without independence via information measures,” in Proceedings of 2023 IEEE International Symposium on Information Theory, Taipei, Taiwan, 2023, pp. 400–405.

A. Shevchenko, V. Kungurtsev, and M. Mondelli, “Mean-field analysis of piecewise linear solutions for wide ReLU networks,” Journal of Machine Learning Research, vol. 23, no. 130. Journal of Machine Learning Research, pp. 1–55, 2022.

Y. Zhang, S. Jaggi, M. Langberg, and A. D. Sarwate, “The capacity of causal adversarial channels,” in 2022 IEEE International Symposium on Information Theory, Espoo, Finland, 2022, vol. 2022, pp. 2523–2528.

A. K. Yadav, M. Alimohammadi, Y. Zhang, A. J. Budkuley, and S. Jaggi, “New results on AVCs with omniscient and myopic adversaries,” in 2022 IEEE International Symposium on Information Theory, Espoo, Finland, 2022, vol. 2022, pp. 2535–2540.

P. Joshi, A. Purkayastha, Y. Zhang, A. J. Budkuley, and S. Jaggi, “On the capacity of additive AVCs with feedback,” in 2022 IEEE International Symposium on Information Theory, Espoo, Finland, 2022, vol. 2022, pp. 504–509.

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

S. Torkamani, J. B. Ebrahimi, P. Sadeghi, R. G. L. D’Oliveira, and M. Médard, “Heterogeneous differential privacy via graphs,” in 2022 IEEE International Symposium on Information Theory, Espoo, Finland, 2022, vol. 2022, pp. 1623–1628.

Y. Zhang and S. Vatedka, “Lower bounds on list decoding capacity using error exponents,” in 2022 IEEE International Symposium on Information Theory, Espoo, Finland, 2022, vol. 2022, pp. 1324–1329.

Y. Zhang and S. Vatedka, “Lower bounds for multiple packing,” in 2022 IEEE International Symposium on Information Theory, Espoo, Finland, 2022, vol. 2022, pp. 3085–3090.

Y. Zhang and S. Vatedka, “List-decodability of Poisson Point Processes,” in 2022 IEEE International Symposium on Information Theory, Espoo, Finland, 2022, vol. 2022, pp. 2559–2564.

N. Polyanskii and Y. Zhang, “List-decodable zero-rate codes for the Z-channel,” in 2022 IEEE International Symposium on Information Theory, Espoo, Finland, 2022, vol. 2022, pp. 2553–2558.

R. Venkataramanan, K. Kögler, and M. Mondelli, “Estimation in rotationally invariant generalized linear models via approximate message passing,” in Proceedings of the 39th International Conference on Machine Learning, Baltimore, MD, United States, 2022, vol. 162.

J. Barbier, T. Hou, M. Mondelli, and M. Saenz, “The price of ignorance: How much does it cost to forget noise structure in low-rank matrix estimation?,” arXiv. .

S. Bombari et al., “Towards differential relational privacy and its use in question answering,” arXiv. .

Y. Zhang and S. Vatedka, “List decoding random Euclidean codes and Infinite constellations,” IEEE Transactions on Information Theory, vol. 68, no. 12. IEEE, pp. 7753–7786, 2022.

N. Doan, S. A. Hashemi, M. Mondelli, and W. J. Gross, “Decoding Reed-Muller codes with successive codeword permutations,” IEEE Transactions on Communications, vol. 70, no. 11. Institute of Electrical and Electronics Engineers, pp. 7134–7145, 2022.

Y. Zhang, S. Vatedka, S. Jaggi, and A. D. Sarwate, “Quadratically constrained myopic adversarial channels,” IEEE Transactions on Information Theory, vol. 68, no. 8. Institute of Electrical and Electronics Engineers, pp. 4901–4948, 2022.

S. A. Hashemi, M. Mondelli, A. Fazeli, A. Vardy, J. Cioffi, and A. Goldsmith, “Parallelism versus latency in simplified successive-cancellation decoding of polar codes,” IEEE Transactions on Wireless Communications, vol. 21, no. 6. Institute of Electrical and Electronics Engineers, pp. 3909–3920, 2022.

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

S. Bombari, M. H. Amani, and M. Mondelli, “Memorization and optimization in deep neural networks with minimum over-parameterization,” in 36th Conference on Neural Information Processing Systems, 2022, vol. 35, pp. 7628–7640.

M. Mondelli and R. Venkataramanan, “Approximate message passing with spectral initialization for generalized linear models,” Journal of Statistical Mechanics: Theory and Experiment, vol. 2022, no. 11. IOP Publishing, 2022.

Q. Nguyen, M. Mondelli, and G. F. Montufar, “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, Virtual, 2021, vol. 139, pp. 8119–8129.

S. A. Hashemi, M. Mondelli, J. Cioffi, and A. Goldsmith, “Successive syndrome-check decoding of polar codes,” in Proceedings of the 55th Asilomar Conference on Signals, Systems, and Computers, Virtual, Pacific Grove, CA, United States, 2021, vol. 2021–October, pp. 943–947.

Q. Nguyen, M. Mondelli, and G. Montufar, “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, Virtual, 2021, vol. 139, pp. 8119–8129.

M. Mondelli, S. A. Hashemi, J. M. Cioffi, and A. Goldsmith, “Sublinear latency for simplified successive cancellation decoding of polar codes,” IEEE Transactions on Wireless Communications, vol. 20, no. 1. IEEE, pp. 18–27, 2021.

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

D. Fathollahi, N. Farsad, S. A. Hashemi, and M. Mondelli, “Sparse multi-decoder recursive projection aggregation for Reed-Muller codes,” in 2021 IEEE International Symposium on Information Theory, Virtual, Melbourne, Australia, 2021, pp. 1082–1087.

M. Mondelli, C. Thrampoulidis, and R. Venkataramanan, “Optimal combination of linear and spectral estimators for generalized linear models,” Foundations of Computational Mathematics. Springer, 2021.

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

Q. Nguyen, P. Bréchet, and M. Mondelli, “When are solutions connected in deep networks?,” in 35th Conference on Neural Information Processing Systems, Virtual, 2021, vol. 35.

M. Mondelli and R. Venkataramanan, “Approximate message passing with spectral initialization for generalized linear models,” in Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, Virtual, San Diego, CA, United States, 2021, vol. 130, pp. 397–405.

A. Fazeli, H. Hassani, M. Mondelli, and A. Vardy, “Binary linear codes with optimal scaling: Polar codes with large kernels,” IEEE Transactions on Information Theory, vol. 67, no. 9. IEEE, pp. 5693–5710, 2021.

S. Li, R. Bitar, S. Jaggi, and Y. Zhang, “Network coding with myopic adversaries,” IEEE Journal on Selected Areas in Information Theory, vol. 2, no. 4. IEEE, pp. 1108–1119, 2021.

Q. Nguyen and M. Mondelli, “Global convergence of deep networks with one wide layer followed by pyramidal topology,” in 34th Conference on Neural Information Processing Systems, Vancouver, Canada, 2020, vol. 33, pp. 11961–11972.

M. Mondelli, S. A. Hashemi, J. Cioffi, and A. Goldsmith, “Simplified successive cancellation decoding of polar codes has sublinear latency,” in IEEE International Symposium on Information Theory - Proceedings, Los Angeles, CA, United States, 2020, vol. 2020–June.

A. Shevchenko and M. Mondelli, “Landscape connectivity and dropout stability of SGD solutions for over-parameterized neural networks,” in Proceedings of the 37th International Conference on Machine Learning, 2020, vol. 119, pp. 8773–8784.

A. Javanmard, M. Mondelli, and A. Montanari, “Analysis of a two-layer neural network via displacement convexity,” Annals of Statistics, vol. 48, no. 6. Institute of Mathematical Statistics, pp. 3619–3642, 2020.

S. A. Hashemi, C. Condo, M. Mondelli, and W. J. Gross, “Rate-flexible fast polar decoders,” IEEE Transactions on Signal Processing, vol. 67, no. 22. IEEE, 2019.

M. Mondelli, S. H. Hassani, and R. Urbanke, “A new coding paradigm for the primitive relay channel,” Algorithms, vol. 12, no. 10. MDPI, 2019.