Concentration without independence via information measures
A.R. Esposito, M. Mondelli, IEEE Transactions on Information Theory (n.d.).

Zero-rate thresholds and new capacity bounds for list-decoding and list-recovery
N. Resch, C. Yuan, Y. Zhang, in:, 50th International Colloquium on Automata, Languages, and Programming, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2023.

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).

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.

Multiple packing: Lower bounds via infinite constellations
Y. Zhang, S. Vatedka, IEEE Transactions on Information Theory 69 (2023) 4513–4527.

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.

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

Zero-error communication over adversarial MACs
Y. Zhang, IEEE Transactions on Information Theory 69 (2023) 4093–4127.

Codes for the Z-channel
N. Polyanskii, Y. Zhang, IEEE Transactions on Information Theory 69 (2023) 6340–6357.

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.

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.

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.

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.

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

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.

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

New results on AVCs with omniscient and myopic adversaries
A.K. Yadav, M. Alimohammadi, Y. Zhang, A.J. Budkuley, S. Jaggi, in:, 2022 IEEE International Symposium on Information Theory, Institute of Electrical and Electronics Engineers, 2022, pp. 2535–2540.

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

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.

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