From sparse selection to risk prediction: Approximate message passing for proteomic survival models and large-scale genomics
A. Depope, From Sparse Selection to Risk Prediction: Approximate Message Passing for Proteomic Survival Models and Large-Scale Genomics, Institute of Science and Technology Austria, 2026.

Joint modeling of whole-genome sequencing data for human height via approximate message passing
A. Depope, J. Bajzik, M. Mondelli, M.R. Robinson, Cell Genomics 6 (2026).

Precise asymptotics for spectral methods in mixed generalized linear models
Y. Zhang, M. Mondelli, R. Venkataramanan, SIAM Journal on Mathematics of Data Science 8 (2026) 411–439.

Information limits and Thouless-Anderson-Palmer equations for spiked matrix models with structured noise
J. Barbier, F. Camilli, Y. Xu, M. Mondelli, Physical Review Research 7 (2025).

Privacy for free in the overparameterized regime
S. Bombari, M. Mondelli, Proceedings of the National Academy of Sciences 122 (2025).

Spectral estimators for structured generalized linear models via approximate message passing
Y. Zhang, H.C. Ji, R. Venkataramanan, M. Mondelli, Mathematical Statistics and Learning 8 (2025) 193–304.

Concentration without independence via information measures
A.R. Esposito, M. Mondelli, IEEE Transactions on Information Theory 70 (2024) 3823–3839.

Average gradient outer product as a mechanism for deep neural collapse
D. Beaglehole, P. Súkeník, M. Mondelli, M. Belkin, in:, 38th Annual Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2024.

Neural collapse versus low-rank bias: Is deep neural collapse really optimal?
P. Súkeník, C. Lampert, M. Mondelli, in:, 38th Annual Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2024.

Improved convergence of score-based diffusion models via prediction-correction
F. Pedrotti, J. Maas, M. Mondelli, in:, Transactions on Machine Learning Research, 2024.

How spurious features are memorized: Precise analysis for random and NTK features
S. Bombari, M. Mondelli, in:, 41st International Conference on Machine Learning, ML Research Press, 2024, pp. 4267–4299.

Towards understanding the word sensitivity of attention layers: A study via random features
S. Bombari, M. Mondelli, in:, 41st International Conference on Machine Learning, ML Research Press, 2024, pp. 4300–4328.

Improved convergence of score-based diffusion models via prediction-correction
F. Pedrotti, J. Maas, M. Mondelli, ArXiv (n.d.).

Inference of genetic effects via approximate message passing
A. Depope, M. Mondelli, M.R. Robinson, in:, 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing, IEEE, 2024, pp. 13151–13155.

High-dimensional limits in artificial neural networks
A. Shevchenko, High-Dimensional Limits in Artificial Neural Networks, Institute of Science and Technology Austria, 2024.

Compression of structured data with autoencoders: Provable benefit of nonlinearities and depth
K. Kögler, A. Shevchenko, H. Hassani, M. Mondelli, in:, Proceedings of the 41st International Conference on Machine Learning, ML Research Press, 2024, pp. 24964–25015.

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).

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 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.