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 (2026).

High-dimensional analysis of knowledge distillation: Weak-to-Strong generalization and scaling laws
M. Emrullah Ildiz, H.A. Gozeten, E.O. Taga, M. Mondelli, S. Oymak, in:, 13th International Conference on Learning Representations, ICLR, 2025, pp. 2967–3006.

Wide neural networks trained with weight decay provably exhibit neural collapse
A. Jacot, P. Súkeník, Z. Wang, M. Mondelli, in:, 13th International Conference on Learning Representations, ICLR, 2025, pp. 1905–1931.

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.

Efficient identification of wide shallow neural networks with biases
M. Fornasier, T. Klock, M. Mondelli, M. Rauchensteiner, Applied and Computational Harmonic Analysis 77 (2025).

Spurious correlations in high dimensional regression: The roles of regularization, simplicity bias and over-parameterization
S. Bombari, M. Mondelli, in:, Proceedings of the 42nd International Conference on Machine Learning, ML Research Press, 2025, pp. 4839–4873.

Test-time training provably improves transformers as in-context learners
H.A. Gozeten, M.E. Ildiz, X. Zhang, M. Soltanolkotabi, M. Mondelli, S. Oymak, in:, Proceedings of the 42nd International Conference on Machine Learning, ML Research Press, 2025, pp. 20266–20295.

Neural collapse beyond the unconstrained features model: Landscape, dynamics, and generalization in the mean-field regime
D. Wu, M. Mondelli, in:, Proceedings of the 42nd International Conference on Machine Learning, ML Research Press, 2025, pp. 67499–67536.

Spectral estimators for multi-index models: Precise asymptotics and optimal weak recovery
F. Kovadevi, Z. Yihan, M. Mondelli, in:, Proceedings of 38th Conference on Learning Theory, ML Research Press, 2025, pp. 3354–3404.

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).

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.

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

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.

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

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.