| 2025 | ICLR | Probing the Latent Hierarchical Structure of Data via Diffusion Models. | Antonio Sclocchi, Alessandro Favero, Noam Itzhak Levi, Matthieu Wyart |
| 2025 | ICML | Learning curves theory for hierarchically compositional data with power-law distributed features. | Francesco Cagnetta, Hyunmo Kang, Matthieu Wyart |
| 2025 | ICML | How Compositional Generalization and Creativity Improve as Diffusion Models are Trained. | Alessandro Favero, Antonio Sclocchi, Francesco Cagnetta, Pascal Frossard, Matthieu Wyart |
| 2024 | ICML | How Deep Networks Learn Sparse and Hierarchical Data: the Sparse Random Hierarchy Model. | Umberto M. Tomasini, Matthieu Wyart |
| 2023 | ICML | What Can Be Learnt With Wide Convolutional Neural Networks? | Francesco Cagnetta, Alessandro Favero, Matthieu Wyart |
| 2023 | ICML | Dissecting the Effects of SGD Noise in Distinct Regimes of Deep Learning. | Antonio Sclocchi, Mario Geiger, Matthieu Wyart |
| 2022 | ICML | Failure and success of the spectral bias prediction for Laplace Kernel Ridge Regression: the case of low-dimensional data. | Umberto M. Tomasini, Antonio Sclocchi, Matthieu Wyart |
| 2018 | ICML | Comparing Dynamics: Deep Neural Networks versus Glassy Systems. | Marco Baity-Jesi, Levent Sagun, Mario Geiger, Stefano Spigler, Grard Ben Arous, Chiara Cammarota, Yann LeCun, Matthieu Wyart, Giulio Biroli |