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Statistical learning in high dimensions: a rigorous statistical physics approach. (Apprentissage statistique en grandes dimensions: une approche rigoureuse par la physique statistique).

. PSL University, Paris, France, (2022)

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Learning curves of generic features maps for realistic datasets with a teacher-student model., , , , , , and . NeurIPS, page 18137-18151. (2021)Multi-layer State Evolution Under Random Convolutional Design., , , and . NeurIPS, (2022)Fluctuations, Bias, Variance & Ensemble of Learners: Exact Asymptotics for Convex Losses in High-Dimension., , , , and . ICML, volume 162 of Proceedings of Machine Learning Research, page 14283-14314. PMLR, (2022)Asymptotic Errors for High-Dimensional Convex Penalized Linear Regression beyond Gaussian Matrices., , and . COLT, volume 125 of Proceedings of Machine Learning Research, page 1682-1713. PMLR, (2020)Learning Gaussian Mixtures with Generalised Linear Models: Precise Asymptotics in High-dimensions., , , , , and . CoRR, (2021)Applying statistical learning theory to deep learning., , , , , and . CoRR, (2023)Statistical learning in high dimensions: a rigorous statistical physics approach. (Apprentissage statistique en grandes dimensions: une approche rigoureuse par la physique statistique).. PSL University, Paris, France, (2022)Learning Gaussian Mixtures with Generalized Linear Models: Precise Asymptotics in High-dimensions., , , , , and . NeurIPS, page 10144-10157. (2021)Rigorous Dynamical Mean-Field Theory for Stochastic Gradient Descent Methods., , , , and . SIAM J. Math. Data Sci., 6 (2): 400-427 (2024)