Asymptotic Errors for High-Dimensional Convex Penalized Linear Regression beyond Gaussian Matrices. - Cédric Gerbelot; Alia Abbara; Florent Krzakala (COLT, 2020)

Learning curves of generic features maps for realistic datasets with a teacher-student model. - Bruno Loureiro; Cédric Gerbelot; Hugo Cui; Sebastian Goldt; Florent Krzakala; Marc Mézard; Lenka Zdeborová (NeurIPS, 2021)

Learning Gaussian Mixtures with Generalized Linear Models: Precise Asymptotics in High-dimensions. - Bruno Loureiro; Gabriele Sicuro; Cédric Gerbelot; Alessandro Pacco; Florent Krzakala; Lenka Zdeborová (NeurIPS, 2021)

Capturing the learning curves of generic features maps for realistic data sets with a teacher-student model. - Bruno Loureiro; Cédric Gerbelot; Hugo Cui; Sebastian Goldt; Florent Krzakala; Marc Mézard; Lenka Zdeborová (CoRR, 2021)

Learning Gaussian Mixtures with Generalised Linear Models: Precise Asymptotics in High-dimensions. - Bruno Loureiro; Gabriele Sicuro; Cédric Gerbelot; Alessandro Pacco; Florent Krzakala; Lenka Zdeborová (CoRR, 2021)

Graph-based Approximate Message Passing Iterations. - Cédric Gerbelot; Raphaël Berthier (CoRR, 2021)

Fluctuations, Bias, Variance & Ensemble of Learners: Exact Asymptotics for Convex Losses in High-Dimension. - Bruno Loureiro; Cédric Gerbelot; Maria Refinetti; Gabriele Sicuro; Florent Krzakala (ICML, 2022)

Multi-layer State Evolution Under Random Convolutional Design. - Max Daniels; Cédric Gerbelot; Florent Krzakala; Lenka Zdeborová (NeurIPS, 2022)

Statistical learning in high dimensions: a rigorous statistical physics approach. (Apprentissage statistique en grandes dimensions: une approche rigoureuse par la physique statistique). - Cédric Gerbelot (2022)

Applying statistical learning theory to deep learning. - Cédric Gerbelot; Avetik Karagulyan; Stefani Karp; Kavya Ravichandran; Menachem Stern; Nathan Srebro (CoRR, 2023)

Learning curves for the multi-class teacher-student perceptron. - Elisabetta Cornacchia; Francesca Mignacco; Rodrigo Veiga 001; Cédric Gerbelot; Bruno Loureiro; Lenka Zdeborová (Mach. Learn. Sci. Technol., 2023)

Asymptotic Errors for Teacher-Student Convex Generalized Linear Models (Or: How to Prove Kabashima's Replica Formula). - Cédric Gerbelot; Alia Abbara; Florent Krzakala (IEEE Trans. Inf. Theory, 2023)

High-dimensional optimization for multi-spiked tensor PCA. - Gérard Ben Arous; Cédric Gerbelot; Vanessa Piccolo (CoRR, 2024)

Stochastic gradient descent in high dimensions for multi-spiked tensor PCA. - Gérard Ben Arous; Cédric Gerbelot; Vanessa Piccolo (CoRR, 2024)

Permutation recovery of spikes in noisy high-dimensional tensor estimation. - Gérard Ben Arous; Cédric Gerbelot; Vanessa Piccolo (CoRR, 2024)

Rigorous Dynamical Mean-Field Theory for Stochastic Gradient Descent Methods. - Cédric Gerbelot; Emanuele Troiani; Francesca Mignacco; Florent Krzakala; Lenka Zdeborová (SIAM J. Math. Data Sci., 2024)

Asymptotics of Non-Convex Generalized Linear Models in High-Dimensions: A proof of the replica formula. - Matteo Vilucchio; Yatin Dandi; Cédric Gerbelot; Florent Krzakala (CoRR, 2025)