Cédric Gerbelot

According to our database1, Cédric Gerbelot authored at least 13 papers between 2020 and 2023.

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Bibliography

2023
Asymptotic Errors for Teacher-Student Convex Generalized Linear Models (Or: How to Prove Kabashima's Replica Formula).
IEEE Trans. Inf. Theory, March, 2023

Learning curves for the multi-class teacher-student perceptron.
Mach. Learn. Sci. Technol., March, 2023

Applying statistical learning theory to deep learning.
CoRR, 2023

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

Rigorous dynamical mean field theory for stochastic gradient descent methods.
CoRR, 2022

Multi-layer State Evolution Under Random Convolutional Design.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Fluctuations, Bias, Variance & Ensemble of Learners: Exact Asymptotics for Convex Losses in High-Dimension.
Proceedings of the International Conference on Machine Learning, 2022

2021
Graph-based Approximate Message Passing Iterations.
CoRR, 2021

Learning Gaussian Mixtures with Generalised Linear Models: Precise Asymptotics in High-dimensions.
CoRR, 2021

Capturing the learning curves of generic features maps for realistic data sets with a teacher-student model.
CoRR, 2021

Learning Gaussian Mixtures with Generalized Linear Models: Precise Asymptotics in High-dimensions.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Learning curves of generic features maps for realistic datasets with a teacher-student model.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020
Asymptotic Errors for High-Dimensional Convex Penalized Linear Regression beyond Gaussian Matrices.
Proceedings of the Conference on Learning Theory, 2020


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