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ORCIDCédric Gerbelot
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2020 – today
- 2024
[j3]Cédric Gerbelot, Emanuele Troiani, Francesca Mignacco
, Florent Krzakala, Lenka Zdeborová:
Rigorous Dynamical Mean-Field Theory for Stochastic Gradient Descent Methods. SIAM J. Math. Data Sci. 6(2): 400-427 (2024)- [i11]Gérard Ben Arous, Cédric Gerbelot, Vanessa Piccolo:
High-dimensional optimization for multi-spiked tensor PCA. CoRR abs/2408.06401 (2024) - [i10]Gérard Ben Arous, Cédric Gerbelot, Vanessa Piccolo:
Stochastic gradient descent in high dimensions for multi-spiked tensor PCA. CoRR abs/2410.18162 (2024) - 2023
- [j2]Elisabetta Cornacchia, Francesca Mignacco, Rodrigo Veiga, Cédric Gerbelot, Bruno Loureiro, Lenka Zdeborová:
Learning curves for the multi-class teacher-student perceptron. Mach. Learn. Sci. Technol. 4(1): 15019 (2023) - [j1]Cédric Gerbelot, Alia Abbara, Florent Krzakala:
Asymptotic Errors for Teacher-Student Convex Generalized Linear Models (Or: How to Prove Kabashima's Replica Formula). IEEE Trans. Inf. Theory 69(3): 1824-1852 (2023) - [i9]Cédric Gerbelot, Avetik Karagulyan, Stefani Karp, Kavya Ravichandran, Menachem Stern, Nathan Srebro:
Applying statistical learning theory to deep learning. CoRR abs/2311.15404 (2023) - 2022
- [b1]Cédric Gerbelot:
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 - [c5]Bruno Loureiro, Cédric Gerbelot, Maria Refinetti, Gabriele Sicuro, Florent Krzakala:
Fluctuations, Bias, Variance & Ensemble of Learners: Exact Asymptotics for Convex Losses in High-Dimension. ICML 2022: 14283-14314 - [c4]Max Daniels, Cédric Gerbelot, Florent Krzakala, Lenka Zdeborová:
Multi-layer State Evolution Under Random Convolutional Design. NeurIPS 2022 - [i8]Bruno Loureiro, Cédric Gerbelot, Maria Refinetti, Gabriele Sicuro, Florent Krzakala:
Fluctuations, Bias, Variance & Ensemble of Learners: Exact Asymptotics for Convex Losses in High-Dimension. CoRR abs/2201.13383 (2022) - [i7]Elisabetta Cornacchia, Francesca Mignacco, Rodrigo Veiga, Cédric Gerbelot, Bruno Loureiro, Lenka Zdeborová:
Learning curves for the multi-class teacher-student perceptron. CoRR abs/2203.12094 (2022) - [i6]Max Daniels, Cédric Gerbelot, Florent Krzakala, Lenka Zdeborová:
Multi-layer State Evolution Under Random Convolutional Design. CoRR abs/2205.13503 (2022) - [i5]Cédric Gerbelot, Emanuele Troiani, Francesca Mignacco, Florent Krzakala, Lenka Zdeborová:
Rigorous dynamical mean field theory for stochastic gradient descent methods. CoRR abs/2210.06591 (2022) - 2021
- [c3]Bruno Loureiro, Gabriele Sicuro, Cédric Gerbelot, Alessandro Pacco, Florent Krzakala, Lenka Zdeborová:
Learning Gaussian Mixtures with Generalized Linear Models: Precise Asymptotics in High-dimensions. NeurIPS 2021: 10144-10157 - [c2]Bruno Loureiro, Cédric Gerbelot, Hugo Cui, Sebastian Goldt, Florent Krzakala, Marc Mézard, Lenka Zdeborová:
Learning curves of generic features maps for realistic datasets with a teacher-student model. NeurIPS 2021: 18137-18151 - [i4]Bruno Loureiro, Cédric Gerbelot, Hugo Cui, Sebastian Goldt, Florent Krzakala, Marc Mézard, Lenka Zdeborová:
Capturing the learning curves of generic features maps for realistic data sets with a teacher-student model. CoRR abs/2102.08127 (2021) - [i3]Bruno Loureiro, Gabriele Sicuro, Cédric Gerbelot, Alessandro Pacco, Florent Krzakala, Lenka Zdeborová:
Learning Gaussian Mixtures with Generalised Linear Models: Precise Asymptotics in High-dimensions. CoRR abs/2106.03791 (2021) - [i2]Cédric Gerbelot, Raphaël Berthier:
Graph-based Approximate Message Passing Iterations. CoRR abs/2109.11905 (2021) - 2020
- [c1]Cédric Gerbelot, Alia Abbara, Florent Krzakala:
Asymptotic Errors for High-Dimensional Convex Penalized Linear Regression beyond Gaussian Matrices. COLT 2020: 1682-1713 - [i1]Cédric Gerbelot, Alia Abbara, Florent Krzakala:
Asymptotic Errors for Teacher-Student Convex Generalized Linear Models (or : How to Prove Kabashima's Replica Formula). CoRR abs/2006.06581 (2020)
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