| Learning curves of generic features maps for realistic datasets with a teacher-student model B Loureiro, C Gerbelot, H Cui, S Goldt, F Krzakala, M Mezard, ... Advances in Neural Information Processing Systems 34, 18137-18151, 2021 | 131 | 2021 |
| Learning Gaussian Mixtures with Generalised Linear Models: Precise Asymptotics in High-dimensions B Loureiro, G Sicuro, C Gerbelot, A Pacco, F Krzakala, L Zdeborová Advances in Neural Information Processing Systems 34, 2021 | 57 | 2021 |
| Asymptotic errors for teacher-student convex generalized linear models (or: How to prove Kabashima’s replica formula) C Gerbelot, A Abbara, F Krzakala IEEE Transactions on Information Theory, 2022 | 52 | 2022 |
| Asymptotic errors for high-dimensional convex penalized linear regression beyond Gaussian matrices C Gerbelot, A Abbara, F Krzakala Conference on Learning Theory, 1682-1713, 2020 | 49* | 2020 |
| Graph-based approximate message passing iterations C Gerbelot, R Berthier Information and Inference: A Journal of the IMA 12 (4), 2562-2628, 2023 | 42 | 2023 |
| Capturing the learning curves of generic features maps for realistic data sets with a teacher-student model B Loureiro, C Gerbelot, H Cui, S Goldt, F Krzakala, M Mézard, ... stat 1050, 16, 2021 | 35 | 2021 |
| Fluctuations, Bias, Variance & Ensemble of Learners: Exact Asymptotics for Convex Losses in High-Dimension B Loureiro, C Gerbelot, M Refinetti, G Sicuro, F Krzakala International Conference on Machine Learning 2022, 2022 | 27 | 2022 |
| Rigorous dynamical mean-field theory for stochastic gradient descent methods C Gerbelot, E Troiani, F Mignacco, F Krzakala, L Zdeborova SIAM Journal on Mathematics of Data Science 6 (2), 400-427, 2024 | 21 | 2024 |
| Capillary leveling of freestanding liquid nanofilms M Ilton, MMP Couchman, C Gerbelot, M Benzaquen, PD Fowler, ... Physical review letters 117 (16), 167801, 2016 | 17 | 2016 |
| Learning curves for the multi-class teacher–student perceptron E Cornacchia, F Mignacco, R Veiga, C Gerbelot, B Loureiro, L Zdeborová Machine Learning: Science and Technology 4 (1), 015019, 2023 | 16 | 2023 |
| Ballistic Brownian motion of nanoconfined DNA I Madrid, Z Zheng, C Gerbelot, A Fujiwara, S Li, S Grall, K Nishiguchi, ... ACS nano 17 (17), 17031-17040, 2023 | 2 | 2023 |
| Fluctuations, bias, variance and ensemble of learners: exact asymptotics for convex losses in high-dimension B Loureiro, C Gerbelot, M Refinetti, G Sicuro, F Krzakala Journal of Statistical Mechanics: Theory and Experiment 2023 (11), 114001, 2023 | 1 | 2023 |
| Multi-layer State Evolution Under Random Convolutional Design M Daniels, C Gerbelot, F Krzakala, L Zdeborová Advances in Neural Information Processing Systems 35, 2022 | 1 | 2022 |
| Applying statistical learning theory to deep learning C Gerbelot, A Karagulyan, S Karp, K Ravichandran, M Stern, N Srebro arXiv preprint arXiv:2311.15404, 2023 | | 2023 |
| Statistical learning in high dimensions: a rigorous statistical physics approach C Gerbelot Université Paris sciences et lettres, 2022 | | 2022 |
| Supplementary information for Learning Gaussian Mixtures with Generalised Linear Models: Precise Asymptotics in High-dimensions B Loureiro, G Sicuro, C Gerbelot, A Pacco, F Krzakala, L Zdeborová | | |
Florent KrzakalaÉcole polytechnique fédérale de Lausanne在 epfl.ch 的电子邮件经过验证
