| Generalisation error in learning with random features and the hidden manifold model F Gerace, B Loureiro, F Krzakala, M Mézard, L Zdeborová International Conference on Machine Learning, 3452-3462, 2020 | 169 | 2020 |
| Chaotic-integrable transition in the Sachdev-Ye-Kitaev model AM García-García, B Loureiro, A Romero-Bermúdez, M Tezuka Physical review letters 120 (24), 241603, 2018 | 134 | 2018 |
| 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 | 130 | 2021 |
| The gaussian equivalence of generative models for learning with shallow neural networks S Goldt, B Loureiro, G Reeves, F Krzakala, M Mézard, L Zdeborová Mathematical and Scientific Machine Learning, 426-471, 2022 | 103 | 2022 |
| Generalization error rates in kernel regression: The crossover from the noiseless to noisy regime H Cui, B Loureiro, F Krzakala, L Zdeborová Advances in Neural Information Processing Systems 34, 10131-10143, 2021 | 68 | 2021 |
| The spiked matrix model with generative priors B Aubin, B Loureiro, A Maillard, F Krzakala, L Zdeborová Advances in Neural Information Processing Systems 32, 2019 | 58 | 2019 |
| Learning Gaussian Mixtures with Generalised Linear Models: Precise Asymptotics in High-dimensions B Loureiro, G Sicuro, C Gerbelot, A Pacco, F Krzakala, L Zdeborová arXiv preprint arXiv:2106.03791, 2021 | 57 | 2021 |
| Phase retrieval in high dimensions: Statistical and computational phase transitions A Maillard, B Loureiro, F Krzakala, L Zdeborová Advances in Neural Information Processing Systems 33, 11071--11082, 2020 | 49 | 2020 |
| Exact asymptotics for phase retrieval and compressed sensing with random generative priors B Aubin, B Loureiro, A Baker, F Krzakala, L Zdeborová Mathematical and Scientific Machine Learning, 55-73, 2020 | 37 | 2020 |
| 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 |
| Phase diagram of stochastic gradient descent in high-dimensional two-layer neural networks R Veiga, L Stephan, B Loureiro, F Krzakala, L Zdeborová Advances in Neural Information Processing Systems 35, 23244-23255, 2022 | 34 | 2022 |
| Gaussian universality of perceptrons with random labels F Gerace, F Krzakala, B Loureiro, L Stephan, L Zdeborová Physical Review E 109 (3), 034305, 2024 | 32* | 2024 |
| 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, 14283-14314, 2022 | 27 | 2022 |
| How two-layer neural networks learn, one (giant) step at a time Y Dandi, F Krzakala, B Loureiro, L Pesce, L Stephan arXiv preprint arXiv:2305.18270, 2023 | 24 | 2023 |
| From high-dimensional & mean-field dynamics to dimensionless odes: A unifying approach to sgd in two-layers networks L Arnaboldi, L Stephan, F Krzakala, B Loureiro The Thirty Sixth Annual Conference on Learning Theory, 1199-1227, 2023 | 22 | 2023 |
| Deterministic equivalent and error universality of deep random features learning D Schröder, H Cui, D Dmitriev, B Loureiro International Conference on Machine Learning, 30285-30320, 2023 | 21 | 2023 |
| Universality laws for gaussian mixtures in generalized linear models Y Dandi, L Stephan, F Krzakala, B Loureiro, L Zdeborová Advances in Neural Information Processing Systems 36, 2024 | 16 | 2024 |
| On double-descent in uncertainty quantification in overparametrized models L Clarté, B Loureiro, F Krzakala, L Zdeborová International Conference on Artificial Intelligence and Statistics, 7089-7125, 2023 | 16* | 2023 |
| Are Gaussian data all you need? The extents and limits of universality in high-dimensional generalized linear estimation L Pesce, F Krzakala, B Loureiro, L Stephan International Conference on Machine Learning, 27680-27708, 2023 | 15 | 2023 |
| 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 | 15 | 2023 |
Florent KrzakalaÉcole polytechnique fédérale de Lausanne在 epfl.ch 的电子邮件经过验证
