| 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 | 46 | 2023 |
| Gaussian universality of perceptrons with random labels F Gerace, F Krzakala, B Loureiro, L Stephan, L Zdeborová Physical Review E 109 (3), 034305, 2024 | 44* | 2024 |
| 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 | 43 | 2022 |
| Robustness of spectral methods for community detection L Stephan, L Massoulié Conference on Learning Theory, 2831-2860, 2019 | 37 | 2019 |
| 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 | 30 | 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 | 28 | 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 | 26 | 2024 |
| Sparse random hypergraphs: Non-backtracking spectra and community detection L Stephan, Y Zhu Information and Inference: A Journal of the IMA 13 (1), iaae004, 2024 | 24 | 2024 |
| Non-backtracking spectra of weighted inhomogeneous random graphs L Stephan, L Massoulié Mathematical Statistics and Learning 5 (3), 201-271, 2022 | 24 | 2022 |
| Planting trees in graphs, and finding them back L Massoulié, L Stephan, D Towsley Conference on Learning Theory, 2341-2371, 2019 | 13 | 2019 |
| Repetita iuvant: Data repetition allows sgd to learn high-dimensional multi-index functions L Arnaboldi, Y Dandi, F Krzakala, L Pesce, L Stephan arXiv preprint arXiv:2405.15459, 2024 | 10 | 2024 |
| Escaping mediocrity: how two-layer networks learn hard single-index models with SGD L Arnaboldi, F Krzakala, B Loureiro, L Stephan CoRR, 2023 | 10* | 2023 |
| A non-backtracking method for long matrix and tensor completion L Stephan, Y Zhu The Thirty Seventh Annual Conference on Learning Theory, 4636-4690, 2024 | 6 | 2024 |
| A simpler spectral approach for clustering in directed networks S Coste, L Stephan arXiv preprint arXiv:2102.03188, 2021 | 6 | 2021 |
| Online Learning and Information Exponents: On The Importance of Batch size, and Time/Complexity Tradeoffs L Arnaboldi, Y Dandi, F Krzakala, B Loureiro, L Pesce, L Stephan arXiv preprint arXiv:2406.02157, 2024 | 4 | 2024 |
| Community detection with the Bethe-Hessian L Stephan, Y Zhu arXiv preprint arXiv:2411.02835, 2024 | | 2024 |
| Computational complexity of deep learning: fundamental limitations and empirical phenomena B Barak, A Carrell, A Favero, W Li, L Stephan, A Zlokapa Journal of Statistical Mechanics: Theory and Experiment 2024 (10), 104008, 2024 | | 2024 |
| Inference problems in large random graphs L Stephan Sorbonne Université, 2021 | | 2021 |
| Inférence dans des grands graphes aléatoires L Stephan Sorbonne université, 2021 | | 2021 |
| Inference in large random graphs L Massoulié, L Stéphan | | 2021 |
Florent KrzakalaÉcole polytechnique fédérale de LausanneVerified email at epfl.ch