| 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 | 209 | 2021 |
| 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 | 107 | 2021 |
| Bayes-optimal learning of deep random networks of extensive-width H Cui, F Krzakala, L Zdeborová International Conference on Machine Learning 40, 6468-6521, 2023 | 49 | 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 | 35 | 2023 |
| Asymptotics of feature learning in two-layer networks after one gradient-step H Cui, L Pesce, Y Dandi, F Krzakala, YM Lu, L Zdeborová, B Loureiro arXiv preprint arXiv:2402.04980, 2024 | 27 | 2024 |
| Analysis of learning a flow-based generative model from limited sample complexity H Cui, F Krzakala, E Vanden-Eijnden, L Zdeborová arXiv preprint arXiv:2310.03575, 2023 | 25 | 2023 |
| Error scaling laws for kernel classification under source and capacity conditions H Cui, B Loureiro, F Krzakala, L Zdeborová Machine Learning: Science and Technology 4 (3), 035033, 2023 | 25* | 2023 |
| High-dimensional asymptotics of denoising autoencoders H Cui, L Zdeborová Advances in Neural Information Processing Systems 36, 11850-11890, 2023 | 19 | 2023 |
| Large deviations for the perceptron model and consequences for active learning H Cui, L Saglietti, L Zdeborova Mathematical and Scientific Machine Learning, 390-430, 2020 | 13 | 2020 |
| A phase transition between positional and semantic learning in a solvable model of dot-product attention H Cui, F Behrens, F Krzakala, L Zdeborová Advances in Neural Information Processing Systems 37, 36342-36389, 2024 | 12 | 2024 |
| Asymptotics of learning with deep structured (random) features D Schröder, D Dmitriev, H Cui, B Loureiro arXiv preprint arXiv:2402.13999, 2024 | 11 | 2024 |
| High-dimensional learning of narrow neural networks H Cui Journal of Statistical Mechanics: Theory and Experiment 2025 (2), 023402, 2025 | 4 | 2025 |
| A random matrix theory perspective on the spectrum of learned features and asymptotic generalization capabilities Y Dandi, L Pesce, H Cui, F Krzakala, YM Lu, B Loureiro arXiv preprint arXiv:2410.18938, 2024 | 3 | 2024 |
| Topics in statistical physics of high-dimensional machine learning HC Cui EPFL, 2024 | 2 | 2024 |
| A precise asymptotic analysis of learning diffusion models: theory and insights H Cui, C Pehlevan, YM Lu arXiv preprint arXiv:2501.03937, 2025 | 1 | 2025 |
| Large deviations of semisupervised learning in the stochastic block model H Cui, L Saglietti, L Zdeborová Physical Review E 105 (3), 034108, 2022 | 1 | 2022 |
| Fundamental limits of learning in sequence multi-index models and deep attention networks: High-dimensional asymptotics and sharp thresholds E Troiani, H Cui, Y Dandi, F Krzakala, L Zdeborová arXiv preprint arXiv:2502.00901, 2025 | | 2025 |
Lenka ZdeborováEPFL, SwitzerlandVerified email at epfl.ch
