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Bruno Loureiro
Tytuł
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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
1592020
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
1502021
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
1302018
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
972022
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
602021
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
552019
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
512021
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
472020
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
342020
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
302022
Gaussian universality of perceptrons with random labels
F Gerace, F Krzakala, B Loureiro, L Stephan, L Zdeborová
Physical Review E 109 (3), 034305, 2024
26*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
252022
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
172023
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
172023
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
152023
Marginal and irrelevant disorder in Einstein-Maxwell backgrounds
AM Garcia-Garcia, B Loureiro
Physical Review D 93 (6), 065025, 2016
142016
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
13*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
112023
Theoretical characterization of uncertainty in high-dimensional linear classification
L Clarté, B Loureiro, F Krzakala, L Zdeborová
Machine Learning: Science and Technology 4 (2), 025029, 2023
112023
Learning two-layer neural networks, one (giant) step at a time
Y Dandi, F Krzakala, B Loureiro, L Pesce, L Stephan
arXiv preprint arXiv:2305.18270, 2023
112023
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