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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
1292020
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
1262018
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
111*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
922022
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
512019
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
452020
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
412021
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
362021
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
302020
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
252022
Gaussian Universality of Perceptrons with Random Labels
F Gerace, F Krzakala, B Loureiro, L Stephan, L Zdeborová
20*2023
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
162022
Marginal and irrelevant disorder in Einstein-Maxwell backgrounds
AM Garcia-Garcia, B Loureiro
Physical Review D 93 (6), 065025, 2016
132016
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
arXiv preprint arXiv:2302.05882, 2023
102023
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
92023
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
8*2023
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
8*2023
Transport in a gravity dual with a varying gravitational coupling constant
AM García-García, B Loureiro, A Romero-Bermúdez
Physical Review D 94 (8), 086007, 2016
82016
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
72023
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
72023
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