Bruno Loureiro
24 papers · 2019–2025 · 6 venues · across top CS/AI venues
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Machine Learning
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Optimization & Theory · Machine Learning
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Deep Learning
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Mathematics & Optimization
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Keywords
generalization error
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high-dimensional statistics
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double descent
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high-dimensional analysis
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neural network
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feature learning
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learning theory
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random feature
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generalized linear model
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statistical learning
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approximate message passing
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ridge regression
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high-dimensional limit
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stochastic gradient descent
(2)
uncertainty quantification
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test error
(2)
random matrix theory
(2)
gradient descent
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phase transition
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high-dimensional regression
(2)
Papers
A High Dimensional Statistical Model for Adversarial Training: Geometry and Trade-Offs
AISTATS 2025
A Random Matrix Theory Perspective on the Spectrum of Learned Features and Asymptotic Generalization Capabilities
AISTATS 2025
Fundamental computational limits of weak learnability in high-dimensional multi-index models
AISTATS 2025
Analysis of Bootstrap and Subsampling in High-dimensional Regularized Regression
UAI 2024
Asymptotics of feature learning in two-layer networks after one gradient-step
ICML 2024
Asymptotics of Learning with Deep Structured (Random) Features
ICML 2024
Dimension-free deterministic equivalents and scaling laws for random feature regression
NeurIPS 2024
How Two-Layer Neural Networks Learn, One (Giant) Step at a Time
JMLR 2024
Online Learning and Information Exponents: The Importance of Batch size & Time/Complexity Tradeoffs
ICML 2024
Are Gaussian Data All You Need? The Extents and Limits of Universality in High-Dimensional Generalized Linear Estimation
ICML 2023
Deterministic equivalent and error universality of deep random features learning
ICML 2023
Expectation consistency for calibration of neural networks
UAI 2023
From high-dimensional & mean-field dynamics to dimensionless ODEs: A unifying approach to SGD in two-layers networks
COLT 2023
On double-descent in uncertainty quantification in overparametrized models
AISTATS 2023
Universality laws for Gaussian mixtures in generalized linear models
NeurIPS 2023
Fluctuations, Bias, Variance & Ensemble of Learners: Exact Asymptotics for Convex Losses in High-Dimension
ICML 2022
Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks
NeurIPS 2022
Subspace clustering in high-dimensions: Phase transitions & Statistical-to-Computational gap
NeurIPS 2022
Generalization Error Rates in Kernel Regression: The Crossover from the Noiseless to Noisy Regime
NeurIPS 2021
Learning curves of generic features maps for realistic datasets with a teacher-student model
NeurIPS 2021
Learning Gaussian Mixtures with Generalized Linear Models: Precise Asymptotics in High-dimensions
NeurIPS 2021
Generalisation error in learning with random features and the hidden manifold model
ICML 2020
Phase retrieval in high dimensions: Statistical and computational phase transitions
NeurIPS 2020
The spiked matrix model with generative priors
NeurIPS 2019