Bruno Loureiro
24 papers · 2019–2025 · 6 conferences · across top CS/AI conferences
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Conferences
NIPS (9)
ICML (7)
AISTATS (4)
UAI (2)
COLT (1)
JMLR (1)
Top co-authors
Research topics
Keywords
generalization error
(7)
neural network
(4)
high-dimensional statistics
(4)
high-dimensional analysis
(3)
double descent
(3)
generalized linear model
(3)
statistical learning
(3)
learning theory
(2)
ridge regression
(2)
stochastic gradient descent
(2)
uncertainty quantification
(2)
random feature
(2)
gaussian mixture
(2)
phase transition
(2)
convex loss
(2)
gaussian universality
(2)
approximate message passing
(2)
sparse principal component analysis
(1)
empirical risk minimization
(1)
feature learning
(1)
Papers
A Random Matrix Theory Perspective on the Spectrum of Learned Features and Asymptotic Generalization Capabilities
AISTATS 2025
A High Dimensional Statistical Model for Adversarial Training: Geometry and Trade-Offs
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
Dimension-free deterministic equivalents and scaling laws for random feature regression
NIPS 2024
Online Learning and Information Exponents: The Importance of Batch size & Time/Complexity Tradeoffs
ICML 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
How Two-Layer Neural Networks Learn, One (Giant) Step at a Time
JMLR 2024
On double-descent in uncertainty quantification in overparametrized models
AISTATS 2023
From high-dimensional & mean-field dynamics to dimensionless ODEs: A unifying approach to SGD in two-layers networks
COLT 2023
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
Universality laws for Gaussian mixtures in generalized linear models
NIPS 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
NIPS 2022
Subspace clustering in high-dimensions: Phase transitions & Statistical-to-Computational gap
NIPS 2022
Learning Gaussian Mixtures with Generalized Linear Models: Precise Asymptotics in High-dimensions
NIPS 2021
Generalization Error Rates in Kernel Regression: The Crossover from the Noiseless to Noisy Regime
NIPS 2021
Learning curves of generic features maps for realistic datasets with a teacher-student model
NIPS 2021
Phase retrieval in high dimensions: Statistical and computational phase transitions
NIPS 2020
Generalisation error in learning with random features and the hidden manifold model
ICML 2020
The spiked matrix model with generative priors
NIPS 2019