Bruno Loureiro

I am a CNRS researcher based at the Centre for Data Science at the École Normale Supérieure in Paris working on the crossroads between machine learning and statistical mechanics.

If you are interested in my research, you can find a complete list of my publications here.

news

Feb 21, 2024	Gaussian ensembles of deep random neural networks have recently made a come back as it was shown they can, in some situations, capture the generalisation behaviour of trained networks. In our recent pre-print, we provide an exact asymptotic analysis of the training of the last layer of such ensembles.
Feb 21, 2024	Resampling techniques such as bagging, the jackknife and the bootstrap are important tools to compute uncertainty of estimators in classical statistics. In our recent pre-print we answer the question: are they reliable in a high-dimensional regime?
Feb 08, 2024	Neural networks are notably susceptible to adversarial attacks. Understanding which features in the training data are more susceptible and how to protect them is therefore an important endeavour. In our recent pre-print we introduce a synthetic model of structured data which captures this phenomenology, and provide an exact asymptotic solution of adversarial training in this model. In particular, we identify a generalisation vs. robustness trade-off, and propose some strategies to defend non-robust features.
Feb 07, 2024	Understanding how neural-networks learn features during training and how these impact their capacity to generalise is an important open question. In our recent pre-print, we provide a sharp analysis of how two-layer neural networks learn features from data, and improve over the kernel regime, after being trained with a single gradient descent step.
Sep 28, 2023	Most of the asymptotic analysis in the proportional regime rely on Gaussianity on the covariates. In our new work High-dimensional robust regression under heavy-tailed data: Asymptotics and Universality with Urte Adomaityte, Leonardo Defilippis and Gabriele Sicuro we provide an asymptotic analysis for generalised linear models trained on heavy-tailed covariates. In particular, we investigate the impact of heavy-tailed contamination to robust M-estimators! Check it out!