45 citations · 139 across the 9 of their papers we have counts for
9 papers
Mean-field methods and algorithmic perspectives for high-dimensional machine learning
Benjamin Aubin
The main difficulty that arises in the analysis of most machine learning algorithms is to handle, analytically and numerically, a large number of interacting random variables. In t…
Linear unit-tests for invariance discovery
Benjamin Aubin, Agnieszka Słowik, Martin Arjovsky +2
There is an increasing interest in algorithms to learn invariant correlations across training environments. A big share of the current proposals find theoretical support in the cau…
Generalization error in high-dimensional perceptrons: Approaching Bayes error with convex optimization
Benjamin Aubin, Florent Krzakala, Yue M. Lu +1
We consider a commonly studied supervised classification of a synthetic dataset whose labels are generated by feeding a one-layer neural network with random iid inputs. We study th…
Tree-AMP: Compositional Inference with Tree Approximate Message Passing
Antoine Baker, Benjamin Aubin, Florent Krzakala +1
We introduce Tree-AMP, standing for Tree Approximate Message Passing, a python package for compositional inference in high-dimensional tree-structured models. The package provides…
Rademacher complexity and spin glasses: A link between the replica and statistical theories of learning
Alia Abbara, Benjamin Aubin, Florent Krzakala +1
Statistical learning theory provides bounds of the generalization gap, using in particular the Vapnik-Chervonenkis dimension and the Rademacher complexity. An alternative approach,…
Exact asymptotics for phase retrieval and compressed sensing with random generative priors
Benjamin Aubin, Bruno Loureiro, Antoine Baker +2
We consider the problem of compressed sensing and of (real-valued) phase retrieval with random measurement matrix. We derive sharp asymptotics for the information-theoretically opt…