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Found 9 papers, 4 papers with code
Mean-field methods and algorithmic perspectives for high-dimensional machine learning
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.
Linear unit-tests for invariance discovery
There is an increasing interest in algorithms to learn invariant correlations across training environments.
Generalization error in high-dimensional perceptrons: Approaching Bayes error with convex optimization
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.
Tree-AMP: Compositional Inference with Tree Approximate Message Passing
We introduce Tree-AMP, standing for Tree Approximate Message Passing, a python package for compositional inference in high-dimensional tree-structured models.
Rademacher complexity and spin glasses: A link between the replica and statistical theories of learning
Statistical learning theory provides bounds of the generalization gap, using in particular the Vapnik-Chervonenkis dimension and the Rademacher complexity.
Exact asymptotics for phase retrieval and compressed sensing with random generative priors
We consider the problem of compressed sensing and of (real-valued) phase retrieval with random measurement matrix.
Precise asymptotics for phase retrieval and compressed sensing with random generative priors
We consider the problem of compressed sensing and of (real-valued) phase retrieval with random measurement matrix.
The spiked matrix model with generative priors
Here, we replace the sparsity assumption by generative modelling, and investigate the consequences on statistical and algorithmic properties.
The committee machine: Computational to statistical gaps in learning a two-layers neural network
Heuristic tools from statistical physics have been used in the past to locate the phase transitions and compute the optimal learning and generalization errors in the teacher-student scenario in multi-layer neural networks.
