3 papers
cs.NE2026
Quantifying Uncertainty In Wide Two-Layer Neural Networks: On The Law Of The Limiting Fluctuation Process
Arnaud Descours, Arnaud Guillin, Geoffrey Lacour +3
Uncertainty quantification in neural networks prediction is a main issue for usual applications. Our approach seeks at reducing computation costs by directly evaluating uncertainty…
cs.LG2025
Gradient Projection onto Historical Descent Directions for Communication-Efficient Federated Learning
Arnaud Descours, Léonard Deroose, Jan Ramon
Federated Learning (FL) enables decentralized model training across multiple clients while optionally preserving data privacy. However, communication efficiency remains a critical…
stat.ML2024
Central Limit Theorem for Bayesian Neural Network trained with Variational Inference
Arnaud Descours, Tom Huix, Arnaud Guillin +3
In this paper, we rigorously derive Central Limit Theorems (CLT) for Bayesian two-layerneural networks in the infinite-width limit and trained by variational inference on a regress…