3 papers
cs.LG2026
When pre-training hurts LoRA fine-tuning: a dynamical analysis via single-index models
Gibbs Nwemadji, Bruno Loureiro, Jean Barbier
Pre-training on a source task is usually expected to facilitate fine-tuning on similar downstream problems. In this work, we mathematically show that this naive intuition is not al…
stat.ML2026
Sharp feature-learning transitions and Bayes-optimal neural scaling laws in extensive-width networks
Minh-Toan Nguyen, Jean Barbier
We study the information-theoretic limits of learning a one-hidden-layer teacher network with hierarchical features from noisy queries, in the context of knowledge transfer to a sm…
cond-mat.dis-nn2026
Generalization performance of narrow one-hidden layer networks in the teacher-student setting
Rodrigo Pérez Ortiz, Gibbs Nwemadji, Jean Barbier +4
Understanding the generalization properties of neural networks on simple input-output distributions is key to explaining their performance on real datasets. The classical teacher-s…