Author

Nicolas Flammarion

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#machine learning Preprint Sep 2026

Statistical Benefits of Fine-Tuning from Pretrained Initialization in Diagonal Linear Networks

Adapting pretrained models to downstream tasks with limited data has become a central paradigm in modern deep learning. Yet, despite its widespread practical success, how fine-tuning leverages information from pretraining remains poorly understood theoretically. We study fine-tuning from pretrained weights through the...

A.-E. Decleves, Etienne Boursier, Nicolas Flammarion · 0 citations

#machine learning Preprint Sep 2026

Two-Timescale Fine-tuning Provably Learns New Features for Two-Layer ReLU Networks

Fine-tuning pre-trained models on specialized tasks with scarce data is central to modern deep learning. Despite its empirical success, theoretical understanding of fine-tuning remains limited. We introduce a Gaussian multi-index setting to study fine-tuning from pre-trained weights, where the teacher network has $m+1$...

Etienne Boursier, Nicolas Flammarion · 0 citations