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

arXiv:2609.34756 · stat.ML, cs.LG · Submitted 2026-09-28 · Read on arXiv

stat.ML, cs.LG

Submitted: 2026-09-28

Updated: 2026-09-28

Terminology

• Fine-Tuning — 45× in this paper · explained in 5 episodes
• Calibration — 10× in this paper · explained in 7 episodes
• Benchmark — 9× in this paper · explained in 2 episodes
• Regularization — 7× in this paper · explained in 3 episodes
• Gradient Flow — 6× in this paper · explained in 2 episodes
• Sample Complexity — 6× in this paper · explained in 15 episodes
• Sparsity — 5× in this paper · explained in 4 episodes
• Transfer Learning — 4× in this paper · explained in 10 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Covariate Shift — 2× in this paper · explained in 2 episodes
• Generative AI — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Distribution Shift — 1× in this paper · explained in 6 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Homogeneity — 1× in this paper · explained in 2 episodes
• Linear Probing — 1× in this paper · explained in 2 episodes

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