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Statistics > Machine Learning

arXiv:2511.08401 (stat)
[Submitted on 11 Nov 2025]

Title:Source-Optimal Training is Transfer-Suboptimal

Authors:C. Evans Hedges
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Abstract:We prove a fundamental misalignment in transfer learning: the source regularization that minimizes source risk almost never coincides with the regularization maximizing transfer benefit. Through sharp phase boundaries for L2-SP ridge regression, we characterize the transfer-optimal source penalty $\tau_0^*$ and show it diverges predictably from task-optimal values, requiring stronger regularization in high-SNR regimes and weaker regularization in low-SNR regimes. Additionally, in isotropic settings the decision to transfer is remarkably independent of target sample size and noise, depending only on task alignment and source characteristics. CIFAR-10 and MNIST experiments confirm this counterintuitive pattern persists in non-linear networks.
Subjects: Machine Learning (stat.ML); Machine Learning (cs.LG); Statistics Theory (math.ST)
Cite as: arXiv:2511.08401 [stat.ML]
  (or arXiv:2511.08401v1 [stat.ML] for this version)
  https://doi.org/10.48550/arXiv.2511.08401

Submission history

From: Charles Hedges [view email]
[v1] Tue, 11 Nov 2025 16:16:10 UTC (443 KB)
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