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The paper "Minimax-optimal Scaling Laws in Ridge Regression via Transfer Learning" theoretically proves that transfer learning can achieve the minimax-optimal scaling exponent.
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10/19/2026
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UnverifiedThe rate transfer proposed in the paper does not require an extremely small step size, but instead quantifies the deviation between the SDE and SGD at a finite step size, thereby obtaining non-asymptotic convergence bounds.69% similarUnverifiedIncremental migration is preferable to a one-time big-bang migration; breaking large-scale migrations into small, verifiable, and rollback-able steps reduces risk.67% similarUnverifiedGradient descent naturally tends to select flatter, simpler solutions from among the many solutions that perfectly fit the training set, and this implicit regularization effect is closely related to generalization ability.64% similarUnverifiedIn transfer learning practice, the smaller the amount of data for the new task, the more layers should be frozen to prevent overfitting; the learning rate should be much smaller than when training from scratch; if the new data differs greatly in distribution from the pretraining data, more layers often need to be fine-tuned.63% similar
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