Multi-source transfer learning incurs an intrinsic adaptation cost that can exceed one, with phase transitions separating regimes where bias-agnostic estimators match oracle performance from those where they cannot.
When does preconditioning help or hurt generalization?arXiv preprint arXiv:2006.10732
4 Pith papers cite this work. Polarity classification is still indexing.
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In ridgeless regression with low intrinsic dimension, discrepancy between weak and strong models reduces W2S generalization variance by dim(V_s)/N in the discrepant subspace while inheriting it in the overlap.
RAT estimates Tikhonov-regularized natural policy gradients by rewriting them with the Woodbury identity, approximating the transformed advantage via randomized block Kaczmarz, and applying it as a vanilla policy gradient surrogate.
Preconditioned gradient descent mitigates spectral bias and reduces grokking delays by enabling uniform parameter space exploration in the NTK regime, confirming grokking as a transition to the rich regime.
citing papers explorer
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The Statistical Cost of Adaptation in Multi-Source Transfer Learning
Multi-source transfer learning incurs an intrinsic adaptation cost that can exceed one, with phase transitions separating regimes where bias-agnostic estimators match oracle performance from those where they cannot.
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Discrepancies are Virtue: Weak-to-Strong Generalization through Lens of Intrinsic Dimension
In ridgeless regression with low intrinsic dimension, discrepancy between weak and strong models reduces W2S generalization variance by dim(V_s)/N in the discrepant subspace while inheriting it in the overlap.
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Randomized Advantage Transformation (RAT): Computing Natural Policy Gradients via Direct Backpropagation
RAT estimates Tikhonov-regularized natural policy gradients by rewriting them with the Woodbury identity, approximating the transformed advantage via randomized block Kaczmarz, and applying it as a vanilla policy gradient surrogate.
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On the Convergence Behavior of Preconditioned Gradient Descent Toward the Rich Learning Regime
Preconditioned gradient descent mitigates spectral bias and reduces grokking delays by enabling uniform parameter space exploration in the NTK regime, confirming grokking as a transition to the rich regime.