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.
Fast learning rates for plug-in classifiers
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
New conditions for support vector proliferation (SVP) in RKHS for bounded orthonormal systems and sub-Gaussian features, yielding generalization bounds for kernel SVMs beyond prior restrictive assumptions.
Formalizes counterfactual individual harm in RL and introduces a two-stage policy learning method with finite-sample guarantees on sub-optimality gap and harm rate control.
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.