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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Covariate-augmented spectral clustering for heterogeneous networks with misclustering bounds under a contextualized stochastic blockmodel, applied to UNGA voting data.
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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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Understanding Geopolitical Alignments Through Covariate Augmented Spectral Clustering of Heterogeneous UNGA Voting Data
Covariate-augmented spectral clustering for heterogeneous networks with misclustering bounds under a contextualized stochastic blockmodel, applied to UNGA voting data.