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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SIC is a prior-fitted network that amortizes Bayesian survival inference by pretraining on synthetic data generated from a controllable survival prior, delivering competitive or better performance than classical and deep models on real datasets especially in small-sample regimes.
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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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Survival In-Context: Amortized Bayesian Survival Analysis via Prior-Fitted Networks
SIC is a prior-fitted network that amortizes Bayesian survival inference by pretraining on synthetic data generated from a controllable survival prior, delivering competitive or better performance than classical and deep models on real datasets especially in small-sample regimes.