Data augmented bootstrap constructs confidence intervals via approximately invariant transformations, recovering classical bootstrap, conformal prediction, wild bootstrap, and SymmPI as special cases with interpolated finite-sample to asymptotic coverage guarantees.
arXiv preprint arXiv:2409.05202 (2024)
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SNR-ST-Mix is a geometry- and expression-aware mixup augmentation that constrains interpolation to k-nearest spatial neighbors and weights by transcriptomic similarity for spatial transcriptomics imputation.
Mixup applied only to the student during KD induces independent linearity acquisition that reduces overconfidence by an order of magnitude while improving accuracy, with calibration transferring separately from accuracy.
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Data augmented bootstrap: Unifying confidence interval construction by approximate invariance
Data augmented bootstrap constructs confidence intervals via approximately invariant transformations, recovering classical bootstrap, conformal prediction, wild bootstrap, and SymmPI as special cases with interpolated finite-sample to asymptotic coverage guarantees.