Subbagging guarantees random seed stability in bounded regression, and adaptive cross-bagging eliminates seed effects from nuisance models and data splits in debiased machine learning estimators.
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Improving reproducibility by controlling random seed stability in machine learning based estimation via bagging
Subbagging guarantees random seed stability in bounded regression, and adaptive cross-bagging eliminates seed effects from nuisance models and data splits in debiased machine learning estimators.