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stat.ME2026
Improving reproducibility by controlling random seed stability in machine learning based estimation via bagging
Nicholas Williams, Alejandro Schuler
Predictions from machine learning algorithms can vary across random seeds, inducing instability in downstream debiased machine learning estimators. We formalize random seed stabili…
stat.ME2025
Score-Preserving Targeted Maximum Likelihood Estimation
Noel Pimentel, Alejandro Schuler, Mark van der Laan
Targeted maximum likelihood estimators (TMLEs) are asymptotically optimal among regular, asymptotically linear estimators. In small samples, however, we may be far from "asymptopia…