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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…
math.ST2026
Highly Adaptive Empirical Risk Minimization with Principal Components
Carlos García Meixide, Mingxun Wang, Alejandro Schuler +1
The Highly Adaptive Lasso (HAL) delivers unprecedented guarantees in nonparametric minimum loss estimation under minimal smoothness assumptions, such as dimension-free minimax opti…
stat.ML2026
Highly Adaptive Principal Component Regression
Mingxun Wang, Alejandro Schuler, Mark van der Laan +1
The Highly Adaptive Lasso (HAL) is a nonparametric regression method that achieves almost dimension-free convergence rates under minimal smoothness assumptions, but its implementat…